[User] Please transcribe this text

[Assistant] The new mortgage is instead of paying 8 every year for 30 years, you pay 8.8 every year for 30 years because if you discount payments of 8.8 forever for 30 years at 8%, you get 100. So the present value


[User] Please transcribe this text

[Assistant] Is a hundred at the agreed upon discounting rate or mortgage rate eight percent.


[User] Please transcribe this text

[Assistant] 0.88 is used to reduce the balance from 100 to 99.117. As you see the balance is going down over time.


[User] Please transcribe this text

[Assistant] And making the lender safer and safer because the same house is backing it. So it's called an amortizing mortgage.


[User] Please transcribe this text

[Assistant] Now, why is it difficult to value? Because you have the option anytime you want, and there's a good reason for that option, anytime you want, you have the option.


[User] Please transcribe this text

[Assistant] of getting out of the mortgage and just saying, okay, I've paid three payments of 8.88. I don't want to do it anymore. I want to pay off 97.13 and then let's call it quits.


[User] Please transcribe this text

[Assistant] And they say okay, and there's nothing they can do about it. Now when are you going to exercise that option? You're going to exercise that option either because you have to move, that's the intention of it,


[User] Please transcribe this text

[Assistant] Or you'll exercise it when it's most advantageous to you. Now why could it become advantageous?


[User] Please transcribe this text

[Assistant] To exercise it. Well, you don't really want to exercise the option. This is the way most people think of it backwards. They think, oh the interest rates are going down, that means I'll get a new mortgage with a lower interest rate. They're hoping for exactly the wrong thing. If the interest rates go up, what they've got is a much better


[User] Please transcribe this text

[Assistant] uh... mortgage because they're continuing to buy at the same eight percent interest and maybe


[User] Please transcribe this text

[Assistant] Uh, interest rates in the economy have become 12% and they're actually making money.


[User] Please transcribe this text

[Assistant] And then there was a deflation, and so they were getting crushed. And the very people who drive the economy were being hurt the most. And so he, that feedback, he said was responsible for the


[User] Please transcribe this text

[Assistant] So you see interest rates can go up or down.


[User] Please transcribe this text

[Assistant] And what happens when they go up, they go up high enough to 19%, you think, my gosh, I've made a fortune holding this mortgage. I'm still borrowing at 7%, 8%, and I'm, I'm, uh,


[User] Please transcribe this text

[Assistant] You don't have to face that big loss, because you just pre-pay at whatever the remaining balance is there, and then you're, you protected your downside.


[User] Please transcribe this text

[Assistant] So by paying attention and deciding when the optimal time to prepay is, you can save yourself a lot of money and thereby cost the bank a lot of money.


[User] Please transcribe this text

[Assistant] So when exactly should you prepay? When should you exercise your option? Well, in this example, if you never exercised it, you'd be handing the bank, effectively, $120, even though they lent you 20. So the bank would have made, uh, 20% profit on you.


[User] Please transcribe this text

[Assistant] But if you exercise your option optimally, you're going to make not a hundred, uh, you're not going to make


[User] Please transcribe this text

[Assistant] The bank is not gonna get $100 out of you. They're gonna even get less than $100. They're gonna get $98 out of you. Okay.


[User] Please transcribe this text

[Assistant] So, when exactly should you be exercising your option? Well, I, we went over this last time, I'll do it once again.


[User] Please transcribe this text

[Assistant] The remaining balance started of course at 100 and then it went down to 99.11 and then it kept going down from there.


[User] Please transcribe this text

[Assistant] You could always say to yourself, do I want to continue or do I want to pay my options? Now you notice that if I had divided this by b1, say, if you had a mortgage that was a little bit smaller, barely over a dollar, for example, uhm,


[User] Please transcribe this text

[Assistant] The payments would, the payments would all, that would divide everything by B1. The payments would all be divided by B1.


[User] Please transcribe this text

[Assistant] So I could always scale this thing up or down. There's nothing fancy about a hundred.


[User] Please transcribe this text

[Assistant] So suppose at any stage you had a dollars left in your mortgage. What would be your remaining balance was one? What would you, so let's say at any node, any node, let's ask the question, what is the value


[User] Please transcribe this text

[Assistant] Okay, so if you started 100 and you haven't prepaid, here you've got B2 dollars. Of course, whatever the value that is.


[User] Please transcribe this text

[Assistant] You take, divide it by b2, that's the value of one dollar. So I'm just going to figure out the value of one dollar remaining balance. And I'm going to call that


[User] Please transcribe this text

[Assistant] I'm in some node in this interest rate tree, right? Here's our interest rate tree, and I'm anywhere, just here.


[User] Please transcribe this text

[Assistant] And I'm doing backward induction, so for all successor nodes, I figured out what


[User] Please transcribe this text

[Assistant] Okay, so what is the remaining balance? It's this node, which I call S, so it's some node right there of R.


[User] Please transcribe this text

[Assistant] Okay, now why, why is this right? I hope it is right, by the way. Should have thought of this a little before.


[User] Please transcribe this text

[Assistant] Okay, then I know that in the next period I'm going to have B6 over B5 dollars of remaining balance left. Doesn't sound too convincing, by the way.


[User] Please transcribe this text

[Assistant] Okay, well, right, and that happens with probability a half, and then with the other probability a half, plus, I make the payment, but I go down instead of up,


[User] Please transcribe this text

[Assistant] You know, assuming I had a $1 remaining balance, okay, I'm either going to


[User] Please transcribe this text

[Assistant] That's it. So I know now by working this forwards, I can always tell backwards, I can tell what a dollar at the beginning is worth.


[User] Please transcribe this text

[Assistant] And so it's exactly the same calculation I did before, except I'm talking about a dollar. I'm always figuring out a dollar's remaining balance instead of the whole thing.


[User] Please transcribe this text

[Assistant] And so remember the present value of a callable mortgage is 98.8. Here are the present value of a dollar.


[User] Please transcribe this text

[Assistant] Figured it out that way. It's .98, obviously. It's divided by 100, but the key is that now you can see just by looking at it where the ones are is where the guy decided to prepay. Okay, so it's the same thing as before, but you see before, you couldn't tell very easily from the numbers, uh, when I did the 100. Sorry, I didn't quite make it.


[User] Please transcribe this text

[Assistant] Alright, that's the only purpose of doing this somewhat, the same calculation, in a somewhat trickier way.


[User] Please transcribe this text

[Assistant] Alright, so this tells us what to do, when the guy should prepay and when he shouldn't prepay. So if you're now in the world looking at what's happening,


[User] Please transcribe this text

[Assistant] You can find the historical record of how people have prepaid. So let's just look at the historical record, for example. Uhm, here if you can see this, uh,


[User] Please transcribe this text

[Assistant] So what is this? This is prepayments for a particular mortgage. 8%. You take all the people in the country who started in 1986 with 8% mortgages. There's a huge crowd of those because that was about what the mortgage rate was that year. So a huge collection of people got these mortgages in 86.


[User] Please transcribe this text

[Assistant] And you keep track of what percentage of them prepaid really every month but you write the annualized rate and then this is the record. So why do you think it changed so dramatically like that? What's the explanation? What?


[User] Please transcribe this text

[Assistant] Okay, it looks like the stock market, but I assure you the stock market had almost nothing to do with it. Why would pre-payments be so low, and then be so high, then be low, then be high? What do you think was happening?


[User] Please transcribe this text

[Assistant] Interest rate. We just did that. We just solved that. That was the whole point of what we were doing. So you tell me, what do you think happened in 93?


[User] Please transcribe this text

[Assistant] This is September 93, I don't know if you can read that. What do you think was going on then?


[User] Please transcribe this text

[Assistant] Interest rates go low, exactly. So, you may not remember this because you were barely born.


[User] Please transcribe this text

[Assistant] In the 90s, the early 90s, there was a recession and the government kept cutting interest rates further and further and further. There was a huge decline in interest rates through the early 90s.


[User] Please transcribe this text

[Assistant] And so, what happened? All these people who, who, in 86, who had these 8% mortgages, the new, you know, interest rates were lower, and so they all prepaid. You got this shocking amount of prepayment. Okay, so this graph, which seems sort of


[User] Please transcribe this text

[Assistant] You know, surprising and looks like the stock market turns out to have nothing to do with the stock market.


[User] Please transcribe this text

[Assistant] It has to do with where the interest rates are. Well, do you think interest rates explain everything? Uhm, no. What else could you notice about the, uhm, escape?


[User] Please transcribe this text

[Assistant] Okay, what else could you notice, what else do you think that, uh, what else have we learned here by doing these calculations? Okay, well what we've learned so far is that if the people are, you know, if the interest rates in the economy are at 6%,


[User] Please transcribe this text

[Assistant] That's where they started. Remember, we said they started 6% or 16% volatility.


[User] Please transcribe this text

[Assistant] Which is, oh, here I have 20% volatility, okay, doesn't matter, I mean, that's a plausible amount of volatility, a little high, but, okay, that volatility, the, uhm, mortgage rate of 8%,


[User] Please transcribe this text

[Assistant] Is not going to give a value of a hundred. It's going to cheat the bank if the homeowners are acting rationally.


[User] Please transcribe this text

[Assistant] You know, the bank could get 120 if the people weren't acting rationally. They were just never exercising their option.


[User] Please transcribe this text

[Assistant] They're exercising their option optimally, the thing was only worth 8%. Now I told you at that time the interest rate should have been around 7.5%, not 8%, given these 6% interest rate in the economy. The mortgage rate should be 7.5%. So we deduced last time that obviously not everybody's acting optimally. Well you can tell that looking at this, uh,


[User] Please transcribe this text

[Assistant] Diagram. How do you know that not everybody's acting out optimally?


[User] Please transcribe this text

[Assistant] Remember, these are 86 mortgages, so everybody's taking them out at the same time, within a few months of each other, the same 8% mortgage. How can you tell from this graph that they're not exercising their, their option optimally?


[User] Please transcribe this text

[Assistant] Completely obvious. Why? Just looking at it for one second, you can say, oh, these people can't be exercising their option optimally. Why is that? Yeah.


