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Measuring the impact of digital exchange cyberattacks on Bitcoin Returns
Abstract
We investigate the impact on Bitcoin returns arising from cyberattacks on digital exchanges over the 2012 – 2021 period. In particular, we test the hypothesis that Bitcoin experiences lower returns on the dates associated with cybersecurity breaches of cryptocurrency exchanges. We find a negative and statistically significant impact where Bitcoin price declines by about 1.513 percent on the cyberattack days. However, a subsample analysis suggests that while the estimated effect is somewhat larger over the 2012 – 2018 period, it has lessened and become statistically insignificant in the more recent 2019 – 2021 subsample.
... This body of literature is relatively nascent and diverse, studying a variety of breaches from multiple aspects of the impact using different methodologies. The literature shows that attacks on cryptocurrency exchanges lead to dropped cryptocurrency valuation (Hu et al., 2020;Lee, 2022;Milunovich & Lee, 2022), increased volatility (Lyócsa et al., 2020), and decreased trading volume (Abhishta et al., 2019). ...
... This result is consistent with the findings of several previous studies in the stock market (Arcuri et Page 3936 al., 2018;Cavusoglu et al., 2004;Colivicchi & Vignaroli, 2019;Foerderer & Schuetz, 2022;Gatzlaff & McCullough, 2010;Ko et al., 2009;Michel et al., 2020;Morse et al., 2011;Rasoulian et al., 2023;Telang & Wattal, 2007) and provides further evidence for breaches' negative price impact in the blockchain ecosystem along with Shanaev et al. (2020), Storsveen & Veliqi (2020), Hu et al. (2020), Milunovich & Lee (2022), and Lee (2022). ...
... Centralized exchanges (CEXs) have facilitated early adoption and value creation for cryptocurrencies during their nascency, through linking cryptocurrency economies to traditional fiat economies. However, challenges such as hacking [1]- [3], wash trading [4], using customer funds [5], price manipulation [6], and regulatory action by government agencies [7] due to their centralized control have become major issues in the cryptocurrency industry. For these reasons, alternatives to CEXs, namely decentralized exchanges (or, DEXs), have received considerable use and attention, as well as market traction. ...
... ALT achieves economic and computational efficiencies by mapping liquidity sources as curves and line segments onto a 2-dimensional space and then calculating the share of input tokens across liquidity sources by finding the intersections 2 DAI is a USD-pegged stablecoin. 3 XOR is the native token of the SORA network. ...
... 2019; Ma and Luan, 2022;Milunovich and Lee, 2022;Koch and Dimpfl, 2023), we consider the following control variables. One measure of market liquidity that provides 5 The related results are reported in the Appendix. ...
... These CARs findings align with Corbet et al. (2020) assertion that each cybercrime event may have a distinct personality that resonates throughout the cryptocurrency market. Our findings also generally concur with those of Milunovich and Lee (2022) who show that cyberattacks significantly impact bitcoin returns. ...
We analyse the impact of cybercrime, particularly cryptocurrency heists and scams, on dynamic conditional correlations and abnormal returns in cryptocurrency. Our high-frequency, hourly data set covers three years, from January 2020 to December 2022, and our results show that certain hacking events have a negligible negative impact on investors, especially when focusing on less popular tokens or coins. This new perspective has significant implications for the investment community. Existing literature generally assumes that the impact of cybercrime is situational and argues for increased awareness, proactive cybersecurity measures and multi-stakeholder collaboration in traditional financial markets such as equities and currencies. However, the cryptocurrency market-a relatively young and still developing area-has a unique dynamic. Less popular tokens or coins often have lower market integration and liquidity, indicating a lower impact of cybercrime. If such a token or coin already has a limited reputation or investor base, the overall negative sentiment may be further mitigated, lessening the expected negative impact.
... The starting date is aligned with the first availability of the FGI data, while the ending date corresponds to the beginning of our study. The control variables for the S&P 500 index, the CBOE volatility index, the U.S. dollar index, and gold were obtained from the CMoney database (e.g., Aalborg et al., 2019;Koch and Dimpfl, 2023;Ma and Luan, 2022;Milunovich and Lee, 2022), while the iShares 7-10 ...
