Market Crash Research Articles

Dynamic Interlinkages between the Twitter Uncertainty Index and the Green Bond Market: Evidence from the Covid-19 Pandemic and the Russian-Ukrainian Conflict

AbstractThis study examines the time-varying connectedness between green bonds, Twitter-based uncertainty indices, and the S&P 500 Composite Index. We implement the time- and frequency-based connectedness methodologies and employ data between April 1, 2014 and April 21, 2023. Our findings suggest that (i) connectedness indices robustly capture prominent incidents during the episode; (ii) Twitter-based uncertainty indices are the highest transmitters of return shocks; (iii) net return spillovers transmitted by the S&P 500 Index sharply increased in 2020:1–2020:3, stemmed by the stock market crash in February 2020; and (iv) Twitter-based uncertainty indices showed significant net spillovers in July and November 2021.

Stock crash and how it spreads over the Vietnamese stock market

Stock crash and how it spreads over the Vietnamese stock market

Elasticity of environmental, social, sustainable and management stocks: an analysis of market breakdown, technology and growth while COVID-19 pandemic

ABSTRACT The COVID-19 pandemic and subsequent eradication precipitated a rare and unexpected stock market crash. Consequently, the crisis presents a unique chance to evaluate Environmental, Social, Sustainable, and Management (ESSM) policy choices. The contribution of this study is to demonstrate the application of the investment horizon of ESSM concerns and investment decisions, technology and growth and shows the significance of the long-term perspective in ESSM investment decision making. During the period of January 2020 to December 2021, stocks with higher ESSM ratings had significantly greater returns, less return arbitrariness, and bigger operational profit margins. ESSM ventures with more advertising expenditures had higher stock returns, but investors with a greater predisposition for ESSM saw less return arbitrariness during market crises. This study emphasizes the importance of investor and consumer loyalty to the elasticity of ESSM stocks.

An innovative approach for optimising CCP default management through agent-based modelling

This paper develops a rigorous model to analyse the market impact of liquidation. Invoking innovative techniques from agent-based simulation, we construct the order book's reaction function to liquidation. A methodology is then developed for establishing the optimal close-out strategy which balances the velocity of positional liquidation with that consequential market reaction function. This model can be used in parallel to more traditional calculations of market volatility to ensure that the overall risk capitalisation of a central clearing counterparty remains robust. In the case of financial markets, agent-based models often seek to account for the so-called stylised facts of financial markets. Stylised facts are simply empirical regularities that appear to be stable across markets and over time. Such facts range from statistical concepts, such as the distribution of returns, to more abstract notions such as stock market bubbles and crashes.

MODELLING STOCK PRICES OF A BANK WITH EXTREME VALUE DISTRIBUTIONS

The study investigates the application of Extreme Value Theory in modelling stock prices, aiming to capture the tail behaviour and extreme movements that conventional distributions often fail to represent accurately. The use of Extreme Value Theory has gained considerable attention in the field of finance due to its ability to model rare events, such as financial crises or market crashes. By incorporating Extreme Value Theory, researchers aim to improve risk management, portfolio optimization, and pricing of financial derivatives. In this study, the Log-normal, Weibull, Gamma, and Normal distributions were used to model the stock price closing data, with a specific focus on extreme value distributions. Both graphical explorations and goodness-of-fit criteria were considered together to evaluate the suitability of these distributions. When assessing the data, it was observed that the Weibull distribution provided the best fit for the given stock price closing data.

The impact of bank money on stock market integration: evidence from the Eurozone

This paper explores the impact of monetary policy on the integration of the equity markets of the five largest economies of the Eurozone. We show that a rise in leverage and accommodating monetary policies significantly affects the probability of simultaneous stock market crashes. Moreover, we find that an increasing loan-to-base ratio divergence has a destabilizing effect on equity markets. Our findings are instrumental for international investors, who might operate under the assumption that Eurozone stock markets exhibit stable correlations during non-crisis periods, bolstered by the prevailing monetary regime. We suggest implementing a probability threshold as a macroprudential tool to manage latent correlations and ensure effective risk management.