[User] Please transcribe this text

[Assistant] We've just done the calculation with those ones and zeros. I told you when the right time to exercise the option is. So everybody's got the same circumstance. Every single person.


[User] Please transcribe this text

[Assistant] If all they're trying to do is minimize the present value of their payment, they should all be prepaying at the same time.


[User] Please transcribe this text

[Assistant] Here, you see that very few people are prepaying, but you know, it's getting up to almost 10%. So these people are, so probably this is a stupid time.


[User] Please transcribe this text

[Assistant] To prepay. But the point is, there's still 10% of them are prepaying. And over here, one presumably, you ought to prepay in the entire year. Right? They have 12 chances during the year. It takes them an entire year and only 60% of them have figured out that they should prepay.


[User] Please transcribe this text

[Assistant] Okay, so you know they're not acting optimally. So that, just from that graph that would tell you, and you have further evidence of that,


[User] Please transcribe this text

[Assistant] That's evidence that they aren't acting optimally. Furthermore, you have evidence that the bank


[User] Please transcribe this text

[Assistant] Don't expect them to be acting optimally, because the banks aren't charging them 8 or 9% interest, which is what they would need to pay to get the thing worth 100. They're charging them 7.5% interest.


[User] Please transcribe this text

[Assistant] Act in the future. And you wanted to trade on that. How, what would you do? How would you think about predicting it?


[User] Please transcribe this text

[Assistant] So this is the data that you have. What would you go, what would you do? You have this data.


[User] Please transcribe this text

[Assistant] These are 8% things. You also have 9% mortgages issued the year before and then maybe a year before that there were 8.5% interest and you have that history. You've got all these different pools and all these different histories. How would you think about figuring out, uh, a prepayment? How would you predict prepayment?


[User] Please transcribe this text

[Assistant] The way economists, macroeconomists, at least in the old days, used to make predictions, they would say,


[User] Please transcribe this text

[Assistant] The first quarter looks pretty good. Now, what are they predicting now? Now they're saying unemployment is probably going to keep rising for the next quarter or two, uh, till well until the next year.


[User] Please transcribe this text

[Assistant] But at that point, things are gonna turn around and we expect the economy to get stronger, come out of its recession, and unemployment should gradually improve from its high, which we expect will be 10.5% to something back down to 6% by the end of 2011. That's more or less the economist prediction.


[User] Please transcribe this text

[Assistant] If you wanted to predict something and you were going to lose a lot of money if your prediction was wrong, how would you


[User] Please transcribe this text

[Assistant] Refine your prediction compared to what I just gave as a sample prediction. Yep.


[User] Please transcribe this text

[Assistant] If you're even the slightest bit sophisticated, you're not going to make a bald


[User] Please transcribe this text

[Assistant] You know, non-contingent prediction, you know, things are gonna get worse the next two quarters, then they're gonna start getting better, then they're gonna, you know,


[User] Please transcribe this text

[Assistant] You know, things are gonna get as well as they're gonna get after two years. You'll solve the problem after two years.


[User] Please transcribe this text

[Assistant] What happens if another war breaks out in Iraq? What if Iran bombs Israel? What if, uh, uh, you know, there's another crash in commercial real estate?


[User] Please transcribe this text

[Assistant] How could that prediction possibly turn out to be true? It's a sure thing, it's gonna be wrong. It's just impossible that's gonna be right, because the guy making the prediction has made no contingencies built in his prediction. You know that guy's making a prediction for free.


[User] Please transcribe this text

[Assistant] Is to realize that you've got a tree of possible futures.


[User] Please transcribe this text

[Assistant] And given this tree of possible futures, you're going to predict different prepayments depending on what, where you go in the tree.


[User] Please transcribe this text

[Assistant] Okay, so you see prediction is not a simple one, one event.


[User] Please transcribe this text

[Assistant] It's not a one shot thing, just as he so aptly put it, it's a many scenario thing. You have to predict over many, many scenarios.


[User] Please transcribe this text

[Assistant] Things are gonna be a lot worse than this original guy's prediction. Okay, so why not, I mean, everybody knows that, so why not make the prediction more sensible?


[User] Please transcribe this text

[Assistant] Okay, so the art of, so on Wall Street, that's what everybody's done for 20 years. Now they haven't done it for 30 years. It's just 20 years that they've been doing that.


[User] Please transcribe this text

[Assistant] They were making these one scenario predictions. So we had to change, it's a long story which I'll tell maybe Sunday night.


[User] Please transcribe this text

[Assistant] I ended up in charge of the research department and so we made, you know, other firms were doing this already, we made scenario predictions, okay? So now what kind of scenario predictions are you going to make?


[User] Please transcribe this text

[Assistant] Contingent predictions, there are an awful lot of them. You can't even write them all down. So what you have to do is you have to have a model.


[User] Please transcribe this text

[Assistant] So what kind of model should you, uh, should you have?


[User] Please transcribe this text

[Assistant] Okay, well I'll tell you now what the standard guys were doing on Wall Street at the time. They were saying...


[User] Please transcribe this text

[Assistant] Present value of one dollar principle, so realistic free payments. So, if we go over here, we'll see that people said, look, from this graph, it's


[User] Please transcribe this text

[Assistant] Okay, so, so what does that mean? Remember, after you've made your coupon payment, you have a remaining balance, you know, B5, you could pay all of it, or none of it, or half of it.


[User] Please transcribe this text

[Assistant] So the prepay, the prepay is what percentage of the B5 that's just after you've paid, right, so B2, let's do that one, B2, just after you've paid 8.88, the remaining balance has now been reduced to B2. You could, in addition to the 8.88, pay off all of that B2.


[User] Please transcribe this text

[Assistant] Typically, some people who are alert and think it's a good time to prepay will pay all of B2. Others will pay none of B2.


[User] Please transcribe this text

[Assistant] So if you aggregate over the whole collection of people, the prepaid percentages out of the sums of all their B2s, what percentage of them are going to pay off?


[User] Please transcribe this text

[Assistant] So, so we look at the aggregate prepayment, that's the old fashioned way, and we say, what percentage of the remaining balance is paid off? And typically, what, so, that you'd make a function like this, you'd say, well, prepay might equal 10%, why am I picking 10%? So if you go back to this picture, you see that prepayments were


[User] Please transcribe this text

[Assistant] Prepayments, you know, seem to be around 10% when nothing's happening. Okay, so you say 10% plus maybe you're gonna get some more prepayments.


[User] Please transcribe this text

[Assistant] 0.1 plus, you can read, can you see that over there? Maybe not, so uhm, this plus 0.1. Okay, so there's a baseline of 10 percent, and


[User] Please transcribe this text

[Assistant] If the interest rate is high, so the interest rate is above the mortgage rate, no one else is going to prepay because this is going to be a negative number and this will be zero. You're just going to do, uhm, .1 percent. Okay?


[User] Please transcribe this text

[Assistant] On the other hand, if the interest rate gets low and falls far enough below the mortgage rate,


[User] Please transcribe this text

[Assistant] People are going to say to themselves, aha, I have a big incentive to prepay now, maybe the interest rates have gone down so far, I can no longer hope they're going to go back up above the mortgage rate, I should stop, I should start prepaying more, so more people are going to prepay, and this thing is going to go up, you know, and I just multiply it by some constant.


[User] Please transcribe this text

[Assistant] Okay, and, uh, but it'll never go up more than 60%. That's what this function says. In sigma, this is the volatility.


[User] Please transcribe this text

[Assistant] Alright, so let's just leave that aside. So there's a pre-tainment function that seems to sort of capture what's going on.


[User] Please transcribe this text

[Assistant] It's usually around 10% when there's no incentive. It never gets above 60%, but as the incentive to prepay, as interest rates get lower and the incentive to prepay increases, more and more people prepay. That's kind of the idea. Alright, and then you would fit fancier curves than that.


[User] Please transcribe this text

[Assistant] You would look at M minus RT, and you would fit a curve, you know, that looks like this. So if there's just a little bit of incentive to prepay, the rates are a little bit lower than the mortgage rate, nobody does it, then quickly a lot of people do it, and then they stop doing it.


[User] Please transcribe this text

[Assistant] So this is like 60%, and you know, most of the time you're around 10%.


[User] Please transcribe this text

[Assistant] And, uh, you know, you try and fit this curve, you'd have millions of parameters, and since you have so much data, you could fit parameters. Okay, that was the old-fashioned way, and that's how people would predict, uh, prepayments.


[User] Please transcribe this text

[Assistant] So let's look at what happens if you now, with those realistic prepayments, you compute


[User] Please transcribe this text

[Assistant] The value of a mortgage. So this is the prepayment that you get for the different rates and so you can see that as the rates go down, uhm, as the rates go down, the total prepayment is going up.


[User] Please transcribe this text

[Assistant] And by the way, it's more than 60% because he's got this 10% added to the 60%. So the most it could be is 70%, which it hits over here.


[User] Please transcribe this text

[Assistant] So, you get 70% as the maximum prepayments, and you know, as interest rates get higher, no one prepays except the 10% of guys. Now by the way, why are people prepaying over here, even when the rates are so high? It's because some people are moving, or they're getting divorced, and they have to sell their house. So obviously, you're gonna get some prepayments no matter what.


[User] Please transcribe this text

[Assistant] People have to prepay, and why is it that people never prepay more than 60% historically or 70%? Because not everybody pays attention. Now I called them the dumb guys last time, but as I said,


[User] Please transcribe this text

[Assistant] You know, I probably fit into that category. It's people who are distracted and doing other things. They're just not paying attention and so they don't realize, you know, they don't know what's going on. They don't realize they should be prepaying.


[User] Please transcribe this text

[Assistant] So as interest rates go down, more people prepay. As interest rate goes up, less people prepay. And if you did some historical thing, and you don't figure out the right parameters, you'd get a prepayment function. So, you know, how did I figure out this was 15? How did I figure out this was .6? Why should I divide this by 133? What's sigma?


[User] Please transcribe this text

[Assistant] Okay, once you get those parameters historically, you now have a well-determined, uh, behavior rule, what people are going to prepay, and from that you can figure out what the prices are of any mortgage by backward induction.


