# Information Flow in the FTX Bankruptcy: A Network Approach

Riccardo De Blasis  Luca Galati<EMAIL_ADDRESS>[ Rosanna Grassi
Giorgio Rizzini

###### Abstract

This paper investigates the cryptocurrency network of the FTX exchange during
the collapse of its native token, FTT, to understand how network structures
adapt to significant financial disruptions, by exploiting vertex centrality
measures. Using proprietary data on the transactional relationships between
various cryptocurrencies, we construct the filtered correlation matrix to
identify the most significant relations in the FTX and Binance markets. By
using suitable centrality measures - closeness and information centrality - we
assess network stability during FTX’s bankruptcy. The findings document the
appropriateness of such vertex centralities in understanding the resilience
and vulnerabilities of financial networks. By tracking the changes in
centrality values before and during the FTX crisis, this study provides useful
insights into the structural dynamics of the cryptocurrency market. Results
reveal how different cryptocurrencies experienced shifts in their network
roles due to the crisis. Moreover, our findings highlight the
interconnectedness of cryptocurrency markets and how the failure of a single
entity can lead to widespread repercussions that destabilize other nodes of
the network.

###### keywords:

Bankruptcy , Centrality Measures , Cryptocurrency Market , Decentralized
Finance , Complex Networks

††journal: Physica A

[label1]organization=Department of Management, Marche Polytechnic University,
addressline=Piazzale R. Martelli 8, city=Ancona, postcode=60121, state=AN,
country=Italy

https://www.unibo.it/sitoweb/luca.galati/en]personal page

[label2]organization=Department of Management, Alma Mater Studiorum -
University of Bologna, addressline=Via Capo di Lucca 34, city=Bologna,
postcode=40126, state=BO, country=Italy

[label3]organization=School of Business, University of Wollongong,
addressline=Northfield Ave, city=Wollongong, postcode=2500, state=NSW,
country=Australia

[label5]organization=Department of Statistics and Quantitative Methods,
University of Milano-Bicocca, addressline=Piazza dell’Ateneo Nuovo 1,
city=Milano, postcode=20126, state=MI, country=Italy

[label6]organization=Faculty of Sciences, Scuola Normale Superiore,
addressline=Piazza dei Cavalieri 7, city=Pisa, postcode=56126, state=PI,
country=Italy

Centrality measures trace network structure dynamics in FTX’s crisis

Network metrics reveal cryptocurrencies’ vulnerabilities

More sophisticated altcoins show their centrality over popular
cryptocurrencies

Network analysis is crucial for risk assessment and crisis management in DeFi

##  1 Introduction

In recent years, the cryptocurrency market has gained a lot of attention from
academics and practitioners [see, e.g., 1]. According to [2], since 2017, the
number of publications on cryptocurrencies has rapidly increased, with more
than 140 papers published only in 2021. Almost half of this research focuses
on the prediction of returns and volatility, while 20% studies the
relationship between pairs and portfolios, and only about 7% of researchers
are interested in bubbles and extreme conditions of this emerging market.
Moreover, the majority of this research (almost 70%) employs statistical and
machine learning methods, and only a partial focus is reserved for the study
of the cryptocurrency market from a network perspective. As such, this study
aims to fill these critical gaps in the literature by examining the network
market structure of the bankrupted cryptocurrency exchange FTX during the
collapse of its token, FTT.

Cryptocurrency markets, as traditional financial markets, are characterized by
a high number and possibly different types of interactions among various
market participants. Due to the intricate nature of such markets, exploiting
complex network tools can be highly effective in revealing hidden attributes
and determining the importance of each cryptocurrency within the market as a
whole. In particular, by the use of centrality measures, it is possible to
assess how central a cryptocurrency is. This allows for the identification of
the relevant cryptocurrencies not only on the basis of the connections with
other cryptocurrencies in the market but also on the information that each can
transmit to others. Notably, the use of these measures is crucial to reveal
the signs of the arrival of a crisis on the market.

In the complex network literature, cryptocurrency markets have been analyzed
following two main approaches. Part of the literature directly analyses the
blockchain public data constructing the cryptocurrency transaction networks,
which are the largest real-world networks with publicly accessible data (see,
e.g., [3]). Conversely, other studies concentrate on the network constructed
from the analysis of the price series. Among possible approaches to construct
networks from time series (see [4] for a review), a particularly powerful
method involves the construction of the correlation matrix: where nodes
represent the assets, and weighted edges capture the relationship among
couples of nodes by means of the correlation coefficient. However, this
approach constructs a full matrix that does not allow the identification of
the informative structure of the network and the filtering of relevant
information. In the literature, there are two main approaches to reducing
information’s redundancy in the correlation matrix and building sparse
networks containing only relevant edges: (i) the minimum spanning tree (MST)
and (ii) the planar maximally filtered graph (PMFG).

The approach proposed in the seminal work [5] belongs to the first stream of
research. The author proposes a method filtering the most important $n-1$
links from a $n\times n$ correlation matrix to construct the MST by
introducing an ultrametric that transforms correlations into distances. [6]
follows a similar approach, first building the network of stocks from the New
York Stock Exchange (NYSE) and then extracting the MST from the distance
matrix. Regarding the second filtering approach, the authors in [7] extend the
methodology in [5] proposing a heuristic algorithm to construct the Planar
Maximally Filtered Graph (PMFG) filtering the correlation matrix. [8] analyzes
the topological features of a class of PFMG networks from the returns of the
300 most capitalized stocks traded at NYSE during the period 2001–2003 at
different time horizons. Recently, [9] proposes a new efficient algorithm to
filter the correlation matrix based on triangulation called Triangulated
Maximally Filtered Graph (TMFG).

Among the network applications to cryptocurrencies, [10] examines the MST
network built from the correlations of daily returns of 16 cryptocurrencies
following the approach in [5]. From a static analysis of the network, the
authors identify the Ethereum currency in a central position of the MST, then
as the benchmark within the market, leaving Bitcoin in a peripheral position.
From a dynamic network perspective, [11] analyzes the effects of information
flows in cryptocurrency markets built from the Granger causality111Granger
causality networks are graphs in which two nodes are connected if one of them
causes in Granger meaning (see [12]) the other one. among weekly log-returns
and find a quite stable network structure over time. On the contrary, [13]
examines the stability of the PMFG cryptocurrency network around critical
events using a function of neighbours’ influence strength. The author shows
that critical events lead to significant changes in the structure of the
network from stability to fragility, going back to stability once the critical
time has passed. [14], instead, explores the dynamic downside risk among
digital financial assets (cryptocurrencies, DeFi tokens, and NFTs) and
traditional financial assets (stock indices and commodities) by constructing a
daily network based on the CoVaR measure. They document significant tail risk
spillovers, bidirectional between digital assets and commodities, and
unidirectional from stocks to digital assets and from commodities to stocks.

To this extent, it is worth noting that a major critical event has occurred in
the cryptocurrency industry, the collapse of the FTX market in November 2022.
This event has been analyzed in the literature from different viewpoints. From
a market microstructure perspective, [15] contributes to understanding the
systemic implications of major disruptions in the cryptocurrency markets. In
particular, the authors examine how the halt in withdrawals at a major
exchange affects market liquidity, traders’ behavior, and asset pricing
dynamics. Their findings indicate significant liquidity deterioration and
effectively highlight the critical vulnerabilities within the cryptocurrency
market infrastructure, especially under stress scenarios like bankruptcy and
operational disruptions. Similar to [16] for the collapse of the Terra-Luna
token and [17] for the Silicon Valley bank bankruptcy, [18] uses a BEKK
Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model to
examine the intraday volatility spillover effects of the FTX collapse to other
cryptocurrency exchanges. The authors found evidence consistent with their
proposition, and also examined the information cascade effects of other
cryptocurrency assets on FTX when nearly all withdrawals were prohibited.
Abnormal returns for major assets indicated a flight to safety from less to
more authoritative digital assets. [19] further corroborates these findings,
showing that the FTX collapse increased overall intraday volatility in the
cryptocurrency markets, with stablecoins being the most affected. Their use of
the Multiplicative Component GARCH (MCGARCH) model and Time-Varying Parameter
VAR model (TVP-VAR) methodology revealed FTT’s role as a primary volatility
transmitter, particularly impacting stablecoins like USD Coin and altcoins
like Polygon, thus highlighting the interconnectedness and systemic risk in
the cryptocurrency market.

