Crash Period Research Articles

Complex network analysis of cryptocurrency market during crashes

This paper identifies the cryptocurrency market crashes and analyses its dynamics using the complex network. We identify three distinct crashes during 2017–20, and the analysis is carried out by dividing the time series into pre-crash, crash, and post-crash periods. Partial correlation based complex network analysis is carried out to study the crashes. Degree density (ρD), average path length (l̄), and average clustering coefficient (cc¯) are estimated from these networks. We find that both ρD and cc¯ are smallest during the pre-crash period, and spike during the crash suggesting the network is dense during a crash. Although ρD and cc¯ decrease in the post-crash period, they remain higher than pre-crash levels for the 2017–18 and 2018–19 crashes suggesting a market attempt to return to normalcy. We get l̄ is minimal during the crash period, suggesting a rapid flow of information. A dense network and rapid information flow suggest that during a crash uninformed synchronized panic sell-off happens. However, during the 2019–20 crash, the values of ρD, cc¯, and l̄ did not vary significantly, indicating minimal change in dynamics compared to other crashes. The findings of this study may guide investors in making decisions during market crashes.

Crash risk prediction using sparse collision data: Granger causal inference and graph convolutional network approaches

Applying deep learning techniques in predicting traffic crashes provides a possibility to achieve the vision of “zero fatalities” by implementing a proactive safety countermeasure. The overly sparse distribution of crashes hinders high-precision prediction of crash risks in urban areas. The lack of effective crash association analysis also reduces the interpretability of the prediction model. To address these gaps, this paper proposes a novel method, Priori Spatial and Temporal Causal Graph Convolutional Network (PST-CGCN) for predicting crash risk based on causal inference and graph convolutional network. In PST-CGCN, a data enhancement strategy utilizing historic priori spatial and temporal knowledge is employed to improve the distinguishability between crash data, then a graph convolutional network based on causal association is designed. The focus of this study is to shift attention from using multiple data sources to predict traffic risk to exploring the intrinsic temporal and spatial causal relationship between traffic crashes in different regions. Results from the NYC case study show that PST-CGCN model performs best compared with seven baseline models on three indicators, identifying high crash risk periods and areas in short-term forecasting. The results of ablation experiments on the PST-CGCN demonstrate the rationality of each component in the framework. Besides, gradient analysis combined with causal association is applied to explore directed causal relationships between regions, which enhances the model’s interpretability.

Tension in Financial Reporting: Reacting to a Peer Bankruptcy Announcement

We investigate if and how a peer’s bankruptcy affects financial reporting by other firms within the industry. Prior research documents that the bankruptcy filing of a peer firm has negative capital market effects on other firms within the industry (lower stock market value and higher cost of debt). We argue that firms within an industry experiencing peer bankruptcies modify their financial reporting to mitigate such negative capital market effects. However, tension arises as to whether such modification is toward more conservative accounting or the opposite. Using a large sample of firms from 1980 to 2018, we find that following a peer firm bankruptcy filing, other firms within the industry exhibit a rise in conditional conservatism in their financial reporting. Our findings are robust to a battery of tests including the exclusion of distressed industries, the 2000 dot-com crash period, and the 2008 financial crisis period as well as employing an alternative proxy for conditional conservatism. The results are not significant for placebo bankruptcies 1 and 2 years before actual bankruptcies. Further analyses show that the spillover effects are more pronounced for firms in homogeneous industries, which exhibit higher leverage, with strong governance mechanisms, and in industries that experience strong market reaction following peer bankruptcy announcements. We also find that the bankruptcy filing of larger and older firms leads to stronger spillover effects.