[User] Please transcribe this text

[Assistant] So how would you do it again by backward induction? The same way we always did it. Over here,


[User] Please transcribe this text

[Assistant] Okay, what would, what would you do over here? How would you change this rule?


[User] Please transcribe this text

[Assistant] Well, you would just be feeding in the prepayment function. Okay, so.


[User] Please transcribe this text

[Assistant] So what would the prepayment function be? Well, people wouldn't be doing a minimum here, right? They're not deciding whether or not to prepay, they're just prepaying. So, so let's get rid of that. They're prepaying.


[User] Please transcribe this text

[Assistant] So some of them are, so this is the value of a dollar's left of principal.


[User] Please transcribe this text

[Assistant] So some of them are prepaying, and that's the function. So prepay, and that depends on what node you're at.


[User] Please transcribe this text

[Assistant] And here it says what fraction, what percentage of the remaining balance is being prepaid.


[User] Please transcribe this text

[Assistant] So that tells you, that rule, who's prepaid, and then with the rest of the money, that's going on until next time, one minus, that same thing, one minus prepaid, times exactly what we had before.


[User] Please transcribe this text

[Assistant] Okay, so this part of the dollar got prepaid immediately, so that's the cash that went to the mortgage holder. The rest of the cash got saved till next time, and here's what happens to it.


[User] Please transcribe this text

[Assistant] Okay, you have to make your coupon, then you have a remaining balance, and then whatever's gonna happen is gonna happen.


[User] Please transcribe this text

[Assistant] Okay, so, that's the, uh, okay, so doing that by, so you'll study this and you'll figure out, I'm sure, it takes a little bit of effort to see that through.


[User] Please transcribe this text

[Assistant] You know, you get a value of a mortgage, okay? And now we can start doing experiments by changing the parameters and see how the mortgage works. Now before I do that, I want to say that there's a better way to do this.


[User] Please transcribe this text

[Assistant] Okay, I mean maybe these numbers are estimated where interest rates didn't, what's a better way of doing it? So how, how would I, how did I do it at Ellington? How did we?


[User] Please transcribe this text

[Assistant] I mean, at Kidder Peabody, how did we predict prepayments? What's another way of looking at, at, uh,


[User] Please transcribe this text

[Assistant] I used to ask people who wanted to work at Kidder Peabody or Ellington the following little simple puzzle.


[User] Please transcribe this text

[Assistant] And most of the genius mathematicians always got this answer wrong.


[User] Please transcribe this text

[Assistant] Of course we hired them anyway, but they'd always get this wrong. So the question is, suppose you've got a group of people like this.


[User] Please transcribe this text

[Assistant] Okay, and you figure out, uh, what the value of the mortgage is.


[User] Please transcribe this text

[Assistant] And interest rates have been constant all this time. Let's suppose for one month interest rates shoot up, shoot down, interest rates collapse.


[User] Please transcribe this text

[Assistant] And half the pool, 60% of the pool disappears.


[User] Please transcribe this text

[Assistant] So now you've only got 40% of the people left you have before and then interest rates return to exactly where they were to begin with. Okay, should the pool that's left


[User] Please transcribe this text

[Assistant] Be worth 40% of the pool that you had just here or more than 40% or less than 40%. So remember you had, you know, 100 people here.


[User] Please transcribe this text

[Assistant] 60% of them suddenly disappeared in one month, leaving 40 left, but now interest rates are back exactly where they were before.


[User] Please transcribe this text

[Assistant] Is the value of the mortgage starting here with the 40% pool


[User] Please transcribe this text

[Assistant] Worth 40% of what it was originally? More than 40% or less than 40%? What do you think?


[User] Please transcribe this text

[Assistant] Exactly. So that's an incredibly important point. It's called the opposite of adverse selection. Every one of these events is selecting the people left, not adversely, not perversely, what's the opposite of adverse?


[User] Please transcribe this text

[Assistant] Right, it doesn't say anything, it just says your prepayments depending on where you are.


[User] Please transcribe this text

[Assistant] So, you know, whether you were here or here, uh, you're gonna get the same prepayment. But we know that that's not gonna be the case. In fact, it's clear that over here, there must have been a much bigger incentive than there was over there. So the prepayments are the same, but actually interest rates here were vastly lower than interest rates there. Okay?


[User] Please transcribe this text

[Assistant] So this is not such a good function. So how would you improve, what would you do to take into account this adverse selection, actually pro-verse selection? What is the opposite of adverse?


[User] Please transcribe this text

[Assistant] You know, your whole livelihood depends on it. You know, there are millions, trillions of dollars at stake here. You've got to model prepayments correctly. So how would you think of doing this? Just giving you some sense of what a hedge fund does or what anyone in this market would have to do. Well, most of them did this. Of course, they got, okay, so what would you do? Yep.


[User] Please transcribe this text

[Assistant] Right here you'd buy it up, right there. Okay, but.


[User] Please transcribe this text

[Assistant] What model would you use to predict prepayment? Not this one. What, so what would, what would you do to do it? How would you imagine doing it?


[User] Please transcribe this text

[Assistant] You'd imagine making a model just like your intuition. So what does that mean doing?


[User] Please transcribe this text

[Assistant] What the whole group of people is doing every year, but what would you do to build a model?


[User] Please transcribe this text

[Assistant] Okay, so maybe another thing you could do


[User] Please transcribe this text

[Assistant] What if you, instead of adding, having this function that says what the aggregate's gonna do, all the data is aggregates, all you can do is test against aggregate data. But suppose you were


[User] Please transcribe this text

[Assistant] So what we should do now is have different kinds of people. Okay, so, oh gosh, sorry, this is there already. Uhm, so let's go back to where we were before. Okay, so realistic.


[User] Please transcribe this text

[Assistant] Okay, what you ought to do is you ought to say, well, 8%


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[Assistant] Okay, remember we had two kinds of people already. We've already got two kinds of people, sorry. We've got


[User] Please transcribe this text

[Assistant] These guys, the guys who never call. So they're people, you know, that's the kind of person. And suppose you go down here and you have the people who are optimally prepaying. Suppose you imagine that half the people were optimally prepaying and half the people never prepaid.


[User] Please transcribe this text

[Assistant] People, all the optimal prepayers, they're all prepaying. So you start off with half optimal guys and half asleep guys.


[User] Please transcribe this text

[Assistant] Once you get down here, all the optimal guys have disappeared and the pool that's left is all asleep. So of course the pool's worth much more here, you know, given the interest rate than it was over here. In fact, if it goes back then, again, to here, you know, where it was before, sorry, the same line, goes back to here,


[User] Please transcribe this text

[Assistant] Have I done this right? No, I gotta go back twice. Here and then here. So once it goes back to here, if it goes here, here, here, and here,


[User] Please transcribe this text

[Assistant] Okay, then the pool is gonna be much more valuable here than it started there. There are half as many people, but it's worth much more than half of what it was there. Okay, so the way to do this is to break


[User] Please transcribe this text

[Assistant] So then you're looking at the individuals. You're saying, one class of people is very smart, the other class of people, one class of people is very alert. Much better word. One class of people is very alert, one class of people is very unalert.


[User] Please transcribe this text

[Assistant] And as you go through the tree, the alert people are going to disappear faster than the non-alert people, and that's why you're going to have a favorable selection of people who's left in the pool.


[User] Please transcribe this text

[Assistant] Okay, well of course, there's no extremes of perfectly rational or perfectly, uh, uh, you know, asleep in the economy. So what you can do is you can make people in between. You can have people who, how do you make them in between? Well, suppose that


[User] Please transcribe this text

[Assistant] For example, I only did one thing. Suppose it's costly to prepay. Some people just say to themselves, you know I'm gonna have to take a whole day off of work


[User] Please transcribe this text

[Assistant] I'm not gonna write my paper, I might lose some business that I was gonna do that day, whole bunch of stuff I'm losing, so unless I think, you know, I'm gonna subtract that, you know, I'm not gonna prepay, not gonna even think about doing it, unless I can get at least a certain benefit from having done it.


[User] Please transcribe this text

[Assistant] Okay, so you can add a cost of prepaying and people aren't gonna prepay unless the gain that they have by prepaying exceeds


[User] Please transcribe this text

[Assistant] The, the, the cost of doing the prepayment. So to take the simplest case, let's suppose the very act of, never mind the thinking and all that, the very act of prepaying going to the bank literally costs you money.


[User] Please transcribe this text

[Assistant] So if you have, you know, a value, if the thing is 100, and you can prepay, if you do your calculations and don't prepay today, it's worth 98. And if you prepay today, the remaining balance is 94.


[User] Please transcribe this text

[Assistant] You're saving four dollars, but if the cost of prepayment is five, you're still not gonna do it. So you get a guy with a high cost of prepaying, an infinite cost of prepaying, he's gonna look like he's totally unalert. A guy with zero cost of paying is gonna look like he's totally alert.


[User] Please transcribe this text

[Assistant] Okay, so you can have gradations of, uh, of, uh, rationality, and you can have different dimensions. So you can have cost of prepaying,


[User] Please transcribe this text

[Assistant] And you can have alertness. What's the percentage of time you're actually paying attention that month?


[User] Please transcribe this text

[Assistant] What fraction of the month do you actually pay attention? And you can have a distribution of people, different costs and different alertnesses. Okay, so.


[User] Please transcribe this text

[Assistant] That's the model that I built, it's a simplified form of it, gives you an idea. Okay, so uhm,


[User] Please transcribe this text

[Assistant] Here's this burnout effect that I showed that if you take the same coupon but an older one rather than a


[User] Please transcribe this text

[Assistant] Uh, an older one that's burned out will always prepay slower, so the pink one is always less than the blue one because it went through an opportunity to prepay.


[User] Please transcribe this text

[Assistant] Okay, so here you start with a pool of guys on the right.


[User] Please transcribe this text

[Assistant] Okay, on the right, and then after a while, after time has gone down, a lot of them are prepaid and you get the, so here's alertness and cost.


[User] Please transcribe this text

[Assistant] You know, but after time, so you describe a person by what his cost of prepaying is and how alert he is. The more alert he is and the lower the cost of prepaying, the closer the rational is.