Intertwined with the recent fall of cryptocurrency exchange FTX was the unveiling to investors of the lack of asset support for FTX's flagship token FTT. Given interest in reputational contagion, as well as recent academic and investor interest in digital tokens, we are motivated to take advantage of the FTX/FTT event to investigate reputational contagion to other tokens. We employ an event study approach to gauge the impact of the FTX collapse on a variety of tokens. Results of abnormal returns, as well as CARs and CAARs, indicate almost all tokens initiated a persistent bearish trend on the event day.
Presently there are over 500 cryptocurrency exchanges worldwide and more than 19,700 different cryptocurrencies. Despite the fact that cryptocurrency trading is possible via private peer-to-peer transactions, more than 90% of trading occurs on organised exchanges which provide convenience and liquidity. However, centralised cryptocurrency exchanges are regarded as high-value targets by criminals and are often victims of cyberattacks. In this paper, we investigate the risk of cryptocurrency exchange closures and develop predictive models to forecast which markets will close down and which ones will remain active using publicly available data. Our models perform well and reach a high level of classification accuracy. Exchange trading volume, the availability of public information on exchange staff, exchange lifetime, and several cybersecurity features are identified as key attributes in predicting exchange closures. Nevertheless, our models do not account for all sources of risk, e.g. potential fraud and mismanagement of client funds committed by the exchanges themselves, and market participants are encouraged to carefully consider where and how they store their digital assets.
This paper examines mean and volatility spillovers between three major cryptocurrencies (Bitcoin, Litecoin and Ethereum) and the role played by cyber-attacks. Specifically, trivariate GARCH-BEKK models are estimated which include suitably defined dummies corresponding to different types, targets and number per day of cyber-attacks. Significant dynamic linkages (interdependence) between the three cryptocurrencies under investigation are found in most cases when cyber-attacks are taken into account, Bitcoin appearing to be the dominant cryptocurrency. Further, Wald tests for parameter shifts during episodes of turbulence resulting from cyber-attacks provide evidence that the latter affect the transmission mechanism between cryptocurrency returns and volatilities (contagion). More precisely, cyber-attacks appear to strengthen cross-market linkages, thereby reducing portfolio diversification opportunities for cryptocurrency investors. Finally, the conditional correlation analysis confirms the previous findings.
Bitcoin (BTC) is a major virtual currency. Using weekly data over the 2010–2013 period, we analyze a BTC investment from the standpoint of a US investor with a diversified portfolio including both traditional assets (worldwide stocks, bonds, hard currencies) and alternative investments (commodities, hedge funds, real estate). Over the period under consideration, BTC investment had highly distinctive features, including exceptionally high average return and volatility. Its correlation with other assets was remarkably low. Spanning tests confirm that BTC investment offers significant diversification benefits. We show that the inclusion of even a small proportion of BTCs may dramatically improve the risk-return trade-off of well-diversified portfolios. Results should however be taken with caution as the data may reflect early-stage behavior that may not last in the medium or long run.
We study whether news and sentiment about bitcoin regulation, the hacking of bitcoin exchanges and scheduled macroeconomic news announcements affect the volatility of bitcoin, measured as realized variance and its jump component. Our results show that realized variance and its jump component exhibit similar dynamics and react similarly to various types of news. Volatility of Bitcion reacts most strongly to news on bitcoin regulation, positive investor sentiment regarding bitcoin regulation extracted using Google searches, and most notably, hacking attacks on cryptocurrency exchanges. Quantile regression reveals that hacking attacks have particularly strong impact on the upper conditional distribution of bitcoin volatility. We also find that the volatility of bitcoin is not influenced by most scheduled US macroeconomic news announcements, such as government budget deficits, inflation, or even monetary policy announcements. On the other hand, bitcoin responds with increased volatility to announcements of forward-looking indicators, such as the consumer confidence index.