The Impact of the Cryptocurrency Market on Islamic vs. Conventional Stock Returns: Evidence from Gulf Cooperation Council Countries

The rapid rise and widespread global adoption of cryptocurrencies in recent years has fundamentally transformed the international financial landscape, with digital assets increasingly being recognized for their potential to influence the stability and performance of traditional capital markets. Against this backdrop, this study aims to empirically investigate the impact of cryptocurrency returns on Islamic vs. conventional stock returns in Gulf Cooperation Council (GCC) countries. The salient distinctions between Islamic and conventional stock markets include fundamental differences in principles, investment allocations, and risk profiles, underscoring the importance of examining the impact of cryptocurrency returns on these distinct equity segments. Daily data were collected from stock indices in five GCC countries over the period 2016–2019, including two sub-periods: before and after the 2017 crypto crash. Pooled OLS, fixed effects, random effects, and generalized linear models (GLMs) were used to analyze the data collected during the study. With the GCC increasingly focusing on cryptocurrency markets, there is growing concern about these markets’ potential impact on regional stocks. This study addresses the important questions of whether the impacts of the cryptocurrency market on Islamic vs. conventional stock markets differ throughout the GCC region and how these impacts have evolved since the crypto crash period. The findings reveal that cryptocurrency returns had a negative impact on both GCC Islamic and conventional stock market returns for the full sample period (2016–2019), and the negative effect was far more pronounced for conventional stocks. For the two sub-periods before and after the crash, only the cryptocurrency market and conventional GCC stocks remained negatively correlated, while the cryptocurrency market and the GCC Islamic stock markets became uncorrelated. Thus, for the calmer sub-periods before and after the crypto crash, the rise in cryptocurrency returns may have enticed GCC investors away from conventional stocks, perhaps resulting in a decline in their investment in these stocks. Meanwhile, those who invest in Islamic stocks may not be exposed to this temptation.

Core inductive electric field during sawtooth crashes on DIII-D

Sawtooth crashes on tokamak plasmas exhibit relaxation much faster than resistive time scales via a mechanism not fully understood. Using core magnetic measurements from the Radial Interferometer-Polarimeter (RIP) diagnostic on the DIII-D tokamak, Grad–Shafranov equilibria constrained by internal magnetic measurements that have high time resolution ( <1 µs) can be computed, allowing analysis of how equilibrium parameters such as safety factor q, current density J, and parallel electric field E∥ , particularly on-axis, evolve. At the sawtooth crash, on-axis safety factor q 0 is observed to rise by 5% but remain below 1 throughout the cycle, and on-axis current density J 0 is observed to drop by 5%. On-axis parallel electric field E∥(0) is found to be balanced by ηJ0 (resistivity times on-axis current density) except during the 200 µs crash period, where E∥(0) reaches 22 V m−1, exceeding ηJ0 by a factor of more than 2000. These first measurements in tokamak plasmas verify that generalized Ohm’s law is not balanced during the crash by resistive effects alone; this is a finding expected due to the relaxation being much faster than resistive timescales. Measurement of the electric field during the tokamak sawtooth serves to illuminate the physical mechanisms at work.

Temporal instability of the determinants of fatal/severe elderly pedestrian injury outcomes in intersections and non-intersections before, during, and after the COVID-19 pandemic

This study examines the variability in the impacts of factors influencing injury severity outcomes of elderly pedestrians (age >64) involved in vehicular crashes at intersections and non-intersections before, during, and after the COVID-19 pandemic. To account for unobserved heterogeneity in the crash data, a random parameters logit model with heterogeneity in the means approach is utilized to analyze vehicle-elderly pedestrian crash data from Seoul, South Korea, occurring between 2018 and 2022. Preliminary transferability tests revealed instability in factor impacts on injury severity outcomes, highlighting the need to estimate individual models across various road segments and time periods. Thus, the dataset was segregated by crash location (intersection/non-intersection) and period (before, during, and after COVID-19), with individual models estimated for each group. Results obtained from the analyses revealed that back injuries positively influenced fatalities at non-intersections after the pandemic and was negatively associated with fatalities at intersections before the pandemic. Additionally, several indicators demonstrated significant instability in their impact magnitudes across different road segments and crash years. During the pandemic, head injuries increased the probability of fatalities higher at non-intersections. After the pandemic, crosswalk locations decreased the possibility of fatalities more at intersections. Compared to intersection segments, the female indicator reduced the likelihood of fatal injuries at non-intersections more before, during, and after the pandemic. Before the pandemic, much older pedestrians experienced a greater decline in fatalities at intersections than non-intersections. This instability could be attributed to altered mobility patterns stemming from the COVID-19 pandemic. Overall, the study findings highlight the variability of determinants of fatal/severe injury outcomes among elderly pedestrians across various road segments and years, with the underlying cause of this fluctuation remaining unclear. Furthermore, the findings revealed that accounting for heterogeneity in the means of random parameters enhances model fit and provides valuable insights for safety professionals. The factor impact variability in the estimated models carries significant implications for elderly pedestrian safety, especially in scenarios where precise projections of the effects of alternative safety measures are essential. Road safety experts can leverage these findings to refine or update current policies to enhance elderly pedestrian safety at intersections and non-intersections.