[User] Please transcribe this text

[Assistant] The, the, the cost, you know, what the distribution of people in the population, what the standard deviation and, uh, expectation of cost is and of alertness is, and that tells you what this distribution looks like. So you're fitting four numbers and you've got thousands of pools and hundreds and hundreds of months and fitting four parameters, you can end up, uhm,


[User] Please transcribe this text

[Assistant] So look at what happens here. So here's the same data. So I just tell you, you know, I know that in the population, given what I've


[User] Please transcribe this text

[Assistant] Calculated in the 90s there, I know what fraction of the people have this cost and that alertness, what fraction of the people are so close to


[User] Please transcribe this text

[Assistant] I, of people, I apply that to the beginning of every single mortgage, and I just crank out. What would those guys do? You know, in the tree, if they knew what the volatilities were, when would they decide to prepay? And then I have to follow a scenario out in the future, and I say, well, along this path,


[User] Please transcribe this text

[Assistant] Which guy would prepay and which guy wouldn't prepay and what would the total prepayments look along that path?


[User] Please transcribe this text

[Assistant] And so this has generated the pink line from the model with no knowledge of the world except they fit those parameters and look how close it is to what actually happened. So it turns out that it was incredibly easy to predict


[User] Please transcribe this text

[Assistant] Uh, contingently predict what prepayments were going to be and therefore to be able to value mortgages.


[User] Please transcribe this text

[Assistant] Okay, and this was a secret that not many people, you know, a bunch of people understood, but not that many understood. And so for years, we were trading at our hedge fund, at first at Kidder, then at Ellington, with this ability to contingently forecast prepayments at a very high rate. And why was it so?


[User] Please transcribe this text

[Assistant] Stable the prediction and so reliable is because the class of people stay pretty much the same and every year there'd be the same kinds of people with the same kinds of behaviors. Some were very alert, some were very not alert, but the distribution of types is more or less the same and you could predict with pretty good accuracy what was going to happen from year to year.


[User] Please transcribe this text

[Assistant] Of course, then, after 2003 or so, the class of people started to radically change, and many more people who never got mortgages before got them, and it became much harder to predict what they were going to do.


[User] Please transcribe this text

[Assistant] But so in the old days it was pretty easy to predict. And why was it so easy to predict? Because it was an agent-based model.


[User] Please transcribe this text

[Assistant] So these guys who just ran regressions, they had to have a volatility or something parameter. So you see, as volatility goes up, the prepayments are slower.


[User] Please transcribe this text

[Assistant] They just had to notice that and build it right into their function. I didn't even have to think of that. Or burnout. None of those things did I have to think about because if you're a guy optimizing here and volatility goes up, so you reset the tree so that the interest rates can change faster,


[User] Please transcribe this text

[Assistant] The option is worth more, so you're gonna wait longer. You're not gonna just exercise it right away, because you've got a chance, you know, that prices will really go up. So you can wait a little longer, afford to wait longer. So prepayments will slow down. Okay, so all I'm saying, all this is just to say, that if you have the right, so it's agent-based, it's uh, contingent predictions, contingent predictions.


[User] Please transcribe this text

[Assistant] Those two things together enable you to make


[User] Please transcribe this text

[Assistant] Quite reliable predictions about the future if you're in a stable environment.


[User] Please transcribe this text

[Assistant] And uh, so what seems like a bewildering amount of stuff turns out to be pretty easy to explain. Okay, so now what happens?


[User] Please transcribe this text

[Assistant] I had to figure out what the mean and the variance is. So how do I know, so there's mean and variance of cost and mean and variance of alertness to get that distribution, right? So how do I know what the population, so let me just put the picture up again.


[User] Please transcribe this text

[Assistant] So given, so who are the hyper-rational guys? They're the people with the really high alertness up there and the really low cost. So they're the guys back there. They're the, uh,


[User] Please transcribe this text

[Assistant] Uhm, it might be going down. So anyway, the guys with very high alertness and very low cost are the hyperrational people.


[User] Please transcribe this text

[Assistant] Corner. You've got the guys who have very low alertness and very high cost. They're the people who you're gonna make a lot of money on if you're the bank.


[User] Please transcribe this text

[Assistant] So, how do I know how many people are of each type? Well, I don't. I have to, I have to, you know, fit this distribution. But you see, I have so much data. I've got this kind of curve.


[User] Please transcribe this text

[Assistant] This kind of curve I've got for every starting year, you know, for the whole history and there are so many different interest rates and so many different, so, you know, I'm applying that same population at the beginning of every single curve and then seeing what happens


[User] Please transcribe this text

[Assistant] You know, if instead of that curve that I showed you, I put a huge crowd here of perfectly rational people, then I would have found that I would have gotten prepayments, you know, at 100% up there, and at zero all the way over here. And so it wouldn't have fit that curve. So that's how I knew that there couldn't be that many perfectly rational people. Okay, yes.


[User] Please transcribe this text

[Assistant] You mean, how do I know cost and alertness? Maybe there's some other factors. Yes, well, okay, there probably are other factors. So,


[User] Please transcribe this text

[Assistant] So what would you commonsensically think are the factors? What keeps people from prepaying? I think the most obvious one is that it's a huge hassle and they're not paying attention. So those are the first two I thought of. Could you think of another one?


[User] Please transcribe this text

[Assistant] Their age. Okay, exactly. So maybe their, so maybe demography has an effect on it. Okay, so maybe, for example, you get more sophisticated the older you get. So okay, that was another factor we put in.


[User] Please transcribe this text

[Assistant] These were the two main factors, okay. Another factor was growing sophistication. We called it the smart factor. Okay, so that's another factor. Okay, but, but, uh, so over time you get more sophisticated. So, uhm,


[User] Please transcribe this text

[Assistant] Okay, so anyway, the point is with a few of these factors, you got a pretty good fit, it was pretty reliable, and you could predict what was going to happen contingently. Okay, and so now, if you want to trade mortgages, what are some of the interesting things that happen? Okay, well,


[User] Please transcribe this text

[Assistant] Okay, the first interesting thing to notice is that uhm, what do you think happens as the interest rate goes down? Okay, so the first thing to notice is


[User] Please transcribe this text

[Assistant] I'm asking two questions, let's go on the other side, I'm running out of room. Suppose that you have the mortgage value, you know, that you get in the tree, right? So in this tree that we've built, you know, here's the tree, going like that.


[User] Please transcribe this text

[Assistant] And at every node, we're predicting, you know, for each class of people, we're predicting where his ones are, so that class is prepaying. The other class is not as smart, so they, you know, they're not prepaying here, but maybe when things get really low, they'll start prepaying here.


[User] Please transcribe this text

[Assistant] So, each class of people, each cost alertness type has its own tree. They're the same tree, but it's own behavior on the tree, and then I add them all together. Okay, so uhm,


[User] Please transcribe this text

[Assistant] What happens with the starting interest rate?


[User] Please transcribe this text

[Assistant] So here we had 0.06 and this value was, you know, 98 or something.


[User] Please transcribe this text

[Assistant] Right, now suppose the interest rate went down to .05. I drew this picture of interest and mortgage value.


[User] Please transcribe this text

[Assistant] What do you think happens, so the interest starts, this is, you know, 98.


[User] Please transcribe this text

[Assistant] Six percent is there. As the interest rate goes down, what do you think happens to the value, the mortgage?


[User] Please transcribe this text

[Assistant] If you're a bank and you've fixed the mortgage rate is 8%, okay, that's a fixed mortgage rate.


[User] Please transcribe this text

[Assistant] It's gonna go up because the interest rates are lower and the present value of the payments is getting higher.


[User] Please transcribe this text

[Assistant] Okay, so if the interest rate goes down, the mortgage is going to go up, like that. Typically.


[User] Please transcribe this text

[Assistant] Okay, but will it keep going up like this and this? If it were a bond, it would go up like that, right? A bond, you know, a one-year bond which owed 1 over 1 plus r, you know, would keep going up and up.


[User] Please transcribe this text

[Assistant] The value, if r got negative, say, it would go up, but you know, if r got negative, it would go way up like that. So, does the mortgage keep going up like that?


[User] Please transcribe this text

[Assistant] As the interest rate goes down, is the value of the mortgage going to get higher and higher and higher?


[User] Please transcribe this text

[Assistant] He's gonna eventually figure out that he should prepay, like, you know, so it'll go like this. If he's perfectly optimal, he'll never let it go above a hundred. So it's gonna go something like this. As the interest rate gets higher, you get crushed, and as the interest rate gets lower, you don't get the full upside because he's prepaying, you know, at a hundred. He's never letting it go above a hundred.


[User] Please transcribe this text

[Assistant] Right? So if he's not so optimal, maybe your value will go up, but not, you know, not so astronomically high.


[User] Please transcribe this text

[Assistant] So this idea that the mortgage curve, instead of being like this, goes like that, this is what was called negative convexity.


[User] Please transcribe this text

[Assistant] The guys are partly irrational, so it's going above 100. Okay, so it's starting to go like this. Then what do you think? If the interest rate gets really low, what's going to happen?


[User] Please transcribe this text

[Assistant] Alright, you just said it. So, if the guy was rational, perfectly rational, it would go like that. He'd never let it go above 100. But now suppose guys are not totally rational. What's going to happen is...


[User] Please transcribe this text

[Assistant] They're going to sort of, as rates get a little bit low, they're not, they're gonna overlook the fact that they should prepay, so now it's advantageous to you, things are worth more than a hundred. But if rates get incredibly low, even the dumbest guy, the least, the least, the highest cost guy, is gonna realize he has an advantage to prepay, and so things are gonna go back down like that.


[User] Please transcribe this text

[Assistant] So the value is going to be quite complicated. So this is the mortgage value as a function of interest rates. Just common sense will tell you this, that, okay, in a typical bond, if the interest rate gets lower, the present value gets higher, you should expect a curve like that. But because of the option, if we're rationally exercised,


[User] Please transcribe this text

[Assistant] The curve would never get above 100, it would have to go like that. But now if people are irrational, you can take advantage of them and get more than 100 out of them. But if the situation gets so favorable to you,


[User] Please transcribe this text

[Assistant] It becomes blindingly obvious eventually to them that, uh, you know, they're getting screwed and eventually they act and bring it all the way back to a hundred again. So the value of the mortgage looks like that. That's a very tricky thing. I'll even like. Very tricky.