Forecasting tail risk of skewed financial returns having exponential‐polynomial tails

AbstractAggregated long and short trading risk positions of speculative assets over time are likely to be unequal. This may be because of irrational decisions of traders and investors as well as catastrophic events that lead to pronounce or salient market crashes. Returns of such assets are therefore more likely to have one polynomial tail and one exponential tail. The generalized hyperbolic (GH) skewed Student‐t distribution is known to handle such situations quite well. In this paper, we use generalized autoregressive conditional heteroscedasticity (GARCH) models to empirically show the superiority of the GH skewed Student‐t distribution in forecasting the extreme tail risks of cryptocurrency returns in the presence of substantial skewness in comparison with some competing distributions. Furthermore, we show the practical significance of the GH skewed Student‐t distribution‐based risk forecasts in computing daily capital requirements. Evidence from the study suggests that the GH skewed Student‐t distribution model tends to be superior in forecasting volatility and expected shortfall (ES) but not value‐at‐risk. In addition, the distribution yields higher value‐at‐risk (VaR) exceptions but surprisingly avoids the red zone of the Basel II accord penalty zones and produces lower but optimal daily capital requirements. Therefore, in the presence of substantially skewed returns having exponential‐polynomial tails, we recommend the use of the GH skewed Student‐t distribution for parametric GARCH models in forecasting extreme tail risk.

Financial Risk: Case Study Analysis

This study explores the complex context of real-life financial risks. Using a thorough analysis of real case studies to reveal its varied nature and practical implications. Beginning with observations in the Age of Exploration and International Trade, the Financial Crisis of the 17th and 18th Centuries, and the Financial Crises of the 21st Century, this paper highlights the lessons learned for the current organization as well as individual contexts on how financial risk can affect the stability of financial conditions. This article, by deconstructing noteworthy instances of market crashes, corporate scandals, and regulatory failures, could also be used as guidance to emphasize the importance of strong risk management frameworks and tactics in limiting possible threats and improving resilience. Finally, this article serves as an insightful reminder of the importance of case studies in exposing real-world financial risks, providing significant insights for practitioners, researchers, and policymakers navigating the complicated environment of risk management.

Intra-urban house prices in Madrid following the financial crisis: an exploration of spatial inequality

In this paper we explore the temporal dynamics of spatial inequality in housing prices for Madrid, the capital city of Spain. Spatial inequalities are a concerning feature of urban areas across the globe. It has been suggested that within cities housing prices are becoming more geographically unequal over time, particularly since the 2008 housing market crash. However, more evidence is needed at the intra-urban level to understand neighbourhood house price differences in large urban areas. Changes are analysed during a key period of the housing market bust (2010–2015) and boom (2016–2019), using data from a major housing listing portal. Fine grain space-time analysis of the distribution of housing prices supports an increase in spatial inequality and polarisation at the neighbourhood level. Two spatially differentiated housing sub-markets of high- and low-priced housing are identified. The persistence and growth of spatial house price inequality has important societal implications for the wealth gap and segregation of rich and poor in cities.

Tail risk aversion and backwardation of index futures

We show that tail risk aversion, proxied by the skewness risk premium implied from the SSE 50 ETF options market, explains a significant proportion of the unusually deep backwardation of index futures during the 2015 Chinese stock market crash, while traditional factors such as non-synchronous trading, spot return, volatility and liquidity, all fail to explain the backwardation. These empirical results imply that investors' concern over the crash risk causes speculators to charge a high ‘insurance premium’ on hedgers. On the other hand, short-sale constraints, namely high security borrowing costs and other barriers, prevent reverse arbitrage such that the deep backwardation of index futures persists.

Manias, panics, & land: The property bubbles of the great Chinese crash of the 1880s

Historians researching China’s debt crisis of the early 1880s have largely focused on its stock market collapse of 1882-83. Yet the crisis was more than this—it was also a real estate crash. More precisely, the crisis entailed the bursting of two asset price bubbles in urban property: one in Hong Kong (1881), the other in Shanghai (1883). These boom-busts have long remained under-appreciated in both Chinese and global history. This article thus draws on what patchwork data exist, qualitative sources, economic theory, and global historical comparison to reconstruct their histories for the first time. That the crash was a property debacle in these globalising ports, not just a share selloff, suggests that it was no bout of speculation divorced from economic fundamentals, as some posit. Instead, the 1880s crash was a crisis that triggered a major depression, one inextricably bound up with late-Qing China’s integration into the global economy.