The recent scholarly examinations of the FTX collapse shed light on its
broader implications as well. [20] finds surprisingly stable systemic risks
and liquidity despite the collapse, suggesting inherent market resilience
against such shocks. [21] identifies specific contagion effects propagated by
FTX-associated tokens like FTT and Serum, which significantly influenced
related financial products. The study [22] reveals that while cryptocurrencies
like Bitcoin, Ethereum, and Binance experienced significant negative returns
due to the FTX collapse, traditional asset markets remained unaffected,
indicating limited contagion. In a similar vein, [23] finds that the FTX
collapse significantly impacted cryptocurrency markets but not traditional
financial assets, showing traditional investors’ indifference to
cryptocurrency fluctuations during bear markets. [24] examines how market
sentiment and investor behavior changed in response to news releases related
to FTX, while [25] argues for enhanced regulatory oversight in crypto markets
to mitigate financial instability risks.

From a network perspective, the FTX collapse has been studied by [26], which
examines the cross-exchange risk in a high-frequency setting employing a
Multivariate Heterogeneous AutoRegression (MHAR) model to build the network.
The network is constructed by filtering the correlations which exceed in
absolute value a fixed threshold. The authors find that FTX bankruptcy
triggered a chain effect among exchanges, i.e., increased partial correlations
with other exchanges, and highlighted the spillover risk and its persistent
effect across centralized exchanges. Additionally, [27] investigates the
causes and consequences of FTX’s failure. Their results reveal that leveraging
and misuse of its native token, FTT, exacerbated FTX’s financial fragility
highlighting the Terra-Luna collapse as a pivotal trigger event. The study
exploits the TMFG to model the evolutionary dependency structures of 199
cryptocurrencies, showing the systemic impact of Binance’s actions on FTX’s
downfall and emphasizing the trend toward centralization in the crypto market.

To the best of our knowledge, this study is the first that combines market
microstructure theory and network analysis to investigate the FTX collapse. It
provides insights into the evolving patterns of suitable centrality measures
throughout the crisis of one the largest cryptocurrency centralized exchanges,
FTX, and unveils the intricate relationships between market behaviors and
investor responses, which, in turn, modify the cryptocurrency network
structures before and during financial disruptions. In addition, our study
provides suitable measures for assessing network resilience and stability. In
particular, it highlights the crucial roles that different cryptocurrencies
play in sustaining the informational architecture of the market during crises.

The results offer a comprehensive picture of resilience and vulnerability
inherent in financial networks, especially within such significant markets as
FTX and Binance, the industry leader (see, e.g., [28]). This also hints at
potential strategic pivot points for stakeholders within the cryptocurrency
ecosystem. Indeed, understanding the centrality dynamics can equip market
participants, analysts, and regulators with deeper insights into the critical
nodes within the networks. This is particularly useful because it potentially
drives investment decisions, risk assessments, and regulatory considerations.
This study, thus, contributes to a better comprehension of the structural
complexities within digital asset markets, revealing the intricate interplay
of connections, resilience, and influence.

The rest of the paper proceeds as follows. Section 2 details the methodology
adopted through a theoretical background on network theory, centrality
measures and filtered graphs. A toy example is proposed to show the
appropriateness of the proposed centrality measures in cryptocurrency
networks. Section 3 describes the data and the empirical findings. Section 4
concludes.

##  2 Methodology

###  2.1 Preliminary definitions on network theory

In this section, we briefly review some mathematical network definitions. A
network is formally represented by a graph $G=(V,E)$, where $V$ is the set of
$n$ nodes (or vertices) and a set $E$ of $m$ edges (or links). Two nodes $i$
and $j$ are adjacent if there exists an edge connecting them, i.e., if
$(i,j)\in E$. $G$ is undirected if $(j,i)\in E$ whenever $(i,j)\in E$. $G$ is
simple if loops – a link joining a vertex to itself – and multiple edges –
more edges incident to the same pair of vertices – are not allowed. In this
paper, we assume that graphs are undirected and simple. A subgraph
$G^{\prime}=(V^{\prime},E^{\prime})$ of $G$ is a graph such that
$V^{\prime}\subseteq V$ and $E^{\prime}\subseteq E$. The degree $d_{i}$ of a
node $i$ is the number of links incident to it. A complete graph $K_{n}$ is a
graph of $n$ nodes such that each node degree is equal to $n-1$. A graph is
weighted if a non-negative real number $w_{ij}$ is associated with each edge
$(i,j)$ of $G$.

The adjacency relations can be represented by a $n-$square matrix
$\mathbf{A}$, the adjacency matrix, whose elements $a_{ij}$ are equal to 1 if
$(i,j)\in E$, and $0$ otherwise. As graphs are simple and undirected, the
diagonal entries of $\mathbf{A}$ are null, and $\mathbf{A}$ is symmetric. The
adjacency relations for a weighted graph are represented by a $n-$square
matrix $\mathbf{W}$, with elements $w_{ij}$ if the weighted link $(i,j)\in E$,
and $0$ otherwise. An $i-j$ path is a sequence of distinct adjacent vertices
from $i$ to $j$. The distance $d(i,j)$ between $i$ and $j$ is the length of
the shortest path joining them when such a path exists, and it is set to
$+\infty$ otherwise. A cycle of length $k$ is a path of $k$ edges in which the
first and the last vertices coincide.

A graph $G$ is connected if there is a path between every pair of vertices. A
connected component of $G$ is a maximal set of nodes such that each pair of
nodes is connected by a path. A connected graph has exactly one connected
component. For a more detailed treatment of graphs and networks, we refer the
reader, for instance, to [29] and [30]. Since the mathematical object
underlying the network is the graph, in the rest of the text, we will use the
words graph and network interchangeably.

Finally, we introduce the concepts of planar graphs and maximally planar
graphs. A planar graph is a graph that can be drawn in such a way that no
edges cross each other, see [31]. A necessary but not sufficient condition for
a graph $G$ to be planar is that $m\leq 3n-6$ for $n\geq 3$. Notice that this
condition implies that planar graphs are sparse networks characterized by a
number of edges of order $O(n)$.

Planar graphs are useful in extracting relevant information from a complex
database represented by a weighted graph. Indeed, in such a complex structure,
it can be meaningful to filter data by unveiling the most significant
information. This can be done by searching for the largest possible subgraph
satisfying some topological constraints. In the related literature about
networks, this problem is known as the Weighted Maximal Planar Graph problem
(see [32]). In the financial context, a first attempt to solve such a problem
is presented in [5] in which the information’s filtering procedure is
performed by extracting the Minimum Spanning Tree (MST), that is a subgraph on
$n$ nodes, connected and without cycles, with $n-1$ edges, such that the sum
of all edge weights is minimized.

A more sophisticated procedure to extract relevant information consists of
constructing the Planar Maximally Filtered Graph (PMFG), which belongs to the
class of Information Filtering Networks (see [7, 33]). Specifically, given the
weighted adjacency matrix of a complete graph $K_{n}$, the authors propose an
algorithm to extract the maximal planar subgraph, with $n$ vertices and
$3(n-2)$ edges, ensuring the highest sum of edge weights. The construction of
PMFG is based on the following procedure: all the edges of the initial dense
network are sorted in non-increasing order, and one edge per time is added to
the PMFG. Edges that violate the planarity constraint are discarded. Edges are
added until the PMFG has exactly $3(n-2)$ edges. Recently [9] proposed a new
algorithm to determine the solution of the Weighted Maximal Planar Graph
problem. The resulting subgraph is based on a triangulation obtained by
maximizing a score function linked to the information withheld by the starting
network.

###  2.2 Centrality measures based on paths

In network theory, centrality is one of the key issues. Broadly speaking, any
element of the network (nodes, edges or groups of nodes) plays a role with
respect to the global network structure. However, to assess their relevance in
terms of connections, the most studied aspect of centrality is the assignment
of a score to the vertices.

The degree centrality of a node $i$ is the most intuitive centrality measure
as it counts the number of neighbours of $i$, and it is formally represented
by the normalized degree $d_{i}=\frac{1}{n(n-1)}\sum_{j=1}^{n}a_{ij}$.