Automated W7-X sawtooth crashes detection and characterization

Sawtooth crashes are observed during ECCD experiments at the superconducting optimized stellarator Wendelstein 7-X. The study and the characterization are necessary in order to understand under which condition ECCD can be driven without posing a risk to experimental operations. The development of automatic tools is crucial to speed up the analysis of extensive datasets. In this work, we report on the first attempt of using a data-driven approach to automatically characterize the sawtooth crashes. Cluster algorithms are applied to the dataset, confirming the existence of two distinct types of crashes. This approach allows to study the two groups separately and underlines the different plasma parameters that influence the sawtooth crash parameters, for instance crash amplitude and period.

Impact of regulatory capital on bank interest margins: Moderating role of default risk

The recent pandemic and aftermath debate regarding bank interest margins deserve special attention and have become policy dialogue in emerging economies. However, the previous literature's findings were largely inconclusive and ignored influential variables such as the impact of default risk on bank interest margins. Using a two-step system GMM estimation considering 32 Bangladeshi commercial banks from 2000 to 2022, we produce robust evidence that higher regulatory capital restrictions reduce the bank interest margin, while increased default risk induces the bank interest margin. The impact intensity during the COVID pandemic is higher than in the pre-COVID period. Moreover, we find the synergy effect of regulatory capital and default risk assists in reducing the bank interest margin. Bank margin persistently fell during the capital market crash period, whereas it rose in the financial crisis period. We cast several robustness tests to confirm our main findings. These findings could generate important implications for bank stakeholders and policymakers.

Impact of real-time weather conditions on crash injury severity in Kentucky using the correlated random parameters logit model with heterogeneity in means

The present study investigated the impact of real-time weather (air temperature, relative humidity, precipitation, wind speed, and solar radiation) on crash injury severity. Recent crash data (January 2016 to April 2021) on Interstate-75 in the state of Kentucky were merged with real-time weather information (retrieved from Kentucky Mesonet stations) at the 1-hour level. The severity index “SI” (i.e., the ratio of percent severe crashes to percent exposure of a specific weather state during the crash period) was introduced to evaluate the impact of different real-time weather states on fatal and severe injury crashes. Furthermore, the standard mixed logit (MXL), correlated mixed logit (CMXL), and correlated mixed logit with heterogeneity in means (CMXLHM) models were fitted and compared to identify the risk factors contributing to crash injury severity while accounting for unobserved heterogeneity. The results showed that the CMXLHM model was statistically superior to the CMXL and MXL models based on various goodness-of-fit measures (e.g., Akaike information criterion “AIC” and McFadden pseudo R-squared). Results from the SI analysis and CMXLHM model showed that real-time weather-related factors (e.g., air temperature ≥ 70 0F and relative humidity ≥ 90 %) were significantly associated with higher severe injury likelihood. Further, driving under the influence (DUI), young drivers, and vehicle travel speed were associated with greater injury severities. On the other hand, presence of horizontal curve, passenger cars, and hourly traffic volume were associated with lower injury severity likelihood. The study outcomes can help in incident management by suggesting specific real-time weather-related states to feed to dynamic message signs (DMS) to enhance travelers’ safety along the interstates.