Bitcoin is gaining traction as an alternative store of value. Its market capitalization transcends all other cryptocurrencies in the market. But its high monetary value also makes it an attractive target to cyber criminal actors. Hacking campaigns usually target the weakest points in an ecosystem. In Bitcoin, these are currently the exchange platforms. As each exchange breach potentially decreases Bitcoin's market value by billions, it is a threat not only to direct victims, but to everyone owning Bitcoin. Based on an extensive analysis of 36 breaches of Bitcoin exchanges, we show the attack patterns used to exploit Bitcoin exchange platforms using an industry standard for reporting intelligence on cyber security breaches. Based on this we are able to provide an overview of the most common attack vectors, showing that all except three hacks were possible due to relatively lax security. We also show that while the security regimen of Bitcoin exchanges is not on par with other financial service providers, the use of stolen credentials, which does not require any hacking, is decreasing. We also show that the amount of BTC taken during a breach is decreasing, as well as the exchanges that terminate after being breached. With exchanges being targeted by nation-state hacking groups, security needs to be a first concern.
Security breaches of the cryptocurrency exchanges usually cause the price fluctuation in the market. Approximately one hundred cryptocurrency thefts, including hacks and scams, has occurred since 2012 to 2018, half of which are hacks of Bitcoin. Based on the thirty Bitcoin hacks, this study portrays the general price pattern during the hack. And it illustrates the link between the size of the hack and the subsequent price change of Bitcoin. The tests reveal that the larger the volume of the hack, the stronger the price drop. However, a similar obvious relationship does not exist for the recovery of the price. The study might be the first piece of research focus on the hacks and the price pattern in a short time period.
Bitcoin has enjoyed wider adoption than any previous cryptocurrency; yet its success has also attracted the attention of fraudsters who have taken advantage of operational insecurity and transaction irreversibility. We study the risk that investors face from the closure of Bitcoin exchanges, which convert between Bitcoins and hard currency. We examine the track record of 80 Bitcoin exchanges established between 2010 and 2015. We find that nearly half (38) have since closed, with customer account balances sometimes wiped out. Fraudsters are sometimes to blame, but not always. Twenty-five exchanges suffered security breaches, 15 of which subsequently closed. We present logistic regressions using longitudinal data on Bitcoin exchanges aggregated quarterly. We find that experiencing a breach is correlated with a 13 times greater odds that an exchange will close in that same quarter. We find that higher-volume exchanges are less likely to close (each doubling in trade volume corresponds to a 12% decrease in the odds of closure). We also find that exchanges that derive most of their business from trading less popular (fiat) currencies, which are offered by at most one competitor, are less likely to close.
About 99 percent of cryptocurrency trades occur on organised exchanges with many investors subsequently keeping their digital assets in accounts with cryptocurrency markets. This generates exposure to the risk of exchange closures. We construct a database containing eight key characteristics on 238 cryptocurrency exchanges and employ machine learning techniques to predict whether a cryptocurrency market will remain active or whether it will go out of business. Both in-sample and out-of-sample measures of forecasting performance are computed and ranked for four popular machine learning algorithms. While all four models produce satisfactory classification accuracy, our best model is a random forest classifier. It reaches accuracy of 90.4 percent on training data and 86.1 percent on a test dataset. From the list of predictors we find that exchange lifetime, transacted volume and cyber security measures such as security audit, cold storage and bug bounty programs rank high in terms of feature importance across multiple algorithms. On the other hand, whether an exchange has previously experienced a security breach does not rank highly according to its contribution to classification accuracy.