Chinese stock market integration with developed world: A portfolio diversification analysis

This study investigates integration dynamics between the Chinese stock market and major developed counterparts—Australia, Germany, Japan, the UK, and the US—focusing on portfolio diversification. Using a comprehensive analytical approach from 2012 to 2022, encompassing events like the Belt and Road Initiative, the Shanghai market crash, US-China trade tensions, and the COVID-19 pandemic, the research employs descriptive statistics, unit root tests, cointegration analysis, and VECM-based Granger Causality Tests. Findings indicate modest integration, endorsing diversified portfolios for developed country investors due to higher returns in China with acceptable risk. Unit root analysis confirms cointegration with developed indices, indicating relatively low integration. Granger Causality Tests reveal bidirectional causality, emphasizing mutual influence. Notably, no causal link exists between the US and China, possibly due to regulatory disparities and the trade war. The study enhances understanding of Chinese stock market dynamics, supporting global economic intertwining and urging further openness of China's domestic shares for economic growth.

Identification of the contagion effect in China's financial market uncertainties: A multiscale and dynamic perspective

Uncertainty is a crucial source of financial market volatility. Unlike previous research considering uncertainty from holistic and static viewpoints, we investigate the contagion effect of financial market uncertainties from a combined time-frequency and dynamic perspective. In a data-rich environment, we construct various uncertainty indices for China's financial market based on forecast errors and examine their contagion effect using a combined wavelet transform and network analysis. We also simulate dynamic contagion patterns under three different extreme scenarios, including a short-term liquidity crisis, a medium-term stock market crash and long-term central bank monetary policy shocks. The results indicate that the short-term fluctuations in China's financial market uncertainty exhibit a greater contagion intensity but with limited range. However, long-term uncertainty shocks exhibit a broader scope, but their impact intensity is significantly mitigated. Moreover, the liquidity crisis does not present a wide impact at the early stage, whereas the contagion pattern of the stock market crash shows explosive characteristics.

The self-contagion effect of a stock crash on the market response of the auditor’s other client

ABSTRACT This study aims to find out how information regarding a firm’s stock crash affects the stock price of other firms with the same auditor using 3,916 corporations listed in Korean stock market. The result shows that at the time of a stock crash within the industry, information regarding the crash is contagious via the auditor, as the market leverages the information audited for the crashed firm to additionally evaluate the auditor’s other clients within the industry. Moreover, the contagion effect of the crash within the same auditor to other client firms was found to not be related to the information quality of the client firm. When taking out information quality, however, a high positive discretional accruals quality is observed, and the greater the contagion effect of the crash. The results recognize and reflect the future risk of a stock crash for firms with a high amount of positive discretionary accruals due to the opportunistic incentives of managers in the market as well as market participants’ use of crash information audited by the same accounting firm. These results demonstrate that the market appreciates the audit effect on the crash and uses it for actual decision-making.

An Emotion-based Explanation for Bitcoin’s High-frequency Price Crash Risk

ABSTRACT We characterize the high-frequency price crash risk of Bitcoin. Using advanced text-mining techniques, we quantify emotional factors in the cryptocurrency market and investigate whether the proliferation of emotions in the cryptocurrency profession predicts the frequent occurrence of instant market crashes. We find that the cross-sectional dispersion of a catalogue of emotions is positively associated with high-frequency crash risk in the cryptocurrency market. These results are more salient for negative emotions. Differences in negative emotions may increase conflicts within a profession, resulting in idiosyncratic market outcomes.

Empirical Distribution of the U.S. Housing Market during the Great Recession: Nonlinear Scaling Behavior after a Major Crash

This study focuses on the real estate bubble burst in the US housing market during 2007–2008. We analyze the dynamics of the housing market crash and the after-crash sequence during the Great Recession. When a complex system deviates away from its typical path by the occurrence of an extreme event, its behavior is strongly characterized as nonstationary with higher volatility. With the utilization of a robust method, we present the characteristics of the aftershock period and provide useful information about the spatial distribution and the decay process of the aftershock sequence in terms of time. The returns of the housing price indices are well approximated by the empirics of a power law. Although we deal with low-frequency data, a time power-law relaxation pattern is identified. Our findings align with those in geophysics, indicating that the value of the relaxation parameter typically hovers around one and varies across different thresholds.

Bidirectional Risk Spillovers between Chinese and Asian Stock Markets: A Dynamic Copula-EVT-CoVaR Approach