Variation in small mammal food resource niche metrics of Western Barn Owl (Tyto alba) at the nesting pair and local population level

Abstract In the present study, we investigated food resource niche parameters and the degree of specialization of two local Western Barn Owl (Tyto alba) populations in two different demographic phases as the crash (2015–2016) and outbreak (2019–2020) of the Common Vole (Microtus arvalis). The study was conducted in two parts of the Transdanubian region of Hungary, namely in Duna-Drava National Park (DDNP) in the southeastern part, and in Fertő-Hanság National Park (FHNP) in the north-western part. For the analysis, we used food consumption data of 20–20 randomly selected breeding pairs from the DDNP population, while 14 and 17 breeding pairs in FHNP population in the crash and outbreak periods, respectively. Since the small mammal consumption of owls represented 99.3% of the total number of individuals, only data of small mammals as main food resource were taken into account during the analysis. Based on a trait-based framework which taking into account the resemblance between resources, Rao’s quadratic entropy metrics was used to estimate the food resource niche breadth at local owl populations and the breeding pair level. The small mammal resource utilization of owls was dependent on populations. The niche breadth of DDNP population was significantly smaller than FHNP population. The estimated niche overlap at the individual level was significantly different between the two populations. The calculated value of specialization of barn owl populations was significantly higher in north-western than south-eastern population. The niche breadth of the owl population living in the DDNP was significantly higher during the crash period. In contrast, the estimated niche breadth of the population living in FHNP did not differ significantly between the two demographic phases. Based on our result, the applied trait-based framework of resource niche pattern analysis demonstrated that the differences of niche breadth were explored in more detail by this method between the local Barn Owl populations of different geographical region.

Modeling dependence structure between green sukuk spread in Malaysia and the uncertainty factors before and during the COVID-19 pandemic

Purpose This paper aims to analyze the dependence structure between the Green Sukuk Spread in Malaysia and uncertainty factors from January 1, 2017, to May 23, 2023, covering two main periods: the pre-COVID-19 and the COVID-19 periods. Design/methodology/approach This study contributes to the current literature by explicitly modeling nonlinear dependencies using the Regular vine copula approach to capture asymmetric characteristics of the tail dependence distribution. This study used the Archimedean copula models: Student’s-t, Gumbel, Gaussian, Clayton, Frank and Joe, which exhibit different tail dependence structures. Findings The empirical results suggest that Green Sukuk and various uncertainty variables have the strongest co-dependency before and during the COVID-19 crisis. Due to external uncertainties (COVID-19), the results reveal that global factors, such as the Infect-EMV-index and the higher financial stress index, significantly affect the spread of Green Sukuk. Interestingly, in times of COVID-19, its dependence on Green Sukuk and the news sentiment seems to be a symmetric tail dependence with a Student’s-t copula. This result is relevant for hedging strategies, as investors can enhance the performance of their portfolio during the COVID-19 crash period. Originality/value This study contributes to a better understanding of the dependency structure between Green Sukuk and uncertainty factors. It is relevant for market participants seeking to improve their risk management for Green Sukuk.

An enhanced Wasserstein generative adversarial network with Gramian Angular Fields for efficient stock market prediction during market crash periods

An enhanced Wasserstein generative adversarial network with Gramian Angular Fields for efficient stock market prediction during market crash periods

Distance Correlation Market Graph: The Case of S&amp;P500 Stocks

This study investigates the use of a novel market graph model for equity markets. Our graph model is built on distance correlation instead of the traditional Pearson correlation. We apply it to the study of S&amp;P500 stocks from January 2015 to December 2022. We also compare our market graphs to the traditional market graphs in the literature, those built using Pearson correlation. To further the comparison, we also build graphs using Spearman rank correlation. Our comparisons reveal that non-linear relationships in stock returns are not captured by either Pearson correlation or Spearman rank correlation. We observe that distance correlation is a robust measure for detecting complex relationships in S&amp;P500 stock returns. Networks built on distance correlation networks, are shown to be more responsive to market conditions during turbulent periods such as the COVID crash period.