[User] Please transcribe this text

[Assistant] Okay, so if you don't know what you're doing, you could easily get yourself uh, hurt holding mortgages.


[User] Please transcribe this text

[Assistant] Okay, you could suddenly find yourself losing money holding mortgages. So that's my next subject here. I want to talk about hedging.


[User] Please transcribe this text

[Assistant] Okay, so we know something now about valuing mortgages


[User] Please transcribe this text

[Assistant] And I'm gonna, I'm gonna, okay, so, if you hold a mortgage, you're gonna hold it because maybe you can lend a hundred to a bunch of people, but actually get a value that's more than a hundred. So it looks like you're here, but you know, if interest rates change a little bit, suddenly this huge value you thought you had might collapse back down to a hundred.


[User] Please transcribe this text

[Assistant] So it's easier to understand there, and I, and so many of you will have thought about this before, so you'll be able to answer it. But if I put it in the mortgage context, it would seem just too difficult. I don't know why I did that. So a world series, I'm gonna, I'm gonna lower it in a second.


[User] Please transcribe this text

[Assistant] The Yankees have a 60% chance, I said beating the Dodgers, I thought the Dodgers would be in the World Series, 60% chance of winning any game, uh, against the Phillies in the World Series.


[User] Please transcribe this text

[Assistant] And you are a bookie and your fellow bookies all understand that it's sixty percent.


[User] Please transcribe this text

[Assistant] So some naive Philly fan comes to you and says,


[User] Please transcribe this text

[Assistant] I want to bet $100 that the Phillies win the World Series. Okay, should you take the bet or not?


[User] Please transcribe this text

[Assistant] Yes, you should take the bet, because 60% of the time, you're going to win $100. No, no. Yes, you should take the bet. If you bet on one game, you would, uh, make...


[User] Please transcribe this text

[Assistant] With 60% probability, you'd win 100. And with 40% probability, you'd lose 100. So that means on average, your expectation is equal to 20.


[User] Please transcribe this text

[Assistant] Now suppose he's willing to make the same bet $100 for the entire series. What's your chance of winning? And what's your expected profit from him? Is it less than 20, 20, or more than 20?


[User] Please transcribe this text

[Assistant] More than twenty, okay? It's gonna turn out to be, so a seven game series, gonna turn out to be forty-two, which we're gonna figure out in a second. Okay, but what's your risk? What's your risk? If it's one game, in either case, you might lose a hundred dollars. The Phillies, you know, they're, they're


[User] Please transcribe this text

[Assistant] Five home runs in the first four games or something and some other unknown guy hits another four home runs and you know, you lose the World Series. You could lose a hundred dollars. And maybe the guy's not betting a hundred dollars but a hundred thousand dollars or a hundred million dollars. You know you've got a favorable bet.


[User] Please transcribe this text

[Assistant] But you don't wanna run the risk of losing, even though there's not that high a chance you're gonna lose. What can you do about it? Okay, so, uh, well.


[User] Please transcribe this text

[Assistant] You know that there's these other bookies out there who every game are willing to bet at odds 60-40.


[User] Please transcribe this text

[Assistant] Either direction on the Phillies or the Yankees, because they just all know, just like you, you all know that the odds are 60% for the Yankees winning every game.


[User] Please transcribe this text

[Assistant] So suppose this naive guy, the Phillies fan, comes up to you and bets $100 on the World Series that the Phillies will win.


[User] Please transcribe this text

[Assistant] You don't want to run the risk of losing $100. You know there are these other bookies who are willing to take bets a game at a time, 60-40 odds. What should you be doing? What would you do?


[User] Please transcribe this text

[Assistant] So what would you do? So this guy's come to you and you're not going to be able to give


[User] Please transcribe this text

[Assistant] We're gonna find out exactly what you should do in one second, but let's just see how far you can get by reason without calculations.


[User] Please transcribe this text

[Assistant] So okay, this guy's come to you and said, I'm betting $100 on the Phillies winning the World Series. This is the night before the first game. Every bookie's standing by ready to take bets at 30 to 20 odds. What would you, uh, do?


[User] Please transcribe this text

[Assistant] It's not so obvious what to do, right, but he's got exactly the right idea. You can hedge your bet by betting on the, uh,


[User] Please transcribe this text

[Assistant] God, I meant to hit mute, I think I hit off. Okay, so.


[User] Please transcribe this text

[Assistant] I can see it. Uhm, alright, so what happens is you'll have a tree which looks like this, and like this, like this, and like this.


[User] Please transcribe this text

[Assistant] Okay, like this and like this, and let's say we go out a few games, like this.


[User] Please transcribe this text

[Assistant] Now, this is a one, two, three game series. Alright, so I've done it. Here's the, here's the, uh,


[User] Please transcribe this text

[Assistant] Here's the start of the World Series. This is the World Series spreadsheet you had before. Now, here's the start, here's game.


[User] Please transcribe this text

[Assistant] So if you win the one, if the Yankees win the series, they get a hundred dollars.


[User] Please transcribe this text

[Assistant] Okay, so every time you end up above the start, win more than you lose.


[User] Please transcribe this text

[Assistant] You get a hundred dollars. On the other hand, if you lose more than you win, you lose a hundred dollars. Okay, and so, control copy here is losing a hundred dollars.


[User] Please transcribe this text

[Assistant] Okay, so now this tree, remember from doing it before, is just by backward induction. If you look at this thing up there, it says you get


[User] Please transcribe this text

[Assistant] You know, sixty percent, I think, was the number we figured out, uh, over here.


[User] Please transcribe this text

[Assistant] Right, so 60% is the probability the Yankees win any game, and so if you figure it so, uhm,


[User] Please transcribe this text

[Assistant] You take any node, like, uh, you know, this one, you're always taking 60% of the value up here, plus 40% of the value here. Okay, so if you do that, you find out,


[User] Please transcribe this text

[Assistant] That the value to you is $42, just what we said. Let's put that in the middle of the screen. The value is $42. Okay. Now, if you, if the Yankees win the first game,


[User] Please transcribe this text

[Assistant] You're in much better shape. So winning the first game means you moved up.


[User] Please transcribe this text

[Assistant] Okay, and if the Yankees lost the first game, you would have gone down to that value, which is like nine dollars.


[User] Please transcribe this text

[Assistant] So you see the risk that you're running and you can calculate the difference. So what should you do in the very first game?


[User] Please transcribe this text

[Assistant] That's 42 because it's the average of this and this, and 64 is the average of .6 of this and .4 of that. Okay, so, what should you do?


[User] Please transcribe this text

[Assistant] Well, on average you're going to make $42. What's the essence of hedging? You want to guarantee that you make $42 no matter what happens, no matter who wins the series, you want to end up with 42 extra dollars. Assuming, you know, the interest rate is zero from the beginning to the end of the series. So how can you arrange that? What can you do?


[User] Please transcribe this text

[Assistant] Okay, so that's the mystery. Give you one second to try and think it through.


[User] Please transcribe this text

[Assistant] You can bet with another bookie at 60 to 40 odds. If the Yankees win the first game, you're just doing great.


[User] Please transcribe this text

[Assistant] In fact, okay, if the Yankees lose the first game, you know, you're looking to be in a little bit of trouble. So the point is, you're not going to get the payoff to the very end, either plus 100 or minus 100, but already by the first game, you're either doing better than you were before or worse than you were before. You're already, in effect, suffering some risk at the very beginning. So this is one of the great ideas of finance.


[User] Please transcribe this text

[Assistant] You can, you shouldn't hedge the final outcome, you should hedge next day's outcome. If you were marking to market, that's what you'd have to do. Marking to market, you'd have to say, my position now, my bet is worth 64 dollars. The Yankees lost the first game, the bet would be worth 9 dollars.


[User] Please transcribe this text

[Assistant] So what does it mean to protect yourself? Not just protect yourself against what's happening at the end. That's really what you want to do. But in order to do that, you should protect yourself every day against what could happen.


[User] Please transcribe this text

[Assistant] Because after all, that's what you're trying to lock in. No matter who wins the first game, you should still say, I'm $42 ahead because I got myself in this position. So how could you do that? Well, let's bet.


[User] Please transcribe this text

[Assistant] At three to two odds, right, sixty-forty is three to two odds. Let's make a bet with another bookie at twenty-two and thirty-three here. So twenty-two, put it in the wrong place, this is the thirty-three, and this is twenty-two. But plus thirty-three and minus twenty-two. So what are you doing here?


[User] Please transcribe this text

[Assistant] Notice that this is 2 times 11, this is 3 times 11, this is 60, 40 odds. I'm betting


[User] Please transcribe this text

[Assistant] On the Phillies. If the Phillies win one game, I collect $33. That's what I should do. That's what he said. He said go to the bookie.


[User] Please transcribe this text

[Assistant] Across the street and bet on one game, not the whole series, bet on one game with that bookie across the street, $33 versus $22.


[User] Please transcribe this text

[Assistant] Let's say you can only bet one game at a time with the other bookies. Actually, maybe you were saying all along, bet on the whole series. Let's say you can only bet one game at a time with the other, uh, bookies. You bet $33 on the Phillies in the first game.


[User] Please transcribe this text

[Assistant] That naive Philly fan has put up $100 on the series. You're, in the first game, gonna put $33. You've taken his bets. You're, you're, you're hoping the Yankees win. But that's bad to be in a position where you have to hope. You don't want to do that.


[User] Please transcribe this text

[Assistant] So you take his bet on the Phillies, you know, cause he's giving you 100 to 100 odds, even odds, even though you know the Yankees have a 60% chance of winning, you go to the bookie across the street and you bet at 60-40 odds on the Phillies.


[User] Please transcribe this text

[Assistant] But you don't bet the whole hundred, you only bet thirty-three dollars of it.


[User] Please transcribe this text

[Assistant] So if you win, you get $33, if you lose, you only have to pay the guy $22. So what's gonna happen? After the first day,


[User] Please transcribe this text

[Assistant] This position is gonna be worth 42, and this position is also gonna be worth 42 exactly where you started.


