MENA stock market volatility persistence: Evidence before and after the financial crisis of 2008

The global financial crisis began with a financial meltdown in the United States in early 2008 and then it had spread to the rest of the world. In this paper we test whether the MENA equity market volatility presents a different behavior before and after the financial crisis of 2008. Using long range dependence techniques we test for long memory in the returns, absolute and squared returns of the MENA equity markets. We subject the series to unit root tests that allow for structural breaks and use the Bai and Perron (1998, Econometrica, 66, 47; 2003a, J. Appl. Econometrics, 6, 72; 2003b, Econometrics J., 18, 1) to test for multiple breaks in the mean returns. The results indicate that the volatility measures represented by absolute and squared returns show evidence of long memory for the full and subsample periods, while the returns show a weak evidence of long memory. Considering the shift dates and corresponding to the 2008 financial crisis, the returns and volatility measures display less evidence of long memory in the after crisis period as opposed to the before crisis period. The change in the returns and volatility dynamics of these markets was due to financial and economic conditions that took place in the MENA region after the crisis.     

Dynamic correlations and volatility linkages between stocks and sukuk: Evidence from international markets

An understanding of volatility and co-movements in financial markets is important for portfolio allocation and risk management practices. The current financial crisis caused a shrinkage in values of most assets, an increased volatility and a threat to the survival of several institutional investors. Managing risks and returns within the classic portfolio theory, when correlations across securities soar, is increasingly challenging. In this paper, we investigate the volatility behavior and the co-movements between sukuk and international stock indexes. Symmetric multivariate GARCH models with dynamic conditional correlations (DCC) were estimated under Student-t distribution. We provide evidence of high correlations between sukuk and US and EU stock markets, without finding the well-known flight to quality behavior affecting Islamic bonds. We also show that volatility linkages between sukuk and regional market indexes are higher during financial crisis. We argue that investors could obtain diversification benefits including sukuk in a well-diversified equity portfolio, given their lower volatility compared to equity. But higher volatility linkages and dynamic correlations during financial crises show that they are hybrid instruments between bonds and equity. Our findings are relevant for institutional investors and asset managers that include Islamic bonds in a diversified portfolio.     

Linkages between Indian and US financial markets: impact of global financial crisis and Eurozone debt crisis

This paper examines inter-linkages between Indian and US equity, foreign exchange and money markets using the vector autoregressive-multivariate GARCH-BEKK framework. We investigate the impact of global financial crisis (GFC) and Eurozone debt crisis (EZDC) on the conditional volatility and conditional correlation estimates derived from the multivariate GARCH model for Indian and US financial markets. Our results indicate that there is significant bidirectional causality-in-mean between the Indian stock market returns and the Rs./USD market returns, and significant unidirectional causality-in-mean from the US stock market returns to the Indian stock market returns. As regards volatility spillovers, we find that volatility in the Indian stock market rises in response to domestic as well as US financial market shocks but Indian financial market shocks do not impact the US markets. Further, impact of the recent crisis episodes on the covariance matrix is found to be significant. We find that volatility in the Indian and US financial markets significantly amplified during GFC. The conditional correlations across asset markets were significantly accentuated in the wake of the two crisis episodes. The impact of GFC on cross-market conditional correlations is higher for majority of the asset market pairs in comparison to the EZDC.     

Local and spillover shocks in implied market volatility: evidence for the U.S. and Germany

The occurrence and the transmission of large shocks in international equity markets is of essential interest to the study of market integration and financial crises. To this aim, implied market volatility allows to monitor ex ante risk expectations in different markets. We investigate the behavior of implied market volatility indices for the U.S. and Germany under a straightforward mean reversion model that allows for Poisson jumps. Our empirical findings for daily data in the period 1992 to 2002 provide evidence of significant positive jumps, i.e. situations of market stress with positive unexpected changes in ex ante risk assessments. Jump events are mostly country-specific with some evidence of volatility spillover. Analysis of public information around jump dates indicates two basic categories of events. First, crisis events occurring under spillover shocks. Second, information release events which include three subcategories, namely—worries about as well as actual—unexpected releases concerning U.S. monetary policy, macroeconomic data and corporate profits. Additionally, foreign exchange market movements may cause volatility shocks.     

Analysing financial contagion and asymmetric market dependence with volatility indices via copulas

This paper explores the cross-market dependence between five popular equity indices (S&P 500, NASDAQ 100, DAX 30, FTSE 100, and Nikkei 225), and their corresponding volatility indices (VIX, VXN, VDAX, VFTSE, and VXJ). In particular, we propose a dynamic mixed copula approach which is able to capture the time-varying tail dependence coefficient (TDC). The findings indicate the existence of financial contagion and significant asymmetric TDCs for major international equity markets. In some situations, although contagion cannot be clearly detected by stock index movements, it can be captured by dependence between volatility indices. The results imply that contagion is not only reflected in the first moment of index returns, but also the second moment, i.e. the volatility. Results also show that dependence between volatility indices is more easily influenced by financial shocks and reflects the instantaneous information faster than the stock market indices.     