With reference to the information flow, an interesting class of centrality
measures is those based on the concept of distance in the network, namely
paths between pairs of nodes. A relevant role in this framework is certainly
played by the closeness centrality (see [34]). This measure is based on the
length of the paths from a node $i$ to all other nodes in the network, and
formally, it is defined as the reciprocal of the sum of the distance between
$i$ and all other nodes (multiplied by $n-1$ to obtain a normalized measure):

$c(i)=\frac{n-1}{\sum_{j}d(i,j)}.$ (1)

A meaningful weighted version has been proposed by [35], based on the idea of
weighted shortest paths. The identification of the shortest paths is quite
simple for unweighted networks. Indeed, the geodesic distance between two
nodes $i$ and $j$ is the length of the path with the minimum number of edges
connecting $i$ and $j$. The matter is more complicated when links are
weighted. The problem of identifying the weighted shortest path has been
analyzed in many papers, e.g., see [36] among others, most of them based on
Dijkstra’s algorithm, where weights on the links are interpreted as costs of
transmission ([37]). In the milestone papers of [38] and [39] the authors
propose to invert the link’s weight before applying the Dijkstra’s algorithm.
Indeed, a low weight of the link makes the passage through it more costly than
passing through a link with a high weight.222Indeed, given two links weights
$w_{ij}$ and $w_{hk}$ such that $w_{ij}>w_{hk}$ then
$\frac{1}{w_{ij}}<\frac{1}{w_{hk}}$. In such a framework, the weighted
shortest path distance between nodes $i$ and $j$ is defined as

$d^{w}(i,j)=\min\left(\frac{1}{w_{ih}}+\dots+\frac{1}{w_{hj}}\right),$ (2)

and the normalized weighted closeness centrality measure as

$c^{w}(i)=\frac{n-1}{\sum_{j}d^{w}(i,j)}.$ (3)

Another centrality path-based measure is information centrality. This measure
is based on the concept of efficiency introduced by [40] to assess how much
nodes in a network exchange information. The authors assume that the
information between two nodes $i$ and $j$ is spread through one of the
possible weighted shortest paths between the involved nodes. In this way, the
efficiency $\epsilon^{w}(i,j)$ is defined as the reciprocal of the shortest
distance. Indeed, the higher the geodesic distance between $i$ and $j$, the
lower their efficiency in the information’s transmission. The efficiency
$\epsilon^{w}(i,j)$ between $i$ and $j$ is therefore defined as

$\epsilon^{w}(i,j)=\frac{1}{d^{w}(i,j)},$ (4)

so that if $d^{w}(i,j)=+\infty$, i.e. there is no a shortest path between $i$
and $j$, then $\epsilon^{w}(i,j)=0$ and therefore no information can travel
between those nodes. From (4), the efficiency of a graph $G$ naturally arises:

$\varepsilon^{w}(G)=\frac{1}{n(n-1)}\sum_{\begin{subarray}{c}i,j\in V\\\ i\neq
j\end{subarray}}\epsilon^{w}(i,j).$ (5)

Giving the previous definition, the information centrality of a node $i$ is
defined as ([41]):

$c_{I}(i)=1-\frac{\varepsilon^{w}(G’)}{\varepsilon^{w}(G)},$ (6)

where $G^{\prime}$ is the subgraph obtained by removing from $G$ all the edges
incident to node $i$, i.e., broadly speaking, the node $i$ in $G’$ is an
isolated node. This measure is suitable to capture how a network reacts when a
node $i$ is deactivated, i.e., when node $i$ cuts all its links. Thus, it
measures the relative reduction in the network efficiency after the node $i$’s
removal. Information centrality ranges in $[0,1]$: if $c_{I}(i)=0$ it means
that the removal of node $i$ does not affect the efficiency of $G^{\prime}$,
i.e., $\varepsilon^{w}(G)=\varepsilon^{w}(G’)$ while $c_{I}(i)=1$ means that
$G^{\prime}$ is an empty graph, i.e. the edge set of $G^{\prime}$ is empty.
Therefore, among the centrality measures described in this section, the latter
is the most significant in terms of information transmission. Moreover, in
Section 3.5, we use a global centrality measure by averaging nodes’
information centrality $c_{I}(G)=\frac{1}{n}\sum_{i}c_{I}(i).$

###  2.3 A toy example

In this section, by means of a simple example, we show the appropriateness of
the proposed centrality measures in capturing the role of each node in the
transmission of information within the network. The network $G$ is plotted in
Figure 1(a) whose weighted adjacency matrix is:

(a) Network $G$ with $\varepsilon^{w}(G)=0.24$.

(b) Network $G^{\prime}$ with $\varepsilon^{w}(G^{\prime})=0.04$.

Figure 1: Networks $G$ and $G^{\prime}$ representation.

$\mathbf{W}=\begin{bmatrix}0.0&0.5&0.2&0.3&0.4\\\ 0.5&0.0&0.1&0.2&0.0\\\
0.2&0.1&0.0&0.0&0.0\\\ 0.3&0.2&0.0&0.0&0.0\\\ 0.4&0.0&0.0&0.0&0.0\\\
\end{bmatrix}.$ (7)

We first compute the centrality measures degree and closeness; then the
information centrality of $G$ by using Formula (6). Notice that the closeness
has been computed applying Formula (3), which is based on the reciprocal of
the link’s weight.
Table 1 reports node rankings based on the three centrality measures. We
observe that the node $a$ is the one with the highest information centrality,
highlighting its strong presence in paths connecting nodes and, therefore,
confirming its central role in spreading information in the network.
Consider in the network $G$ the four weighted shortest paths connecting nodes
$d$ and $c$ (i.e., $d-a-c,d-b-c,d-a-b-c,d-b-a-c$). Among these paths, the
shortest one, i.e. the one with the smallest cost, is $b-a-c$ with
$d^{w}(d,c)=8.33$. We notice that the node $a$ is present in three out of four
paths connecting $d$ and $c$, in line with its central role in the network.
After the node $a$’s removal (graph $G^{\prime}$ in Figure 1(b)), there exists
only one path connecting $d$ and $c$ with a cost of $15$. This result confirms
that the higher the information centrality of the deactivated node, the higher
the cost of information transmission in the network. Table 2 reports all the
weighted paths with their costs both in $G$ (Panel A) and $G^{\prime}$ (Panel
B).
Similar considerations can be done by deactivating one node of $G$ per time
and the impact in terms of graph efficiency is shown in Table 3. The results
confirm the predominant role of node $a$ in the network: indeed, the deepest
drop in the network efficiency is observed when the node $a$ is deactivated,
while the less deep efficiency is associated with the deactivation of the node
$e$.

Ranking | $d_{i}$ | Ranking | $c^{w}(i)$ | Ranking | $c_{I}(i)$
---|---|---|---|---|---
$a$ | 1.00 | $a$ | 0.28 | $a$ | 0.85
$b$ | 0.75 | $b$ | 0.19 | $b$ | 0.45
$c$ | 0.50 | $e$ | 0.17 | $e$ | 0.39
$d$ | 0.50 | $d$ | 0.16 | $d$ | 0.33
$e$ | 0.25 | $c$ | 0.13 | $c$ | 0.25
Table 1: Node rankings based on degree, closeness, and information
centralities of the network $G$ depicted in Figure 1(a). Panel A: shortest
path in $G$
---
$i$ | $j$ | Shortest path | $d^{w}(i,j)$
$b$ | $a$ | $b-a$ | 2.00
$e$ | $a$ | $e-a$ | 2.50
$d$ | $a$ | $d-a$ | 3.33
$c$ | $a$ | $c-a$ | 5.00
$e$ | $b$ | $e-a-b$ | 4.50
$d$ | $b$ | $d-b$ | 5.00
$e$ | $d$ | $e-a-d$ | 5.83
$c$ | $b$ | $c-a-b$ | 7.00
$e$ | $c$ | $e-a-c$ | 7.50
$d$ | $c$ | $d-a-c$ | 8.33
Panel B: shortest paths in $G^{\prime}$, i.e. when node $a$ is deactivated
$i$ | $j$ | Shortest path | $d^{w}(i,j)$
$d$ | $b$ | $d-b$ | 5.00
$c$ | $b$ | $c-b$ | 10.00
$c$ | $d$ | $c-b-d$ | 15.00
Table 2: Shortest paths before and after eliminating all the edges incident to node $a$. Deactivated node | $e^{w}(G^{\prime})$
---|---
$a$ | $0.04$
$b$ | $0.13$
$c$ | $0.18$
$d$ | $0.16$
$e$ | $0.15$
Table 3: Graph efficiency after disconnecting a node.