[User] Please transcribe this text

[Assistant] So because a win in the first game is going to put you so far ahead, and you're back with the first naive filly, better,


[User] Please transcribe this text

[Assistant] And a loss in the first game is going to put you so far behind, you hedge that possibility by going, uhm,


[User] Please transcribe this text

[Assistant] 33-22 in favor of the Phillies. You take a big bet on the Yankees.


[User] Please transcribe this text

[Assistant] And then you make a smaller bet on the Phillies that cancels out part of the big bet on the Yankees.


[User] Please transcribe this text

[Assistant] But you've made the two at different odds, and so on net, you're still going to be $42 ahead.


[User] Please transcribe this text

[Assistant] Okay, so that, let's just pause for a second and see if you got that. So by doing this, you can't possibly


[User] Please transcribe this text

[Assistant] Now you're going to repeat this bet down here and here. Okay, so in the next, uhm, you see where things go next. Here you're down eight dollars.


[User] Please transcribe this text

[Assistant] Okay, if you, if you lost again, you'd be down $32. Now things would really be bad. After the Yankees lost two games in a row, your bet with the original, your original bet would look terrible.


[User] Please transcribe this text

[Assistant] Okay, but things aren't so bad because you've been betting, you bet on the Phillies here, you already made $33.


[User] Please transcribe this text

[Assistant] Okay, well, you wanna lock in $42 at every node, no matter what happens. This $42, by making the right offsetting bet, you can keep $42 everywhere here, until the very end, and so no matter what happens, you can always end up with $42.


[User] Please transcribe this text

[Assistant] Okay, that's the essence of hedging. So let's, let's just say it again with the idea.


[User] Please transcribe this text

[Assistant] Okay, it's a great idea and we don't have time to go through all the details, but the great idea is this. You've made some gigantic bet with somebody. Why do you bet with anybody? Because you think you know more than they do. The whole essence of


[User] Please transcribe this text

[Assistant] You know, trading and finances, you think you understand the world better than somebody else.


[User] Please transcribe this text

[Assistant] Doesn't really know is gonna happen. So you're making a bet on whether you're right or wrong. Okay, so just because you think you, when you say you know, you don't know for sure. You just have a better idea than he does. So you don't want, you want to use your idea without running the risk. So how can you do it if your idea is really correct?


[User] Please transcribe this text

[Assistant] Okay, there may be a way so that you can eliminate the luck.


[User] Please transcribe this text

[Assistant] So here, if you really know the odds are 60-40, your class of bookies knows the odds are 60-40, and some other guys who doesn't know thinks the odds are 50-50 and is willing to bet against you, you can lock in your $42 for sure. You don't just take a bet and, uh, you know, hope you win, you can


[User] Please transcribe this text

[Assistant] Take a bet and then hedge it to lock in your profit for sure, uh, step by step. And that's what we have to explain how that dynamic hedging works. So, uhm, I have to stop, unfortunately. So, Sunday we're gonna do the, uh, class about mortgages.


[User] Please transcribe this text

[Assistant] Hello listeners, welcome back to Luke's English Podcast. This is episode number 865, and it's called Catching Up with Amber and Paul number 13. And I have just a couple of things I want to say at the beginning here before we get started, but I will keep this very brief, okay? So first of all,


[User] Please transcribe this text

[Assistant] This episode sees the very welcome return of my good friends Amber Minogue and Paul Taylor. Amber and Paul have been regular guests and very popular guests on this podcast since I first got to know them and invited them on the show about 10 years ago. So they've been regular guests over the last 10 years.


[User] Please transcribe this text

[Assistant] Uh, for those people who don't know, uh, Amber and Paul are both stand-up comedians and that's how I know them. We know, we all know each other, uh, from doing stand-up comedy in Paris in English. Now, uhm,


[User] Please transcribe this text

[Assistant] Paul is definitely the most successful comedian of the three of us. In fact, he's a professional, full-time professional stand-up comedian. And he does his own one-man shows. And when we recorded this...


[User] Please transcribe this text

[Assistant] Paul was just on a break from a big world tour that he was doing, touring his latest one-man show, which was called Bizu Bye.


[User] Please transcribe this text

[Assistant] Uhm, it's a bilingual show in both French and English. You heard me talking about it in the last episode. So when we recorded this, Paul was on a break from that tour. So that kind of gives you context. Paul talks about staying in hotel rooms and uhm,


[User] Please transcribe this text

[Assistant] Sleeping in his own bed for the first time and getting a bad neck because he's not used to it.


[User] Please transcribe this text

[Assistant] So that's because he'd been on tour, touring around his comedy show.


[User] Please transcribe this text

[Assistant] Uhm, and also I need to say that this episode, this conversation was recorded a couple of months ago actually, in the autumn, all the way back in the autumn of 2023, so a couple of months ago.


[User] Please transcribe this text

[Assistant] That's why we talk about, for example, uh, the weather turning cold and some other things like that. You will also hear us talking about this big final show that Paul did.


[User] Please transcribe this text

[Assistant] Uh, at the Zenith, that's this big venue, this big concert venue in Paris. So Paul talks about


[User] Please transcribe this text

[Assistant] Uh, the fact that he's going to do a show at the Zenith in front of about four or five thousand people. And, uh, I tell you how to get tickets for that. But, uh, if, again, if you listen to the last episode, you will know that that show has already happened.


[User] Please transcribe this text

[Assistant] Okay, that show took place on the 6th of January, which is now in the past. So I'm sorry, if you, if you watch this or listen to this and you think, oh, that would be fantastic, I'd love to get tickets to see that show, then unfortunately, it's not possible unless you have a time machine. But uhm,


[User] Please transcribe this text

[Assistant] Yeah, so sorry about that, but to be fair, I did actually tell you about the show on this podcast in the introduction to episode 855 and a couple of other episodes. I did actually announce that the show was happening.


[User] Please transcribe this text

[Assistant] So I did give you a warning to get tickets, but anyway, sorry if you wanted to get tickets and now you realise that time travel is just not possible. If I had a time travelling machine, if I had a time machine, then I would let you use it, because the show was great.


[User] Please transcribe this text

[Assistant] He invited me to perform, actually, uhm, and I did 10 minutes. You can listen to the last episode of this podcast to hear me talking about that.


[User] Please transcribe this text

[Assistant] Okay, I think that's basically what I wanted to say, really. So now I'll stop this introduction and we'll just let the episode begin. So here is a sort of tangential conversation. That means a conversation that goes off in different directions.


[User] Please transcribe this text

[Assistant] Okay, which is typical about all sorts of things. Just enjoy another conversation with Amber and Paul, uh, and so let's get started and here we go.


[User] Please transcribe this text

[Assistant] Hello everyone, welcome back to Luke's English Podcast. Yes, you've seen from the title, it's actually happening. Amber and Paul are on the podcast. Hello. Has the jingle already happened? No, we haven't done the jingle yet. I'm just going straight in. But would you want to do the jingle? We'll do the jingle. Oh no, I have to do the jingle live, don't I? Do you? Well...


[User] Please transcribe this text

[Assistant] I dunno, what am I supposed to do? Put it in afterwards? Do you not edit your, your things? I do edit my things, but, uh, okay, I'll do the jingle. There needs to be a visual then with the jingle too, doesn't there?


[User] Please transcribe this text

[Assistant] So Paul's in a mood because it's his 15 year wedding anniversary, 15, not wedding, 15 year anniversary of being together with your significant other.


[User] Please transcribe this text

[Assistant] Oh, and I don't know, whatever. Yeah, what a state to be in where your own bed is foreign to you, where you're like, I don't like this, I don't like my own bed, I'd rather sleep in a hotel room.


[User] Please transcribe this text

[Assistant] We'll come back to that. So I've got some things to say at the beginning. Actually, my first question is Paul, what's the situation? But I think we know what the situation is.


[User] Please transcribe this text

[Assistant] You nearly murdered, you nearly killed your daughter basically, is probably the crux of the argument.


[User] Please transcribe this text

[Assistant] I mean the, the, the French kids at my daughter's school were all in hats and coats and gloves late August, you know, it's like there's a sniff of, of, of autumn in the air and suddenly they're all pow in these like, you know, either down


[User] Please transcribe this text

[Assistant] Yep, same, same here. Now guys, normally, uhm, normally in these situations I do prepare some questions, don't I? Uh, or even some kind of theme and then we just ignore it.


[User] Please transcribe this text

[Assistant] Because the conversation starts and then it's impossible to keep it under control like some kind of horse. Right? Or a wild horse. It just, it's off and you can't get it.


[User] Please transcribe this text

[Assistant] So now, uh, this summer, right, I went through my entire episode archive, because I had to do some website admin.


[User] Please transcribe this text

[Assistant] Okay. Right. I had to update every single embedded player, every single audio embedded player for every single episode on every single website page for this podcast. I took the entire month of, uh, I took all of July and August off.


[User] Please transcribe this text

[Assistant] I had to update all the audio players, right? So I went through every single


[User] Please transcribe this text

[Assistant] And also, I saw you two, because you've been on this podcast quite a lot of times. I don't know how many it is. It's gotta be something like 50, 60 times. No. Seriously. No. Seriously, yeaah. It's something in the region of that, okay? I mean, this is one of my favourite things to do.


[User] Please transcribe this text

[Assistant] So it can't be 50. I don't know. I haven't counted actually the number. This is, this episode is going to be called catching up with Amber and Paul and this is number 13 of these.


[User] Please transcribe this text

[Assistant] Uhm, highlights, just a few highlights, if you remember, from the very first time you were both on, which I think was in like 2013 or 14.


[User] Please transcribe this text

[Assistant] It was when you were about to give birth to your son and you, you came on for Christmas time.


[User] Please transcribe this text

[Assistant] Continue, you don't know your own language. There's obviously Amber's lovely voice, Paul's infectious laugh. Do you remember Paul's naive eyes?