A non-traditional type of financial asset, cryptocurrencies based on public blockchains are still little understood in their real-world behavior. Exogenous events such as cyber-attacks and their media coverage can strongly affect their supply and demand, adoption and usage, efficiency, and infrastructural development, thus influencing their price stability and market valuation. Given the great technical complexity of blockchains, we believe that a pure economic analysis of risks associated with cryptocurrencies is simply not sufficient to convey the relationships between attacks and the disruptive effects that these can bring on the operation of cryptocurrencies.On these grounds, we survey the most common types of attacks for PoW cryptocurrencies and evaluate their impact on the returns of a number of real-world cryptocurrencies for which market data are available. Due data availability, our event study analysis focuses on instances of 51% attacks, hard forks, and wallet attacks. The main goal of our analysis is to understand the relationship between technical events (cyber-attacks and coordinated user/miner behavior) and the economic impacts surrounding them. We aim to develop a deeper understanding of these systems that are object of great research interest in separate disciplines, supporting policy makers in their regulatory decisions concerning crypto-assets and associated cyber-related financial risks.
Bitcoin, developed by Satoshi Nakamoto after the financial crisis in 2008, has risen in price since the beginning of 2010. To date, many investors have been participating in the Bitcoin market and trading. This study analyzes if the FOMC and macroeconomic announcements affect Bitcoin prices. Various studies have been actively conducted as the cryptocurrency market, including Bitcoin, rapidly develops. However, few studies on the effect of FOMC and macroeconomic announcements on Bitcoin prices are available. If the announcements affect the Bitcoin prices, then investors could consider the conclusions of this study to prepare for price changes.
As the indispensable trading platforms of the ecosystem, hundreds of cryptocurrency exchanges are emerging to facilitate the trading of digital assets. While, it also attracts the attention of attackers. A number of scam attacks were reported targeting cryptocurrency exchanges, leading to a huge amount of financial loss. However, no previous work in the research community has systematically studied this problem. This paper makes the first effort to identify and characterize the cryptocurrency exchange scams. First, over 1,500 scam domains and over 300 fake apps are identified, by collecting existing reports and using typosquatting generation techniques. Then, by investigating the relationship between the scam domains and fake apps, this paper identifies 94 scam domain families and 30 fake app families. By further characterizing the impacts of such scams, it is revealed that these scams have incurred financial loss of 520k US dollars at least. It is further observed that the fake apps have been sneaked to major app markets (including Google Play) to infect unsuspicious users. The findings in this paper demonstrate the urgency to identify and prevent cryptocurrency exchange scams. To facilitate future research, all the identified scam domains and fake apps have been publicly released to the research community.
We develop a model where a firm has an optimal exposure to cyber risk. With rational, fully informed agents and with no hysteresis, a successful cyberattack should have no impact on a financially unconstrained target's reputation and post-attack policies. In contrast, when a successful attack involves the loss of personal financial information, there is a significant shareholder wealth loss, which is much larger than the attack's out-of-pocket costs. This excess loss is higher when the attack decreases sales growth more and lower when the board pays more attention to risk management before the attack. Further, an attack decreases a firm's risk appetite, as it beefs up its risk management and information technology and decreases the risk-taking incentives of management. Finally, successful cyberattacks adversely affect the stock price of firms in the target's industry. These results imply that successful attacks with personal financial information loss provide adverse information about cyber risk to target firms, their stakeholders, and their competitors.
This paper investigates the financial market effects of recent cybercriminality in cryptocurrency markets. Hacking events are found to increase both the price volatility of the targeted cryptocurrency and broad cross-cryptocurrency correlations. Further, cybercrime events significantly reduce price discovery sourced within the hacked currency relative to other cryptocurrencies. Finally, abnormal returns in the hours prior to the cybercrime event, revert to zero when news is publicly announced.
Blockchain technology has attracted considerable attention owing to its wide range of potential applications. It first appeared as a cryptocurrency, called Bitcoin, but has since been used in many other business and nonbusiness applications. Unlike most existing systems that are based on centralized frameworks, this new technology utilizes peer‐to‐peer networks and distributed systems which includes blockchain registers to store transactions. Its structure is designed as a digital log file and stored as a series of linked groups, called blocks. Each individual block is locked cryptographically with the previous block. Once a block has been added, it cannot be altered. Many security experts speculate that the inherent cryptographic nature of the blockchain system is sufficient to withstand constant hacking and security threats. However, previous studies on the security and privacy of blockchain technology have shown that many applications have fallen victim to successful cyberattacks. Owing to the increasing demand for cryptocurrency and its current security challenges, previous studies have not focused on blockchain technology cybersecurity vulnerabilities extensively. Here, our study extends upon the previous studies on vulnerabilities and investigates the types of potential attacks. Our study then provides further direction to highlight possible countermeasures against blockchain technology vulnerability to cybersecurity.