##  3 Results

###  3.1 Data

This study uses cryptocurrency trading price data spanning from October
$16^{th}$ to November $16^{th}$, 2022, gathered from FTX,333https://ftx.com/
the bankrupted exchange of reference, and Binance,444https://www.binance.com/
the major exchange in cryptocurrency markets. However, data from FTX were only
available up until November $12^{th}$, 03:28 a.m., due to operational
disruptions. Proprietary data were collected from
Refinitiv,555https://www.lseg.com/en a London Stock Exchange Group (LSEG)
business, and sourced from the DataScope Select
database.666https://www.lseg.com/en/data-analytics/products/datascope-select-
data-delivery

Figure 2: Normalized price series of major cryptocurrencies traded on FTX.
Event (c) corresponds to the FTX’s disruptions.

The initial dataset comprises a total of 442 cryptocurrencies on FTX and 344
cryptocurrencies on Binance. In order to maintain focus on purely
cryptocurrency movements and exclude external financial influences, several
categories of assets were removed from the initial dataset. Specifically, we
excluded leveraged tokens (bear and bull), tokens with partial exposure
(half), and hedging options (hedge), as well as all fiat currencies, which are
not the focus of our study. In addition, to avoid repetition and high
correlations between similar crypto-assets, we excluded the cryptocurrency
pairs against Tether (USDT) or other digital assets and focused exclusively on
trading pairs against the US dollar (USD), being the most liquid and used
instrument.777For Binance, instead, we focus exclusively on trading pairs
against the USDT as the exchange does not allow customers to trade against the
fiat currency USD. After these exclusions, the dataset was consolidated into
195 cryptocurrencies traded on FTX and 324 cryptocurrencies traded on
Binance.888The full lists of the cryptocurrencies included in this study are
available in the appendix Nonetheless, as in [15], the dataset collected from
Binance is used as an overall market comparison only, and the focus of this
study remains on the actual bankrupted exchange, FTX. The full list of the
cryptocurrencies analyzed is in B.

For high-frequency analytical granularity, in the empirical analysis we
processed the data to include 1-minute logarithmic returns for each of the
remaining cryptocurrencies, computed as
$r_{i,t}=\ln\large(\frac{P_{i,t}}{P_{i,t-1}}\large)$ where $P_{i,t}$ is the
price of the cryptocurrency $i$ at time $t$ and $P_{i,t-1}$ is the price of
the cryptocurrency $i$ at time $t-1$. This high-frequency data allows for a
detailed analysis of price dynamics and volatility within the specified period
across both trading platforms.

Date | Event |  Description
---|---|---
2022-11-02 14:44 | (a) |  Alameda Research’s (FTX’s sister) balance sheet is possibly compromised and predominantly consists of FTX’s native exchange token, FTT, as reported by CoinDesk.
2022-11-05 12:51 | (b) |  Binance’s CEO, Changpeng Zhao (CZ), tweets “Crypto is high risk”
2022-11-05 14:09 | (c) |  Binance moves $585 millions of FTT
2022-11-06 05:59 | (d) |  Tweet speculating on proof of FTX insolvency
2022-11-06 14:32 | (e) |  Alameda Reserach’s CEO, Caroline Ellison (CE), tweets against insolvency, which was retweeted by FTX’s CEO, Sam Bankman-Fried (SBF)
2022-11-06 15:47 | (f) |  CZ’s tweets on Binance liquidating any remaining FTT
2022-11-06 22:49 | (g) |  CZ’s tweet that confirms Binance’s position against FTX on the rumored acquisition
2022-11-07 xx:xx | none |  SBF’s tweet that was later deleted against Binance’s supposedly false rumors and defending FTX and its assets
2022-11-08 10:37 | (h) |  The last outgoing transaction from FTX on the Ethereum blockchain (i.e., FTX’s halt of funds withdrawals)
2022-11-09 21:00 | (i) |  Binance drops out from FTX acquisition
2022-11-10 19:08 | (j) |  FTX tweets on reopening some withdrawals for Bahamian customers only
2022-11-11 00:00 | (k) |  FTT token collapsed entirely
2022-11-11 xx:xx | none |  FTX filed for Chapter 11 (bankruptcy)
2022-11-12 03:28 | (l) |  FTX’s close of business (availability of data from the FTX market)
Table 4: Timeline of events (UTC time) as in [15].

The visual representations provided in Figure 2 effectively capture the
normalized price trajectories of major cryptocurrencies, including Bitcoin
(BTC), Ethereum (ETH), Polkadot (DOT), Solana (SOL), Avalanche (AVAX), and the
FTX Token (FTT). These plots are particularly illustrative of the market
dynamics in response to the unfolding events during the bankruptcy timeline,
as reported in Table 4. The price data reveal significant volatility,
especially visible in the sharp decline of FTT’s value, which directly
correlates with the escalated phases of the FTX crisis. The graphical
depictions allow for a clear observation of the cascading effects on other
cryptocurrencies, underscoring the interconnectedness of the crypto market.

Finally, based on the observation of the price series, we divide the sample
into two periods: one preceding the collapse of the FTX’s token, FTT, which
arose from the main exchange competitor, Binance, moving a large amount of the
token (Event (c) in Table 4); and one during the collapse period after that
event.

###  3.2 Network construction

We construct the network as follows. Firstly, we consider $n=195$
cryptocurrencies and we construct the correlation matrix $\mathbf{C}$ in which
the element $c_{ij}$ is the Pearson correlation coefficient between
cryptocurrencies $i$ and $j$ returns time series. The correlation matrix
$\mathbf{C}$ is a full matrix, then its associated weighted graph is a
complete $K_{n}$ graph containing loops. Moreover, the entries can be either
positive or negative, as $c_{ij}\in[-1,1]$. According to the literature ([42,
43]), to overcome these issues we consider the absolute value of the entries,
focusing only on the intensity of the assets co-movements, and we set the
diagonal elements of $\mathbf{C}$ to zero. We obtain a new non-negative matrix
$\tilde{\mathbf{C}}$ such that the associated complete graph becomes simple.
At this stage, we need to filter the information in $\tilde{\mathbf{C}}$ to
unveil the hidden relevant structure. Therefore, we filter the correlation
matrix by constructing the Triangulated Maximally Filtered Graph (TMFG)
proposed in [9] which is a computationally efficient algorithm to construct
the PMFG (see Sect. 2.1).

###  3.3 Descriptive statistics

In this study, we focus on the major cryptocurrencies (BTC, DOT, ETH, FTT,
AVAX, SOL) showing the highest logarithmic returns correlations in the FTX
market during the period preceding the collapse of FTT (Event (c) in Table 4),
which caused the bankruptcy of the FTX exchange, given the fraudulent
behaviors with the sister trading company Alameda Research. We analyse market
conditions in three distinct phases: pre-collapse, during the collapse, and
over the entire observed period. Table 5 shows the top 20 correlations between
the cryptocurrency returns analyzed in the FTX market, highlighting
significant variations across the different periods examined. The major
cryptocurrencies we refer to are those at the top 5 positions in the first
column of Table 5.