[User] Please transcribe this text

[Assistant] Do you remember the comment about Paul's naive eyes? Apparently you have naive eyes. I think it was naive. Okay. No, I don't remember that. Innocent eyes. Yeah. Okay. We did the bank robbery, we did the role-playing games, like the one with the robot teacher who, you know, was very prescient.


[User] Please transcribe this text

[Assistant] The burlap sack from the Victorian detective series, the philosophy quiz, Paul's sudden meteoric rise to success. I was thinking about that when I wrote that, meteoric rise. Is that like a meteor? Because meteors do not rise, they go down.


[User] Please transcribe this text

[Assistant] In Australia, in Australia, yea. Well, potentially. Yea, anyway.


[User] Please transcribe this text

[Assistant] So there was that. Uhm, there was, you know, the philosophy quiz, the cultural stereotypes, the bad gig stories, uh, talking all about sleeping one episode randomly, Amber's son shitting under a table in a restaurant.


[User] Please transcribe this text

[Assistant] A total of four babies born, plus five now with the new arrival in our family, uh, surviving lockdown with the kids, the lockdown happened.


[User] Please transcribe this text

[Assistant] But seriously though, right? It's been awesome. It's been awesome having you both on the podcast. You've brought a lot to the show and I just wanted to express my gratitude for what you've brought to this podcast by offering you both a token of my appreciation. So I've got you both gifts somewhere. Yeah, yeah, yeah.


[User] Please transcribe this text

[Assistant] You're welcome. You're welcome. I went for the old school pictures because I just thought, you know, that's the sort of the vibe here is that, you know, just as a little reference to the, to the good old days now we can call them.


[User] Please transcribe this text

[Assistant] So uhm, I will make you a cup of tea now. In our new mugs. Paul arrived earlier on, he was like, can I have a cup of tea? And I was like, no.


[User] Please transcribe this text

[Assistant] Do you want to just like, uh, swill out the mugs, uh, with some water and put the water into the plant pots? Oh, that's a good idea. And then I'll get the kettle on. Okay. And then we can sort of settle into this episode, which is, as I said, called Catching Up with Amber and Paul. The idea of these episodes is that, uhm, well.


[User] Please transcribe this text

[Assistant] The piece lily, yes. It's quite a thirsty plant that one. Uhm, the idea being behind these episodes is that basically I just say to you both, so what have you been up to? And then hope that we get about an hour of podcast content out of it.


[User] Please transcribe this text

[Assistant] Then we're gonna go and have lunch, right? And, uh, and then after that, well, we'll see. Hopefully we'll have time to then do, uh, the classic lying game, uhm, which we'll explain in a bit.


[User] Please transcribe this text

[Assistant] Of course we will. There will be a fair number of new listeners who have actually never heard


[User] Please transcribe this text

[Assistant] Yes, it's coming along quite well, yeaah. Uh, you know, YouTube's a funny one, you kind of start, sometimes you wonder, oh, when's the algorithm going to be like, nah, I've finished with him now, I'll move on to someone else, because that's kind of how it feels a little bit on YouTube, that you're just at the mercy of the algorithm.


[User] Please transcribe this text

[Assistant] Yeah, go on then. So what have you been up to, Paul Taylor?


[User] Please transcribe this text

[Assistant] Paul, stand-up comedian, extraordinaire, uh, in English, in French, both. This is your third, uh, spectacular one-man, one-man show. Yeah. Right. This one is called, uh, Bisou Bai. Yeah. Right. Um, and yeah, you're kind of,


[User] Please transcribe this text

[Assistant] Absolutely, yes. So there you go. So Paul's a stand-up comedian and he goes around normally France and French-speaking countries, uh, doing, uh, a show, a one-man show where you tell jokes and stories and that sort of thing. So 18 different countries. Which countries?


[User] Please transcribe this text

[Assistant] And I'm forgetting some. A couple of other countries. In Europe. In Europe. Switzerland? Switzerland, correct.


[User] Please transcribe this text

[Assistant] It dies. Okay, so we can look forward to a bit of that maybe in the lion game.


[User] Please transcribe this text

[Assistant] Uh, you tell the, you now tell the Russian joke on stage in your big show.


[User] Please transcribe this text

[Assistant] I can't do the Russian, so this is a misunderstanding that people who understand, who know about this joke, they think that when I do stand up, I do the Russian joke, as if I can do it every time. No. It's only something that happens once, like completely on the spur of the moment.


[User] Please transcribe this text

[Assistant] And they're late as well, and it's normal at the beginning of the show that you kind of do crowd work.


[User] Please transcribe this text

[Assistant] So that means just asking the audience questions and engaging them in conversation, uh, to get to know them and to get the right rapport going on. And it's normal to sort of ask people where they're from and stuff, right? So these two people arrive late.


[User] Please transcribe this text

[Assistant] Exactly. Now, this is the thing though, when you, when we told Amber about the joke eight years ago, she had a good laugh. We had a good laugh about it. We moved on. We did.


[User] Please transcribe this text

[Assistant] Because then, of course, lots of listeners heard the little story and heard Amber and us laughing about it and they're like, wait, wait, wait, wait, wait, what? Because not only did the people in the audience not...


[User] Please transcribe this text

[Assistant] They're just looking at me, not even like, what's he talking about? Or this is, this doesn't make sense this bit. They're just looking at me like expectantly waiting for the show to start. And I'm like five minutes in, you know, and they're like, uh, you know, they're up for it. They just don't, it's like the jokes I'm making are invisible.


[User] Please transcribe this text

[Assistant] Those are the days when I used to repeat myself a bit more than I do now. I've kind of got to grips with that slightly. But so, a little tangent off from your story, if that's alright, which is that, so I did that on stage twice, they didn't get it, nothing, didn't realise. We told the story on the podcast, people didn't get the joke, they're like, what's he saying about Russian people?


[User] Please transcribe this text

[Assistant] Especially Russian listeners who are very sensitive. They're like, what is he, what joke, what is he trying to say about us? And uhm, and so I noticed all these comments and I was like, then next time.


[User] Please transcribe this text

[Assistant] I had a few comments about the Russian joke. Let me just clarify that for you. I told the joke again and explained it again. Even more comments. I don't understand the Russian joke.


[User] Please transcribe this text

[Assistant] I'll just post this and then they'll understand it. And then of course I get comments underneath the YouTube videos saying I don't understand, including one comment that said I think the problem here is that for Russian people, the words Russian and rushing sound very similar. I was like, what? That's not the problem. That is the joke. That's literally the joke. It's not a problem.


[User] Please transcribe this text

[Assistant] You know, so, and I even got an email from one listener who said, uh, who, uh, was very upset with me because she was like, uh, I understand with this Russian joke, you're making, you're making statements about the way that Russian people rush in to other countries all the time. I was like, no, I'm not fucking talking about that.


[User] Please transcribe this text

[Assistant] It's even though precipitate in English means rain, but precipitation. Oh yeah, that's true.


[User] Please transcribe this text

[Assistant] Did you experience culture shock going back to your own country?


[User] Please transcribe this text

[Assistant] Yeah, go on, go on. I love doing this when I go back. I was in the UK this weekend as well, actually. Were you? Yeah, I went to London.


[User] Please transcribe this text

[Assistant] You know, me, my brother and two of our friends. Like a lads blokes weekend, you know? Um, and whenever I'm in the UK, I love just listening to other people's conversations just to.


[User] Please transcribe this text

[Assistant] Hear the general tone and the kind of indirectness. I think I texted you, uh, maybe a year or so ago saying I'm in London and this is what it sounds like on the train.


[User] Please transcribe this text

[Assistant] Get out of my way. Sorry means I don't understand what you just said. Sorry means everything.


[User] Please transcribe this text

[Assistant] Cheap paracetamol and ten pound vouchers at Boots, the chemist. That's it.


[User] Please transcribe this text

[Assistant] Well, I can't really give an opinion just on the song, just on its own, because for me the song only means anything in context with the story and the rest of it.


[User] Please transcribe this text

[Assistant] I mean the song is, it's alright, it's not like as good as some of their work in the past and uh, you know the production is, it's got that kind of shiny Paul McCartney 2020 kind of production to it.


[User] Please transcribe this text

[Assistant] It's not generative AI, so it's not AI singing with the voice of John Lennon. Instead it's, they've used it to separate his voice from the rest of the recording. So it's his real voice. So that's great.


[User] Please transcribe this text

[Assistant] Everything goes on the drums. George is on guitar and some vocals. Paul is on fricking everything and John is, he's got his vocals in it. Paul re-recorded Paul, uh, John's piano.


[User] Please transcribe this text

[Assistant] And it's actually very good, but there's also, you know, you start to wonder what it's really about.


[User] Please transcribe this text

[Assistant] And the more you kind of get into the story of it, you understand that.


[User] Please transcribe this text

[Assistant] It might even be John singing to Paul in the 70s when he was estranged, when they were estranged from each other. He was holed up in his apartment, sort of not really going out into the world, not really writing or publishing music. Well, he was writing, he was at home writing, but he was in a strange place and, uhm,


[User] Please transcribe this text

[Assistant] I think that him and Paul definitely communicated to each other through songs in the seventies and there's lots of very clear examples of it.


[User] Please transcribe this text

[Assistant] And there's a lot of it that's very, that's a lot more obvious than, you know, that people give you credit for. He's quoting Paul's songs in his song lyrics for, in songs that are supposed to be about Yoko.


[User] Please transcribe this text

[Assistant] And stuff like this and all sorts of, uh, odd things. And, uhm, so there's that and also the lyrics in the song, uh, now and then, uh, from a, there's a, there's a story there as well, which is that basically, okay, I'm going to try and give you the very quick version. Yeah, this is deep, deep cuts.


[User] Please transcribe this text

[Assistant] So, 1981, Paul McCartney was grieving the loss of John Lennon, right? He was on Montserrat, where George Martin...


[User] Please transcribe this text

[Assistant] The Beatles' producer had a recording studio. He was there recording his new album, right? And he invited some friends to come and work with him, including Ringo. He also invited this guy called, uh, Carl Perkins, who wrote Blue Suede Shoes, one of the old American 50s rock and roll guys.