This study aims to investigate bidirectional risk spillovers between the Chinese and other Asian stock markets. To achieve this, we construct a dynamic Copula-EVT-CoVaR model based on 11 Asian stock indexes from 1 January 2007 to 31 December 2021. The findings show that, firstly, synchronicity exists between the Chinese stock market and other Asian stock markets, creating conditions for risk contagion. Secondly, the Chinese stock market exhibits a strong risk spillover to other Asian stock markets with time-varying and heterogeneous characteristics. Additionally, the risk spillover displays an asymmetry, indicating that the intensity of risk spillover from other Asian stock markets to the Chinese is weaker than that from the Chinese to other Asian stock markets. Finally, the Chinese stock market generated significant extreme risk spillovers to other Asian stock markets during the 2007–2009 global financial crisis, the European debt crisis, the 2015–2016 Chinese stock market crash, and the China–US trade war. However, during the COVID-19 pandemic, the risk spillover intensity of the Chinese stock market was weaker, and it acted as the recipient of risk from other Asian stock markets. The originality of this study is reflected in proposing a novel dynamic copula-EVT-CoVaR model and incorporating multiple crises into an analytical framework to examine bidirectional risk spillover effects. These findings can help Asian countries (regions) adopt effective supervision to deal with cross-border risk spillovers and assist Asian stock market investors in optimizing portfolio strategies.

Early warning signals for stock market crashes: empirical and analytical insights utilizing nonlinear methods

This study introduces a comprehensive framework grounded in recurrence analysis, a tool of nonlinear dynamics, to detect potential early warning signals (EWS) for imminent phase transitions in financial systems, with the primary goal of anticipating severe financial crashes. We first conduct a simulation experiment to demonstrate that the indicators based on multiplex recurrence networks (MRNs), namely the average mutual information and the average edge overlap, can indicate state transitions in complex systems. Subsequently, we consider the constituent stocks of the China’s and the U.S. stock markets as empirical subjects, and establish MRNs based on multidimensional returns to monitor the nonlinear dynamics of market through the corresponding the indicators and topological structures. Empirical findings indicate that the primary indicators of MRNs offer valuable insights into significant financial events or periods of extreme instability. Notably, average mutual information demonstrates promise as an effective EWS for forecasting forthcoming financial crashes. An in-depth discussion and elucidation of the theoretical underpinnings for employing indicators of MRNs as EWS, the differences in indicator effectiveness, and the possible reasons for variations in the performance of the EWS across the two markets are provided. This paper contributes to the ongoing discourse on early warning extreme market volatility, emphasizing the applicability of recurrence analysis in predicting financial crashes.

Global financial risk and market connectedness: An empirical analysis of COVOL and major financial markets

This study presents a novel investigation into the dynamic connectedness between the recently developed global risk measure, COVOL, created by Engle and Campos-Martins (2023), and the major international stock, gold, and crude oil markets in China, the US and Europe. Applying conditional and aggregated connectedness approaches, we systematically uncover the multi-dimensional interlinkages to understand volatility transmissions within these markets. Results reveal that COVOL predominantly receives volatility transmissions, with the US and European stock markets acting as principal transmitters of shocks across all three regional systems. The research also highlights the crucial role of significant events, such as the Global Financial Crisis, the Chinese stock market crash, and the COVID-19 pandemic, when increasing the intensity of connectedness. The volatility transmission of the COVOL-gold pairing is comparatively weaker than the COVOL-stock and COVOL-oil pairs. At the same time, the US and Europe consistently transmit stronger shocks to global financial risk, contributing significantly to the global risk transmission channel, whereas, in contrast, China’s financial markets transmit less international uncertainty. Robustness testing verifies these findings.

Time-varying spillovers in high-order moments among cryptocurrencies

This study uses high-frequency (1-min) price data to examine the connectedness among the leading cryptocurrencies (i.e. Bitcoin, Ethereum, Binance, Cardano, Litecoin, and Ripple) at volatility and high-order (third and fourth orders in this paper) moments based on skewness and kurtosis. The sample period is from February 10, 2020, to August 20, 2022, which captures a pandemic, wartime, cryptocurrency market crashes, and the full collapse of a stablecoin. Using a time-varying parameter vector autoregressive (TVP-VAR) connectedness approach, we find that the total dynamic connectedness throughout all realized estimators grows with the time frequency of the data. Moreover, all estimators are time dependent and affected by significant events. As an exception, the Russia–Ukraine War did not increase the total connectedness among cryptocurrencies. Analysis of third- and fourth-order moments reveals additional dynamics not captured by the second moments, highlighting the importance of analyzing higher moments when studying systematic crash and fat-tail risks in the cryptocurrency market. Additional tests show that rolling-window-based VAR models do not reveal these patterns. Regarding the directional risk transmissions, Binance was a consistent net transmitter in all three connectedness systems and it dominated the volatility connectedness network. In contrast, skewness and kurtosis connectedness networks were dominated by Litecoin and Bitcoin and Ripple were net shock receivers in all three networks. These findings are expected to serve as a guide for portfolio optimization, risk management, and policy-making practices.