In the pre-collapse phase, higher correlations were observed among major
cryptocurrencies, such as a correlation of 0.80 between ETH and BTC. This
indicates a closely knit market where the movements of these major currencies
were largely synchronous. However, during the collapse, the correlation
figures decreased substantially, such as between ETH and BTC dropping to 0.40,
reflecting a market dislocation where individual cryptocurrencies responded
differently to the crisis. Even stronger evidence is reported for the
correlation between SOL and BTC, going from a level of 0.69 in the pre-event
to 0.26 within the collapse period, and other cryptocurrency trading pairs.
This pattern suggests that the market structure was significantly altered by
the event, affecting how asset prices moved in relation to each other.

| Pre-collapse |  | Collapse |  | Full period
---|---|---|---|---|---
1 | ETH | BTC | 0.80 |  | ETH | BTC | 0.40 |  | ETH | BTC | 0.47
2 | SOL | BTC | 0.69 |  | DOGE | BTC | 0.36 |  | BTC | AVAX | 0.37
3 | SOL | ETH | 0.68 |  | XRP | BTC | 0.33 |  | XRP | BTC | 0.37
4 | FTT | BTC | 0.66 |  | DOGE | AVAX | 0.32 |  | DOGE | BTC | 0.33
5 | SOL | AVAX | 0.65 |  | BTC | AVAX | 0.31 |  | DOGE | AVAX | 0.31
6 | FTT | ETH | 0.64 |  | DOGE | CHZ | 0.29 |  | ETH | AVAX | 0.30
7 | SOL | DOT | 0.63 |  | XRP | DOGE | 0.27 |  | DOT | BTC | 0.30
8 | SHIB | KSHIB | 0.63 |  | ETH | DOGE | 0.27 |  | DOT | AVAX | 0.29
9 | BTC | AVAX | 0.61 |  | SOL | BTC | 0.26 |  | SOL | BTC | 0.29
10 | DOT | AVAX | 0.61 |  | SOL | DOGE | 0.25 |  | XRP | ETH | 0.29
11 | DOT | BTC | 0.61 |  | CHZ | AVAX | 0.25 |  | CHZ | AVAX | 0.28
12 | ETH | AVAX | 0.60 |  | SOL | AVAX | 0.25 |  | SOL | AVAX | 0.28
13 | ETH | DOT | 0.59 |  | CHZ | BTC | 0.25 |  | LTC | BTC | 0.28
14 | ETH | BNB | 0.59 |  | ETH | AVAX | 0.24 |  | BTC | BCH | 0.28
15 | BTC | BNB | 0.58 |  | XRP | ETH | 0.24 |  | CHZ | BTC | 0.28
16 | SOL | LINK | 0.58 |  | XRP | CHZ | 0.23 |  | XRP | AVAX | 0.27
17 | LINK | DOT | 0.58 |  | LTC | BTC | 0.23 |  | ETH | DOGE | 0.27
18 | LINK | BTC | 0.56 |  | MYC | ENS | 0.23 |  | SOL | ETH | 0.26
19 | LINK | AVAX | 0.56 |  | DOT | BTC | 0.23 |  | SHIB | DOGE | 0.26
20 | LINK | ETH | 0.55 |  | XRP | SOL | 0.23 |  | DOGE | CHZ | 0.26
Table 5: Top 20 correlations between returns of cryptocurrencies traded on
FTX.

The summary statistics presented in Table 6 delineate the market conditions in
the three periods.In the pre-collapse phase, the cryptocurrencies exhibited
relatively low volatility and modest positive mean returns, except for FTT
which showed a minimum negative return of -3.8596, indicating early signs of
distress specific to the FTX token. The collapse phase presents a stark
contrast, with all cryptocurrencies experiencing increased volatility and
negative mean returns, highlighted by FTT’s dramatic mean return of $-0.0277$
and an exceptionally high standard deviation of $1.5709$, which is around 22
times the volatility observed in the pre-collapse phase. This period clearly
reflects the acute market stress and investor panic triggered by the unfolding
crisis. The full period combines these diverse phases, showing generally
subdued mean returns and heightened volatility, which underscores the long-
term impact of the crisis on market dynamics.

| AVAX | BTC | DOT | ETH | FTT | SOL | Market
---|---|---|---|---|---|---|---
Panel A: pre-collapse
mean | 0.0008 | 0.0004 | 0.0005 | 0.0009 | 0.0003 | 0.0006 | 0.0004
std | 0.1023 | 0.0528 | 0.0855 | 0.0845 | 0.0724 | 0.0986 | 0.2598
min | -1.2175 | -0.7287 | -1.0151 | -0.9198 | -3.8596 | -1.1457 | -44.9316
Q1 | -0.0445 | -0.0204 | -0.0326 | -0.0365 | -0.0261 | -0.0480 | 0.0000
median | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000
Q3 | 0.0446 | 0.0205 | 0.0327 | 0.0369 | 0.0264 | 0.0483 | 0.0000
max | 2.4216 | 1.6502 | 2.4874 | 1.9938 | 1.3012 | 2.1189 | 57.1662
Panel B: collapse
mean | -0.0040 | -0.0026 | -0.0022 | -0.0018 | -0.0277 | -0.0071 | -0.0052
std | 0.3326 | 0.1964 | 0.3081 | 0.3734 | 1.5709 | 0.7396 | 1.7276
min | -8.1488 | -2.7816 | -9.1398 | -6.8753 | -29.9622 | -12.7876 | -172.0257
Q1 | -0.0811 | -0.0389 | -0.0721 | -0.0669 | -0.2097 | -0.1470 | 0.0000
median | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000
Q3 | 0.0774 | 0.0353 | 0.0707 | 0.0599 | 0.1771 | 0.1331 | 0.0000
max | 6.3315 | 4.5145 | 5.4806 | 10.3006 | 32.3448 | 9.8197 | 299.0720
Panel C: full period
mean | -0.0004 | -0.0003 | -0.0001 | 0.0002 | -0.0063 | -0.0013 | -0.0009
std | 0.1851 | 0.1063 | 0.1677 | 0.1964 | 0.7684 | 0.3706 | 0.8721
min | -8.1488 | -2.7816 | -9.1398 | -6.8753 | -29.9622 | -12.7876 | -172.0257
Q1 | -0.0494 | -0.0239 | -0.0344 | -0.0386 | -0.0359 | -0.0566 | 0.0000
median | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000 | 0.0000
Q3 | 0.0490 | 0.0235 | 0.0348 | 0.0383 | 0.0347 | 0.0560 | 0.0000
max | 6.3315 | 4.5145 | 5.4806 | 10.3006 | 32.3448 | 9.8197 | 299.0720
Table 6: Summary statistics of percentage log-returns of the 6 main
cryptocurrencies traded on FTX.

###  3.4 The network structure surrounding the collapse of the FTT node

To highlight changes in topological properties in the FTX market due to FTT’s
collapse, we construct two networks: one preceding the FTT’s collapse and one
during the collapse period after that event. Figure 3 shows the filtered
networks of the FTX market in both periods pre and post event. We highlighted
with different colors the nodes corresponding to the major cryptocurrencies
that resulted in the highest correlations. However, the main topological
changes cannot be detected by a simple visual network inspection. The analysis
of the centrality measures computed for the cryptocurrency network
particularly focusing on the FTX platform, reveals significant insights into
the structural dynamics and influence patterns across the sample period.
Analyzing the normalized centrality measures — degree, closeness, and
information centrality — provides an understanding of the node’s importance,
both prior to and following the market collapse.

Figure 3: Maximal Filtered Planar Graph of the 195 cryptocurrencies from FTX
market in the periods pre-collapse (top) and collapse (bottom). Colored nodes
highlight the cryptocurrencies analysed.

Table 7 presents the ranking of centrality measures of the top 20
cryptocurrencies in the FTX market, divided into the pre-collapse (Panel A)
and collapse (Panel B) periods. We first observe that the centrality measures
are close to zero, due to the network sparsity. However, the centrality
measures well reflect the results of Table 5 revealing the most informative
cryptocurrencies in the FTX market analysis. In the period preceding the FTT
collapse (Panel A), AVAX and DOT emerged prominently across the centrality
measures. AVAX, the token of Avalanche, is one of the most promising emerging
proof-of-stake (PoS) blockchain projects and perhaps the one that is followed
with the greatest attention by developers and cryptocurrency investment
experts. It shows a notable number of direct connections (normalized degree
score $0.20103$) compared to other cryptocurrencies, suggesting a pivotal role
in the network’s structure. Similarly, its closeness score ($0.00102$)
underscores its centrality within the FTX network, being the most followed
among cryptocurrencies as per its shortest distances from all the other nodes.
However, in terms of information centrality, DOT takes precedence with a score
of $0.06773$, reflecting its critical role in sustaining network efficiency in
terms of rapid communication between nodes despite potential disruptions.
Arguably, DOT is the native token of the Polkadot blockchain, which is
considered among the most ambitious and innovative projects in the
cryptocurrency arena, aiming to establish a decentralized web that enables
various blockchains to interact and collaborate seamlessly.