Cryptocurrencies such as Bitcoin are establishing themselves as an investment asset and are often named the New Gold. This study, however, shows that the two assets could barely be more different. Firstly, we analyze and compare conditional variance properties of Bitcoin and Gold as well as other assets and find differences in their structure. Secondly, we implement a BEKK-GARCH model to estimate time-varying conditional correlations. Gold plays an important role in financial markets with flight-to-quality in times of market distress. Our results show that Bitcoin behaves as the exact opposite and it positively correlates with downward markets. Lastly, we analyze the properties of Bitcoin as portfolio component and find no evidence for stable hedging capabilities. We conclude that Bitcoin and Gold feature fundamentally different properties as assets and linkages to equity markets. Our results hold for the broad cryptocurrency index CRIX. As of now, Bitcoin does not reflect any distinctive properties of Gold other than asymmetric response in variance.
To its proponents, the cryptocurrency Bitcoin offers the potential to disrupt payment systems and traditional currencies. It has also been subject to security breaches and wild price fluctuations. This paper identifies and analyzes the impact of suspicious trading activity on the Mt. Gox Bitcoin currency exchange, in which approximately 600,000 bitcoins (BTC) valued at 150 to more than $1,000 in two months.
We explore the optimal conditional heteroskedasticity model with regards to goodness-of-fit to Bitcoin
price data. It is found that the best model is the AR-CGARCH model, highlighting the significance of
including both a short-run and a long-run component of the conditional variance.
This paper explores the financial asset capabilities of bitcoin using GARCH models. The initial model showed several similarities to gold and the dollar indicating hedging capabilities and advantages as a medium of exchange. The asymmetric GARCH showed that bitcoin may be useful in risk management and ideal for risk averse investors in anticipation of negative shocks to the market. Overall bitcoin has a place on the financial markets and in portfolio management as it can be classified as something in between gold and the American dollar on a scale from pure medium of exchange advantages to pure store of value advantages.
This is the first article that studies BitCoin price formation by considering both the traditional determinants of currency price, e.g., market forces of supply and demand, and digital currencies specific factors, e.g., BitCoin attractiveness for investors and users. The conceptual framework is based on the Barro (1979) model, from which we derive testable hypotheses. Using daily data for five years (2009–2015) and applying time-series analytical mechanisms, we find that market forces and BitCoin attractiveness for investors and users have a significant impact on BitCoin price but with variation over time. Our estimates do not support previous findings that macro-financial developments are driving BitCoin price in the long run.
The authors find support for a negative relation between conditional expected monthly return and conditional variance of monthly return using a GARCH-M model modified by allowing (1) seasonal patterns in volatility, (2) positive and negative innovations to returns having different impacts on conditional volatility, and (3) nominal interest rates to predict conditional variance. Using the modified GARCH-M model, they also show that monthly conditional volatility may not be as persistent as was thought. Positive unanticipated returns appear to result in a downward revision of the conditional volatility, whereas negative unanticipated returns result in an upward revision of conditional volatility. Copyright 1993 by American Finance Association.
While many studies have investigated the link between macroeconomic events and equity market volatility, few have considered the impact on option implied volatilities. Given the recent focus on trading in implied volatility, in the context of the S&P 500 VIX index, this paper examines how the VIX index behaves around US monetary policy announcements. It is revealed that the VIX index falls significantly on the day of Federal Open Market Committee meetings.
A natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) to allow for past conditional variances in the current conditional variance equation is proposed. Stationarity conditions and autocorrelation structure for this new class of parametric models are derived. Maximum likelihood estimation and testing are also considered. Finally an empirical example relating to the uncertainty of the inflation rate is presented.
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