[User] Please transcribe this text

[Assistant] Invited him, because he was friends with him. Uh, Kyle stayed on the island for a few days, had a wonderful time. Apparently wasn't in the best


[User] Please transcribe this text

[Assistant] He had a place in his life at that time, but he had a lovely time and he was so moved by the hospitality and generosity of Paul and Linda that he wrote a song.


[User] Please transcribe this text

[Assistant] And the song was like very smooth country ballad and the last line of the song was uhm, uhm,


[User] Please transcribe this text

[Assistant] If we, uhm, if we never see each other again, dear friend, think of, think about me every now and then. Right? He plays the song to Paul and Linda on the day he's leaving for the airport. Paul is in tears. He's in floods of tears at the end of the song. He has to leave the room.


[User] Please transcribe this text

[Assistant] And, and, because he's crying so much and, and, and he, and Carl's like, oh my God, I'm so sorry to Linda. And Linda's like, no, it's fine. How, you know, he really needed that. It's the first time he's really been able to cry. Uh, and, uh, then she says, but how did you know?


[User] Please transcribe this text

[Assistant] And he goes, what do you mean, how did I know what? And, uh, she says, well, those, the lyrics of the song, those are only Paul and I, we're the only people in the world who know about that.


[User] Please transcribe this text

[Assistant] And he said, you're freaking me out, what is it? And she said, the line you said at the end of the song, those are the last words that John ever said to Paul.


[User] Please transcribe this text

[Assistant] Very superstitious about it and believes that it's John speaking to him through Carl's song. Because Carl was this hero for both of them.


[User] Please transcribe this text

[Assistant] That there was like, this is John's way of reaching out to Paul through Carl.


[User] Please transcribe this text

[Assistant] And it's very weird, and there's a documentary where Carl Perkins tells this story.


[User] Please transcribe this text

[Assistant] And he's just telling this, he's just finished telling the story and his wife buzzes into the studio on the intercom.


[User] Please transcribe this text

[Assistant] There's this telephone call for you, Carl. There's Paul McCartney on the line.


[User] Please transcribe this text

[Assistant] So weirdly, when Carl tells the story on the documentary, at the same moment, Paul just randomly chooses to call him.


[User] Please transcribe this text

[Assistant] So that's weird as well, isn't it? But that's, so that's, that's.


[User] Please transcribe this text

[Assistant] Because of your jokes are full of, your stand-up is full of puns, not, it's not really. So, uh, just to be clear that Paul is, you know, this, his last show is called Bisou Bye. Uh, it's called Bisou Bye because apparently this is the last show he's gonna do, uh, in France. No, it's not, it's the last show.


[User] Please transcribe this text

[Assistant] This is one hour. Yeah, we're going to wrap up soon this catching up session. I think we're nearly caught up. Yeah. But I just wanted to ask you what, how's it going? What have you been up to? Cause obviously you went to London.


[User] Please transcribe this text

[Assistant] Interesting isn't it how girls always end up going, well, not always, often end up going for pink, isn't it? I mean, like, you know, some people believe that we program that into them, but I don't know.


[User] Please transcribe this text

[Assistant] Have your kids said anything funny recently? Because every now and then you text me.


[User] Please transcribe this text

[Assistant] So my daughter, who's five, uh, just a couple of days ago, spilled some yoghurt on the table and she went, fuck.


[User] Please transcribe this text

[Assistant] Going through when she was like one and a half. Oh, look, a book of animals. Horse, you know, cheval in vache.


[User] Please transcribe this text

[Assistant] Fuck. Fuck. Really? So anyway, so she said, fuck. And I was like, what?


[User] Please transcribe this text

[Assistant] So anyway, that's, that was interesting. Interesting moment. They are funny. Yeah, they are funny little kids.


[User] Please transcribe this text

[Assistant] It's like this brown sticky stuff and you're only supposed to have a little bit, unlike some cheese on toast with a little bit of marmite, not eating it with a spoon. Wow, so she's so British. Yeah.


[User] Please transcribe this text

[Assistant] Very good. Well, do you feel like we've caught up now?


[User] Please transcribe this text

[Assistant] Yeah, I think so. Okay, well I think that then what we're, the plan is now- What about you? What have you been up to? Not much really, just uhm- Doing the podcast. Doing this. Crushing. Showing it on YouTube.


[User] Please transcribe this text

[Assistant] Uhm, yeaah, trying, uhm, doing the podcast, updating all the website pages on, you know, my website. Dealing with a new baby. Dealing with a new baby. How is this new baby? He's great, yeaah, he's a total cutie pie, he's a really...


[User] Please transcribe this text

[Assistant] He's good at the moment, I was just saying to Paul earlier, like uhm, he's great now.


[User] Please transcribe this text

[Assistant] Sometimes, you know, wake up a couple of times in the night, but other times just sleep straight through. Yeah, he's a good lad and he smiles and he charms all the ladies.


[User] Please transcribe this text

[Assistant] Yeah, and the guys as well as, I mean he's just a charmer.


[User] Please transcribe this text

[Assistant] Yeah, he's pretty good that way. You can kind of leave him for a while. And so his sister, his sister adores him and she's like, she loves him so much. She goes up to him.


[User] Please transcribe this text

[Assistant] She's like, you know, she can't contain how cute, uh, he is. She wants to just squeeze his cheeks like this. So yeah, so far, so good. We'll see what happens when he moves into her bedroom and starts trying to play with her toys. We'll see how she feels then.


[User] Please transcribe this text

[Assistant] Yeah. Okay. All right. Something to look forward to. Guys, do you want some lunch? Do you want me to buy you some lunch? Yes. Yes. Okay. Great. And then hopefully if we have time, we'll stick around to play.


[User] Please transcribe this text

[Assistant] Or not. Truth or lies, we'll see. So that's probably going to be in another episode, you're just going to have to wait until next time, folks, for that one. But thanks so much for being back here, and I'll speak to you both soon.


[User] Please transcribe this text

[Assistant] I love the world so I like the diamond in the sky. Winkle, winkle, little star. How I wonder what you are.


[User] Please transcribe this text

[Assistant] Here's a quick intro to what's coming your way and how I've structured this course.


[User] Please transcribe this text

[Assistant] First, we'll study the strings as a family, as all orchestral string instruments share basic construction elements and sound production techniques. All string players use bows and pluck strings, and every string player's left hand covers the same set of positions on the fingerboard and sweetens the tone with vibrato.


[User] Please transcribe this text

[Assistant] All these instruments are capable of harmonics and double stops, trills and tremolo.


[User] Please transcribe this text

[Assistant] So we'll study the strings collectively to begin with, and really focus in on the common approach that gives this section such unity in the orchestra.


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[Assistant] As we explore these fundamental techniques, watch the video samples and really absorb how the music is being played and why each player interprets the music as you see it.


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[Assistant] Notice the differences in the way the musical samples are scored and the way they must be performed in order to create a satisfying sound.


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[Assistant] And finally, look at the physical effort and range of motion that the different musicians use as they play.


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[Assistant] This can be quite free and active, as a musician may use their whole body to bring out the expressive qualities of the score.


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[Assistant] This motion, in turn, acts as a guide for the other players of the musician's section, as most of the players I've filmed are principal or co-principal orchestral musicians of top professional orchestras.


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[Assistant] Once you've got a firm grasp on the fundamentals of string playing, we'll study the instruments individually.


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[Assistant] You'll see how the different sizes and ranges affect everything from articulation to phrasing, and give each instrument a special character that can make it stand on its own, or blend seamlessly into a group sound.


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[Assistant] Along the way, those peculiar strengths and quirks will be explored, showing you how to use them to their best advantage.


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[Assistant] If you remember our first video, orchestration is the craft of putting it all together.


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[Assistant] The best way to start is by familiarizing yourself with great orchestral repertoire.


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[Assistant] So I've provided you with a guide to score reading strings, followed with a detailed list of scores to study. Now that you've joined the party, let's get to the introductions, which we'll start in the next video.


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[Assistant] Walk into a wood and you enter a peaceful, familiar world. But what if you look down?


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[Assistant] Beneath every forest and wood there is a kind of mysterious underground social network. Let's peel back the earth to take a look.


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[Assistant] There are the tree roots and then mingling among them, along with bacteria, are thousands of super fine threads of fungi known as hyphae. And research has shown that they're all interconnected.


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[Assistant] They can help each other by sharing nutrients and they can even warn of approaching threats. And scientists have described this as if the trees are talking to one another.


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[Assistant] Now Dr. Thomas Crowther and his team have mapped this subterranean social network of fungi on an epic global scale.


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[Assistant] He likens it to producing an MRI scan of the world's forests.


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[Assistant] There are two main types of fungal network. They both suck up the greenhouse gas carbon, a key factor in climate change. Systems in woods like here in the UK absorb more than ones in tropical climates, but they're more vulnerable to rising temperatures.


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[Assistant] There are the tree roots, and then mingling among them, along with bacteria, are thousands of super fine threads of fungi.


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[Assistant] They can help each other by sharing nutrients and they can even warn of approaching threats.


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[Assistant] Now Dr. Thomas Crowther and his team have mapped this subterranean social network of fungi on an epic global scale.


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[Assistant] Systems in woods like here in the UK absorb more than ones in tropical climates, but they're more vulnerable to rising temperatures.


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[Assistant] Know the right network to plug the tree into and it will flourish.


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[Assistant] In our last video, we covered each of the members of the string section, learning their names, ranges, and basic roles in the orchestra.


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[Assistant] It's important to remember, though, that these instruments are merely the tools used by the musicians who play them.


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[Assistant] A staff of violin music is not just a written out idea by a composer.


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[Assistant] It's essentially a set of instructions for 12 to 16 human beings sitting in close proximity.


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[Assistant] The act of performing that music has its own parameters, some of which are dictated merely by the way the musicians are arranged on the stage.


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[Assistant] Each group of instruments represents a section of its own within the string section, and is seated so that the players can see and hear each other as well as possible.


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[Assistant] The section leaders sit right in front in a semicircle around the conductor, so that his direction to them is immediate and unmistakable. The section seating fans out behind these principal players, allowing all players to see the bowing up front.


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[Assistant] The exception to this is the double basses, who are usually placed in a row in the back because of concerns of acoustics and instrument size.