BTC, consistently recognized for its market dominance, maintained notable
centrality, reflective of its enduring influence and robust integration within
the network. Interestingly, the presence of lesser-known cryptocurrencies like
NEAR, a PoS protocol based on the concept of sharding, and SOL, a competitor
of the Polkadot and Ethereum blockchains, in the top ranks across different
measures highlights their emerging significance within the network’s
architecture. ETH, the second-largest cryptocurrency in terms of market
capitalization and liquidity, follows the rank among the most central nodes of
the FTX network in terms of degree, closeness, and information centrality.
Apart from NEAR, the centrality measures in the pre-collapse period show
consistent results to the correlations ranking of Table 5 in Section 3.3.

Centrality Measures Degree Closeness Information Centrality Panel A: pre-
collapse 1 AVAX 0.20103 AVAX 0.00102 DOT 0.06773 2 DOT 0.19588 DOT 0.00101
AVAX 0.06511 3 BTC 0.18041 BTC 0.00100 BTC 0.04789 4 NEAR 0.15979 SOL 0.00100
SOL 0.03962 5 SOL 0.15979 ETH 0.00099 NEAR 0.03362 6 OMG 0.14433 NEAR 0.00099
ETH 0.03283 7 KNC 0.12371 BCH 0.00098 RAY 0.02847 8 ETH 0.11856 KNC 0.00098
TRX 0.02816 9 RAY 0.09794 AAVE 0.00098 OMG 0.02761 10 1INCH 0.08763 LINK
0.00098 AAVE 0.02690 11 CHR 0.07732 1INCH 0.00097 1INCH 0.02665 12 TOMO
0.07216 RAY 0.00097 KNC 0.02566 13 AAVE 0.06701 BNB 0.00097 LINK 0.02549 14
LINK 0.06186 MATIC 0.00097 BCH 0.02511 15 FTT 0.05670 SRM 0.00096 SNX 0.02372
16 CRV 0.05670 FTT 0.00096 BNB 0.02364 17 TRX 0.05670 LTC 0.00096 FTT 0.02355
18 CRO 0.05670 UNI 0.00096 MATIC 0.02300 19 STETH 0.05670 ATOM 0.00096 SRM
0.02280 20 STSOL 0.05155 OMG 0.00096 UNI 0.02272 Panel B: collapse 1 DOGE
0.21134 DOGE 0.00276 DOGE 0.05907 2 CHZ 0.17010 BTC 0.00274 BTC 0.05406 3 BTC
0.14433 AVAX 0.00266 XRP 0.04525 4 AVAX 0.14433 CHZ 0.00264 AVAX 0.04434 5 DOT
0.13402 XRP 0.00258 DOT 0.03845 6 XRP 0.10309 DOT 0.00258 CHZ 0.03704 7 MATIC
0.10309 ETH 0.00257 STG 0.03523 8 SOL 0.09278 SOL 0.00252 MATIC 0.03458 9 ATOM
0.08247 LTC 0.00248 ENS 0.02852 10 LTC 0.08247 RSR 0.00241 LDO 0.02840 11 GT
0.07216 BCH 0.00241 LTC 0.02569 12 STG 0.07216 LINK 0.00240 WFLOW 0.02553 13
LRC 0.06701 ATOM 0.00239 AXS 0.02402 14 CRV 0.06701 FTT 0.00237 ATOM 0.02291
15 AAVE 0.06186 MATIC 0.00235 BAND 0.02277 16 ALICE 0.05670 UNI 0.00226 IP3
0.02157 17 WFLOW 0.05670 CRV 0.00224 SOL 0.02157 18 BAND 0.05670 USDT 0.00224
C98 0.02126 19 SPELL 0.05155 BAND 0.00223 ETH 0.02076 20 IP3 0.05155 APT
0.00223 OMG 0.02061

Table 7: Ranking of the top 20 cryptocurrency in terms of degree, closeness,
and information centrality measures in the FTX market

The collapse phase delineated a stark transformation in network structure (see
Figure 3), with Dogecoin (DOGE), one of the first altcoins999i.e., alternative
coins to Bitcoin. created as a fast and instant payment system based on the
Litecoin blockchain architecture, leading in all centrality measures. Its
degree centrality (0.21134) notably increased, paired with the highest
closeness (0.00276) and information centrality (0.05907) scores, indicating a
surge in its network influence post-collapse. This shift might suggest a
realignment of network structures where market participants pivot towards
alternative nodes during periods of instability. Ripple (XRP) and Litecoin
(LTC), absent in the centrality ranking of the pre-collapse period, also
gained more importance in terms of network centrality and information
dissemination during the collapse period, a signal of investors’ reliance on
well-established cryptocurrencies. Indeed, BTC maintained its predominant
position, albeit with reduced scores compared to DOGE, signaling its
resilience and foundational role within the network even amidst market
upheavals. Other cryptocurrencies like AVAX and DOT also remained influential,
though with adjusted rankings, reflecting a reconfiguration of inter-node
relations and dependencies.

Before the collapse of the FTX market, FTT was solidly positioned within the
top 20 in degree centrality, signifying a robust number of direct connections,
i.e., strong correlations within a high number of cryptocurrencies, and an
important role within the network’s structure. This initial placement
highlighted its prominence and influence among investors and traders on the
platform. However, after the collapse of FTX market, we observe that FTT
experienced a significant reduction in its degree centrality, indicating a
loss of its direct connections and, therefore, high correlations with other
cryptocurrencies within the network. Another interesting result emerges from
the ranking of the closeness. Indeed, results in Table 7 show that the
collapse of FTT has led to an increase in closeness centralities as a
consequence of the average decrease of the geodesic distances between the
nodes of the network. This shift suggests a decrease in its active integration
and participation within the trading environment, likely due to diminished
investor confidence and a restructuring of the network’s dynamics. Despite
this decline in degree and information centralities, FTT is still present in
the closeness centrality’s ranking. This result derives from the fact that FTT
keeps few connections in the network and its neighbors are central in terms of
degree, closeness and information centrality.101010The FTT’s neighbors after
its collapse are: BTC, SOL, and DOGE This could be economically interpreted as
a sign of investors’ need to readjust their positions during times of crisis,
reflecting a scenario of ongoing adjustments and liquidations by investors
trying to navigate the market’s volatility. Interestingly, the information
centrality for FTT post-collapse suggests an absence of significant
information flow through the FTX’s token, underscoring a scenario where
investors were likely in a rush to disassociate from the token amid its
collapse. This lack of flowing information indicates that while FTT retained
some network closeness—perhaps due to its previous importance and lingering
transactions—the effective communication and utility of FTT within the network
had substantially diminished, mirroring the broader crisis impacts on its
value and operational significance.

###  3.5 Pattern of information flow surrounding the collapse of the FTT node

To deepen the analysis of the previous Sections, we capture the evolving
patterns in the FTX network by means of rolling windows. In particular, we
construct daily rolling window networks as in Sect. 3.2 with 24-hour windows
at intervals of one hour. Based on the results of the previous Sections, we
focus on the closeness and the information centrality as the most informative
centrality measures. We plot in Figure 4 the daily rolling closeness
centrality measure111111The normalized version is represented by Figure 9 in
A) over time for the major cryptocurrencies of the FTX network. This measure
is crucial for understanding how well connected (in terms of reachability) a
node is, not only through direct links but with respect to the entire
network’s structure. Figure 4 illustrates that Bitcoin (BTC) consistently
maintained the highest score, indicative of its central role in the network.

The prominence of BTC throughout the period illustrates its foundational
status within the cryptocurrency network, maintaining efficient information
dissemination capabilities despite market upheavals. Overall, the average
closeness centrality (pink line) of the FTX network saw a slight increase
after the announcement of Binance dropping out from the acquisition deal
(Event (i)). This signals a reduction of the distance between pairs of nodes
and therefore, an improvement in the reachability121212In graph theory, the
reachability refers to the ability of a node $i$ to reach all the other nodes
in the network. between cryptocurrencies after the disruption of the FTT. Each
remaining colors correspond to the other main cryptocurrencies analyzed, while
the three ranges comprise the percentiles as described in the Figure legend.

Figure 4: Rolling closeness centrality on FTX network in the entire sample
period (top) and the zoomed-in collapse period (bottom).