Revealing stock market risk from information flow based on transfer entropy: The case of Chinese A-shares

According to the efficient-market hypothesis, asset prices can reflect all available information in an efficient stock market. This paper focuses on the transmission of information flow between different equities during a financial crisis to reveal the potential risk spreading in the stock market. The information flow and corresponding time-varying stock networks (i.e., information flow networks and industry networks) are constructed using the transfer entropy with the intra-day data of the Chinese A-Share stock market around the 2008 subprime mortgage crisis. The empirical results show that the information flow can reflect the market condition and the financial crisis stage well. The information flow is more apparent when it falls than when it rises. Similarly, it is more apparent in the crash period than in the downturn period. Furthermore, the evolution of information flow networks, markedly shows that several crucial topological parameters, indicates the crash period. We identify four key stocks, through which the whole stock market tends to exchange market information. Finally, we transform the information flow networks into coarse-grained industry networks, and find that the stock market is more likely to be driven by a master or two industries. Through the core industries, the entire stocks are more like to exchange market information, resulting in the increasing system risk in the crash period. Our work provides an interesting insight for regulators to manage market risks and for market participants to avoid troubles.

Spillover effects of crash and jump events: evidence from Chinese market

PurposeThis paper aims to investigate the spillover effects of jump and crash events among Chinese nonfinancial firms.Design/methodology/approachThis sample consists of more than 1.5 million weekly observations of over 3,000 Chinese listed firms over the period 1991–2015. The authors utilize univariate tests to compare the post-event performance of matched peer and non-peer control firms and cross-sectional regressions of their abnormal returns/cumulative abnormal returns (ARs/CARs) and returns on assets (ROAs).FindingsThe authors find that extreme risk-adjusted abnormal stock returns (stock price crashes and jumps) generate statistically significant ARs/CARs in the same directions in industry, size, leverage, and geographical location matched peer firms in Chinese stock market. Further tests reveal that peer firms' response to the crash event is pronounced more in the group of firms about which the information asymmetry is high between investors and firms.Research limitations/implicationsPortfolio investors can adjust their portfolios accordingly by selling stocks of the matching rival firms during a crash period. Policymakers may develop policies so as to protect the interests of small investors in the events of crashes in the markets. They can reduce the information asymmetry between the firms and the investors by making information about the firms more transparent, so as to reduce the contagion in case of crash event.Practical implicationsThis study has important implications for portfolio investment managers and policymakers.Originality/valueTo the best of authors' knowledge, this is the first study that combines the jump and crash events and attempts to assess their spillover effects on other firms in Chinese stock market.

Forecasting stock volatility during the stock market crash period: The role of Hawkes process

We use a heterogeneous autoregressive model with Hawkes process (HAR-RV-H) to forecast the volatility of 300 major individual stocks in the Chinese stock market during the 2015 market crash period. The Hawkes intensity process is calculated with the tick-by-tick data of individual stocks. We show that the Hawkes indicator has predictive power for most individual stocks in the market crash period. We compare the in- and out-of-sample forecast results for the HAR type models and conclude that the Hawkes indicator can improve both in- and out-of-sample forecasting abilities.