As previously discussed, specific events had pronounced impacts on the
centrality measures of other cryptocurrencies, especially FTT. The revelation
of compromised balance sheet of Alameda Research, containing significant
amounts of FTT, precipitated an immediate reaction: it can be easily noticed
by the drop in FTT’s centrality on November $2^{nd}$. This initial impact was
compounded by subsequent market movements and public statements from key
market players. The series of events from November $5^{th}$ through $6^{th}$,
involving the large-scale movement of FTT by Binance and Binance’s CEO’s
public warnings, sparked notable fluctuations in FTT’s centrality. This is
even more evident in Figure 9 of A when normalizing by the average centrality
of the entire FTX network. These changes underscored the market’s sensitivity
to news and actions by major stakeholders, reflecting real-time adjustments
within the network’s structure.

The heightening uncertainty following conflicting public statements about the
insolvency of FTX and Alameda Research and the dramatic fallout from the
failed Binance acquisition deal further influenced the network dynamics.
Closeness centrality values for all the cryptocurrencies analyzed,
particularly after Event (i) in Table 4, displayed erratic movements that
mirrored the tumultuous information environment. The operational disruptions
at FTX, highlighted by the halting and partial reopening of withdrawals,
followed by the collapse of the FTT token, were critical as well. These events
led to a pronounced and sustained decline in FTT’s closeness centrality, but
also to a major centrality volatility in other major cryptocurrencies within
the network. This decline was stark against the backdrop of FTX filing for
bankruptcy and closing operations, where FTT’s centrality reached its nadir by
November $11^{th}$, showing its reduced role in the network.

Figure 5: Rolling information centrality on FTX network in the entire sample
period (top) and the zoomed-in collapse period (bottom).

Figure 5 shows, instead, the patterns of nodes’ information centrality
measure. Information centrality, a measure of the efficiency of information
flow through the shortest paths in the network, showcases distinct patterns in
response to the events leading to the collapse of FTT. The plot shows spikes
in DOT’s information centrality around the time when concerns about Alameda
Research’s balance sheet surfaced (Events (a) and (d) in Table 4). This
highlights the importance of DOT within the network as a safe-haven vehicle
through which to disseminate information. The same happens to AVAX, which saw
major spikes throughout the FTT collapse period as well. On the other hand,
this could also indicate temporary scrutiny of FTT within the network. The
reason can likely be due to increased transactions and information flow as
market participants assessed the implications of the news. Perhaps
cryptocurrencies like DOT and AVAX are likely traded less for their
complexities than more popular ones, such as BTC and ETH, but they still
represent central points for network structure, suggesting the importance of
more stable protocols over crypto-assets popularity driven by less
sophisticated investments.

Figure 6: Average rolling information centrality on FTX network in the entire
sample period (top) and the zoomed-in collapse period (bottom).

Throughout this turbulent period, other cryptocurrencies such as BTC, ETH, and
SOL also experienced shifts in their information centrality measures, though
less dramatically than FTT. This pattern underscores the broader impact of the
crisis, affecting the entire cryptocurrency market as participants adjusted
their strategies in response to the unfolding events. Finally, in Figure 6 we
plot the average information centrality measure of the entire FTX network
$c_{I}(G)$, showing the overall trend of the information flow during the
sample period. As it is readily apparent, there is a structural break after
the Event (h) in Table 4, showing that the impossibility of withdrawing
digital assets from the exchange led investors to cautiously divest and re-
allocate their positions in the face of the crisis, a result consistent with
the findings in [15]. While the weeks before the first announcement and the
period during the collapse of FTX’s native token show a stable flow of
information within the network, there was substantial informed trading after
the exchange halted funds withdrawals to avoid a “bank run”.

The observed centrality trends from the figures provide detailed insights into
how information is disseminated and processed within the cryptocurrency
network in response to crisis events. These dynamics are indicative of the
market’s reactive nature and the critical role of information centrality in
understanding the mechanisms of crisis management and response in
decentralized financial systems. It is interesting to see how well the
centrality measures used in this study reflect the market behaviors
surrounding each of the events considered, signaling the appropriateness of
the methodology employed for the proposed investigation.

###  3.6 The market responses in the Binance network

Consistent with the market comparison in [15] and the dominance results of
[27], we lastly explore the centrality measures across major cryptocurrencies
within the Binance network during the FTX crisis. In particular, similarly to
the previous section, we construct daily rolling window networks at intervals
of one hour. Each network has $n=324$ nodes and it is constructed as in Sect.
3.2. Our aim is to provide a better overall understanding of the decentralized
financial system dynamics that underpin the distribution and reception of
information in times of financial upheaval. Given Binance’s significant market
presence, this analysis serves as a proxy for broader market behaviors and
offers insights into how cryptocurrencies interact and respond within one of
the largest trading environments.

Figure 7: Rolling closeness centrality on Binance network in the entire sample
period (top) and the zoomed-in collapse period (bottom).

Figure 7 (as well as its normalized version in Figure 10 in A) clearly depicts
the shifts in closeness centrality that occurred in response to the pivotal
events mentioned above. Throughout the observed period, BTC demonstrates
relatively high and stable closeness centrality, indicative of its central
role in the cryptocurrency market. This stability reflects Bitcoin’s broad
acceptance within the whole market as a central node through which information
and transactions are disseminated. Besides, we notice the resilience of the
overall Binance network shown after the Event (h), as all the closeness
reversed and started to increase again. This is consistent with the findings
of [15] documenting an improvement of Binance market quality upon the halt of
funds withdrawal on FTX.

Particularly for FTT, the figures show a distinct pattern in closeness
centrality, peaking around critical events before exhibiting a dramatic fall
as the crisis escalated. This trend is even more visible on the Binance
network, within which the FTT closeness is downward below the overall
closeness average of the network. This is crucial as it reflects how such
crises can influence related cryptocurrencies within a large trading platform
like Binance, which is supposedly not connected with the bankrupted FTX
exchange. Contrastingly, most popular cryptocurrencies, like BTC and ETH,
displayed more stable closeness centrality during the crisis, suggesting a
retained level of trust and stability despite the surrounding turmoil.

While FTT shows significant centrality spikes (before the first announcement)
and subsequent declines (during the collapse), other cryptocurrencies like
AVAX and SOL display varying levels of centrality changes. These variations
could be indicative of differing levels of exposure to systemic shocks.
Additionally, DOT shows similar patterns albeit seemingly less volatile in the
Binance network compared to FTX. Before the onset of the crisis, the closeness
centrality of all tracked cryptocurrencies exhibits a somewhat synchronized
pattern, suggesting a tightly interlinked market structure. As the crisis
unfolds, the divergence in centrality measures becomes more pronounced,
underscoring a shift in how these cryptocurrencies interact and influence each
other within the network.

Figure 8: Rolling information centrality on Binance network.

Figure 8 reveals noticeable fluctuations in information centrality as the FTX
crisis unfolded. Interestingly, as in Figure 5, FTT exhibited sharp centrality
peaks coinciding with a supposedly market event that anticipated the crisis.
The same happens for ETH, showing what can arguably be described as insider
trading, or more generally, an anticipation of information leading to the
overall crisis.131313ETH is arguably the most central node within the Binance
network in terms of the measures considered. Its spike, just before the first
public announcement of FTX’s crisis, might be seen as a signal of the
information spreading through Binance to the overall market.

ETH and AVAX had the highest information centrality on Binance during the
sample period, particularly after Event (a). Such spikes in centrality reflect
their role in information spreading due to a surge in transaction activities
associated with these tokens. Overall, the patterns of the other major
cryptocurrencies do not show significant changes, highlighting their non-
primary role in spreading information. This emphasizes the role of Binance’s
network in mediating these dynamics even though the crisis incurred on another
exchange.

##  4 Conclusions

This study examined the anatomy of information flow in the bankrupted FTX
cryptocurrency exchange by underscoring the distinct role of centrality
measures in capturing the critical dynamics within financial networks during
periods of market instability. Through an exploration of degree, closeness,
and information centrality, our findings reveal how the market structure and
the efficacy of information dissemination within such networks evolve in
response to substantial financial disruptions. In this study, we also
elucidate the dynamic nature of centrality within the cryptocurrency markets
more broadly, influenced by both internal network adjustments and external
market conditions.

Our analysis illustrates that path-based centrality measures in financial
networks not only offer insights into the immediate impacts of such crises but
also highlight the broader structural vulnerabilities and resilience within
the cryptocurrency market. The rapid shifts in centrality metrics observed in
response to the unfolding crisis at FTX reflect the market’s sensitivity to
both internal network changes and external economic pressures. This study
exposes the interconnected nature of cryptocurrency markets, where the failure
of a single node such as FTT can lead to widespread repercussions across
different networks, affecting investor behavior and market stability.
Importantly, the resilience of certain cryptocurrencies like Bitcoin, which
maintained high centrality throughout the crisis, contrasts sharply with the
volatility in centrality measures of other tokens directly implicated in the
crisis, like FTT. This disparity underscores the importance of robust network
positions and the dangers of over-reliance on individual market players within
decentralized financial systems.

Finally, the insights derived from this research have significant implications
for stakeholders across the financial spectrum—from investors and analysts to
regulators and policymakers. Understanding the centrality dynamics within such
networks can aid in developing more resilient financial structures and inform
strategic decisions aimed at mitigating risks associated with market
centralization and the cascading effects of financial crises. This study,
therefore, not only contributes to the academic debate on complex networks and
financial markets but also provides practical frameworks for assessing and
enhancing the stability of the cryptocurrency ecosystem.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or
personal relationships that could have appeared to influence the work reported
in this paper.

## Acknowledgement

Luca Galati thanks Refinitiv, an LSEG business, which provided him with access
to data and technical assistance.
Riccardo De Blasis is member of the Gruppo Nazionale Calcolo Scientifico-
Istituto Nazionale di Alta Matematica (GNCS-INdAM).
Rosanna Grassi acknowledges financial support from the European Union –
NextGenerationEU. Project PRIN 2022 “Networks: decomposition, clustering and
community detection” code: 2022NAZ0365 - CUP H53D23002510006. Rosanna Grassi
is a member of GNAMPA-INdAM.
Giorgio Rizzini gratefully acknowledges financial support from “SoBigData.it”,
which receives funding from the European Union – NextGenerationEU – PNRR –
Project: “SoBigData.it – Strengthening the Italian RI for Social Mining and
Big Data Analytics” – Prot. IR0000013 – Avviso n. 3264 del 28/12/2021. Giorgio
Rizzini acknowledges partial support by the European Program scheme
“INFRAIA-01-2018-2019: Research and Innovation action”, grant agreement n.
871042 “SoBigData++: European Integrated Infrastructure for Social Mining and
Big Data Analytics”.

##  Appendix A Additional figures

Figure 9: Rolling normalized closeness centrality on FTX market in the entire
sample period (top) and the zoomed-in collapse period (bottom).

Figure 10: Rolling normalized closeness centrality on Binance market in the
entire sample period (top) and the zoomed-in collapse period (bottom).

##  Appendix B List of cryptocurrencies

1INCH | CHZ | GOG | MPLX | SNX
---|---|---|---|---
AAVE | CITY | GRT | MSOL | SNY
AGLD | CLV | GST | MTA | SOL
AKRO | COMP | GT | MTL | SPA
ALCX | CONV | HGET | MYC | SPELL
ALEPH | COPE | HMT | NEAR | SRM
ALGO | CQT | HNT | NEXO | STARS
ALICE | CREAM | HOLY | OKB | STEP
ALPHA | CRO | HT | OMG | STETH
AMPL | CRV | HUM | OPENDAOSOS | STG
APE | CVC | HXRO | ORBS | STMX
APT | CVX | IMX | ORCA | STORJ
ASD | DAI | INDI | OXY | STSOL
ATLAS | DAWN | INTER | PAXG | SUN
ATOM | DENT | IP3 | PEOPLE | SUSHI
AUDIO | DFL | JOE | PERP | SWEAT
AURY | DMG | JST | POLIS | SXP
AVAX | DODO | KIN | PORT | TLM
AXS | DOGE | KNC | PRISM | TOMO
BADGER | DOT | KSHIB | PROM | TONCOIN
BAL | DYDX | LDO | PSG | TRU
BAND | EDEN | LEO | PSY | TRX
BAO | EMB | LINA | PTU | TULIP
BAR | ENJ | LINK | PUNDIX | UBXT
BAT | ENS | LOOKS | QI | UMEE
BCH | ETH | LRC | RAY | UNI
BICO | ETHW | LTC | REALY | USDT
BIT | EURT | LUA | REEF | VGX
BLT | FIDA | MANA | REN | WAVES
BNB | FRONT | MAPS | RNDR | WAXL
BNT | FTM | MATH | ROOK | WFLOW
BOBA | FTT | MATIC | RSR | WRX
BTC | FXS | MBS | SAND | XAUT
BTT | GAL | MCB | SECO | XPLA
BVOL | GALA | MEDIA | SHIB | XRP
C2X | GARI | MER | SKL | YFI
C98 | GENE | MKR | SLND | YFII
CEL | GMT | MNGO | SLP | YGG
CHR | GODS | MOB | SLRS | ZRX

Table 8: List of the 195 cryptocurrencies (RIC codes) from the FTX market.

1INCH | BSW | ERN | KP3R | ORN | STX
---|---|---|---|---|---
AAVE | BTCST | ETC | KSM | OXT | SUN
ACA | BTC | ETHERNITY | LAZIO | PAXG | SUSHI
ACH | BTG | ETH | LDO | PEOPLE | SXP
ACM | BTS | FARM | LEVER | PERL | SYS
ADA | BTT | FET | LINA | PERP | TCT
ADX | BURGER | FIDA | LINK | PHA | TFUEL
AGLD | BUSD | FIL | LIT | PLA | THETA
AION | C98 | FIO | LOKA | PNT | TKO
AKRO | CAKE | FIRO | LPT | POLS | TLM
ALCX | CELO | FIS | LRC | POLYX | TOMO
ALGO | CELR | FLM | LSK | POND | TORN
ALICE | CFX | FLOW | LTC | PORTO | TRB
ALPACA | CHESS | FLUX | LTO | POWR | TRIBE
ALPHA | CHR | FORTH | LUNA | PSG | TROY
ALPINE | CHZ | FOR | LUNC | PUNDIX | TRU
AMP | CITY | FRONT | MANA | PYR | TRX
ANC | CKB | FTM | MASK | QI | T
ANKR | CLV | FTT | MATIC | QNT | TVK
ANT | COCOS | FUN | MBL | QTUM | TWT
APE | COMP | FXS | MBOX | QUICK | UMA
API3 | CONT | GALA | MC | RAD | UNFI
APT | COTI | GAL | MDT | RARE | UNI
ARDR | CRV | GHST | MDX | RAY | UTK
ARPA | CTK | GLMR | MFT | REEF | VET
AR | CTSI | GMT | MINA | REI | VGX
ASR | CTXC | GNO | MIOTA | REN | VIDT
ASTR | CVC | GRT | MIR | REP | VITE
ATA | CVP | GTC | MITH | REQ | VOXEL
ATM | CVX | GTO | MKR | RIF | VTHO
ATOM | DAR | HARD | MLN | RLC | WAN
AUCTION | DASH | HBAR | MOB | RNDR | WAVES
AUDIO | DATA | HIGH | MOVR | ROSE | WAXP
AUTO | DCR | HIVE | MTL | RSR | WING
AVA | DEGO | HOT | MULTI | RUNE | WIN
AVAX | DENT | ICP | NANO | RVN | WNXM
AXS | DEXE | ICX | NBS | SAND | WOO
BADGER | DF | IDEX | NBT | SANTOS | WRX
BAKE | DGB | ILV | NEAR | SCRT | WTC
BAL | DIA | IMX | NEO | SC | XEC
BAND | DNT | INJ | NEXO | SFP | XEM
BAR | DOCK | IOST | NKN | SHIB | XLM
BAT | DODO | IOTX | NMR | SKL | XMR
BCH | DOGE | IRIS | NULS | SLP | XRP
BEAM | DOT | JASMY | OCEAN | SNX | XTZ
BEL | DREP | JOE | OGN | SOL | XVG
BETA | DUSK | JST | OG | SPELL | XVS
BICO | DYDX | JUV | OMG | SRM | YFII
BIFI | EGLD | KAVA | OM | STEEM | YFI
BLZ | ELF | KDA | ONE | STG | YGG
BNB | ENJ | KEY | ONGAS | STMX | ZEC
BNT | ENS | KLAY | ONT | STORJ | ZEN
BNX | EOS | KMD | OOKI | STPT | ZIL
BOND | EPX | KNC | OP | STRAX | ZRX

Table 9: List of the 324 cryptocurrencies (RIC codes) from the Binance market.

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