Road crashes in Adelaide metropolitan region, the consequences of COVID-19

BackgroundMany countries instituted lockdown rules as the COVID-19 pandemic progressed, however, the effects of COVID-19 on transportation safety vary widely across countries and regions. In several situations, it has been shown that although the COVID-19 closure has decreased average traffic flow, it has also led to an increase in speeding, which will indeed increase the severity of crashes and the number of fatalities and serious injuries. MethodsAt the local level, Generalized linear Mixed (GLM) modelling is used to look at how often road crashes changed in the Adelaide metropolitan area before and after the COVID-19 pandemic. The Geographically Weighted Generalized Linear Model (GWGLM) is also used to explore how the association between the number of crashes and the factors that explain them varies across census blocks. Using both no-spatial and spatial models, the effects of urban structure elements like land use mix, road network design, distance to CBD, and proximity to public transit on the frequency of crashes at the local level were studied. ResultsThis research showed that lockdown orders led to a mild reduction (approximately 7%) in crash frequency. However, this decrease, which has occurred mostly during the first three months of the lockdown, has not systematically alleviated traffic safety risks in the Greater Adelaide Metropolitan Area. Crash hotspots shifted from areas adjacent to workplaces and education centres to green spaces and city fringes, while crash incidence periods switched from weekdays to weekends and winter to summer. ImplicationsThe outcomes of this research provided insights into the impact of shifting driving behaviour on safety during disorderly catastrophes such as COVID-19.

Modelling and forecasting risk dependence and portfolio VaR for cryptocurrencies

In this paper, we investigate the co-dependence and portfolio value-at-risk of cryptocurrencies, with the Bitcoin, Ethereum, Litecoin and Ripple price series from January 2016 to December 2021, covering the crypto crash and pandemic period, using the generalized autoregressive score (GAS) model. We find evidence of strong dependence among the virtual currencies with a dynamic structure. The empirical analysis shows that the GAS model smoothly handles volatility and correlation changes, especially during more volatile periods in the markets. We perform a comprehensive comparison of out-of-sample probabilistic forecasts for a range of financial assets and backtests and the GAS model outperforms the classic DCC (dynamic conditional correlation) GARCH model and provides new insights into multivariate risk measures.

Performance and risk of Brazilian equity mutual funds in the context of the covid-19 pandemic

The goal of this research was to analyze how Brazilian equity investment funds behaved during the Covid-19 period. The analysis was divided into 3 sub-periods: (i) pre-crisis (2019-10-03 to 2020-01-31); (ii) crash (2020-02-19 to 2020-03-23); (iii) and recovery (2020-03-24 to 2020-04-30. The main results showed that during the crash period, in all the categories, more than 50% of the funds obtained a return superior to that of the IBRX100. However, in the recovery period, the scenario is reversed and the vast majority of funds start to underperform the index. However, when performance is analyzed from the perspective of multifactor risk models, it is possible to identify positive alpha generation during the pre-crisis period and negative (destruction of value) during the crash period. In addition, when examining fund flows, it can be seen that there was no "run on the fund" phenomenon to redeem the shares. Actually, in the analyzed period, the inflows surpassed the redemptions. This may indicate that the fund investors are financially more educated and that he or she acted with more caution when waiting for a less turbulent moment. The article concludes that the industry was coming from an excellent performance in the pre-crisis period, which was interrupted by a period of value destruction during the moment of the greatest turmoil, and that, finally, during the recovery period, the performance was below the market index, but without generating or destructing significant value.

Crisis Response Strategies During Cryptocurrency Crash: A Netnographic Studies of Lunatics Community

Cryptocurrency is a speculative investment due to its volatility which could result in significant returns but also could end in crashes. Terra blockchain collapsed when its stable coin UST failed to maintain its peg to 1 USD and caused its sister coin LUNA to drop by more than 90% only in a few days. Terra LUNA itself has gained success and attracted many investors that became a strong community called Lunatics. Using Netnography; this study tried to observe crisis response strategies from Do Kwon, founder and CEO of Terraform Labs, and from Terra LUNA official Twitter account during the crash. Also, this study used community sentiment as an indicator to measure the success of the strategies. In addition, this study observed the interaction of the community during the crash period and how they overcome the crisis together. The results show that mortification and corrective action are the most effective strategy to generate positive sentiment. However, denials toward rumors cause more negative sentiment within the community. Despite the recovery plan from the Terra network, the Lunatics community also has its ways of recovering from the crisis. This study also revealed that community influencers' roles are crucial in controlling rumors during the crisis.