How useful is the Marginal Expected Shortfall for the measurement of systemic exposure? A practical assessment

We explore the practical relevance from a supervisor’s perspective of a popular market-based indicator of the exposure of a financial institution to systemic risk, the Marginal Expected Shortfall (MES). The MES of an institution can be defined as its expected equity loss when the market itself is in its left tail. We estimate the dynamic MES recently proposed by Brownlees and Engle (2012) for a panel of 68 large US banks over the last decade and a half. Running panel regressions of the MES on bank characteristics, we first find that the MES can be roughly rationalized in terms of standard balance-sheet indicators of bank financial soundness and systemic importance. We then ask whether the cross section of the MES can help to identify ex ante, i.e. before a crisis unfolds, which institutions are more likely to suffer the most severe losses ex post, i.e. once it has unfolded. Unfortunately, using the 2007–2009 crisis as a natural experiment, we find that some standard balance-sheet ratios are better able than the MES to predict large equity losses conditionally to a true crisis.     

Research on China's financial systemic risk contagion under jump and heavy-tailed risk

To accurately measure the dynamic characteristics of systemic risk contagion under the impact of extreme financial events, we construct a research framework that analyzes the contagion dynamics of systemic risk under extreme risk impact from the perspectives of both time and space. Based on the macro-jump CCA method, this paper extracts the heterogeneous volatility sequence of financial industries considering the thick tail of the distribution of financial assets returns. Then, the dynamic variation of systemic risk in the financial sectors is characterized from the time dimension. The volatility spillover network method is used to examine the spillover contagion of systemic risk among financial system sectors from the spatial dimension. Empirical studies have found that when considering the risk contagion level, the capital market service sector plays a risk‑leading role, followed by the currency service sector and the insurance sector. The measurement indicators that consider the jump risk and the tail risk have good early warning effects on extreme financial events. Seen from the spatial direction of risk spillover, the real estate sector exhibits the most obvious risk spillover effect on other sectors and can be regarded as the source of systemic risk, which suggests differentiated regulation.     

Illiquidity and volatility spillover effects in equity markets during and after the global financial crisis: An MEM approach

Even though the volatility spillover effects in global equity markets have been documented extensively, the transmission of illiquidity across national borders has not. In this paper, we propose a multiplicative error model (MEM) for the dynamics of illiquidity. We empirically study the illiquidity and volatility spillover effects in eight developed equity markets during and after the recent financial crisis. We find that equity markets are interdependent, both in terms of volatility and illiquidity. Most markets show an increase in volatility and illiquidity spillover effects during the crisis. Furthermore, we find volatility and illiquidity transmission are highly relevant. Illiquidity is a more important channel than volatility in propagating the shocks in equity markets. Our results show an overall crucial role for illiquidity in US markets in influencing other equity markets' illiquidity and volatility. These findings are of importance for policy makers as well as institutional and private investors.     

Tail dependence and indicators of systemic risk for large US depositories

In this study, we investigate the extreme loss tail dependence between stock returns of large US depository institutions. We find that stock returns exhibit strong loss dependence even in their limiting joint extremes. Motivated by this result, we derive extremal dependence-based systemic risk indicators. The proposed systemic risk indicators reflect downturns in the US financial industry very well. We also develop a set of firm-level average extremal dependence measures. We show that these firm-level measures could have been used to identify the firms that were more vulnerable to the 2007–2008 financial crisis. Additionally, we explore the performance of selected systemic risk indicators in predicting the crisis performance of large US depository institutions and find that the average stock return correlations are also good predictors of crisis period returns. Finally, we identify factors predictive of extremal dependence for the US depository institutions in a panel regression setting. Strength of extremal dependence increases with asset size and similarity of financial fundamentals. On the other hand, strength of extremal dependence decreases with capitalization, liquidity, funding stability and asset quality. We believe the proposed indicators have the potential to inform the prudential supervision of systemic risk.     

Measuring systemic risk contribution of global stock markets: A dynamic tail risk network approach

Measuring the systemic risk contribution (SRC) of country-level stock markets helps understand the rise of extreme risks in the worldwide stock system to prevent potential financial crises. This paper proposes a novel SRC measurement based on quantifying tail risk propagation's domino effect using ΔCoVaR and the cascading failure network model. While ΔCoVaR captures the tail dependency structure among stock markets, the cascading failure network model captures the nonlinear dynamic characteristics of tail risk contagion to mimic tail risk propagation. As an illustration, we analyze 73 markets' SRCs using a daily closing price dataset from 1990.12.19 to 2020.9.8. The validity test demonstrates that our method outperforms seven classic methods as it helps early warning global financial crises and correlates to many systemic risk determinants, e.g., the market liquidity, leverage, inflation, and fluctuation. The empirical results identify that Southeast European markets have higher SRCs with time-varying and momentum features corresponding to significant financial crisis events. Besides, it needs attention that South American and African markets have displayed increasing risk contributions since 2018. Overall, our results highlight that considering tail risk contagion's dynamic characteristics helps avoid underestimating SRC and supplement a “too cascading impactive to fail” perspective to improve financial crisis prevention.     

Do measures of systemic risk predict U.S. corporate bond default rates?

Using univariate and multivariate Mixed Data Sampling (MIDAS) and LASSO estimation methodologies, we explore whether the U.S. annual average corporate bond default rate can be predicted by 12 monthly systemic risk measures proposed in the literature. We find that nearly all of the systemic risk indicators have predictive power for the default rate. Granger causality tests based on multivariate mixed frequency VAR models further support this conclusion. On the basis of MIDAS models, we illustrate that five of these indicators are able to forecast out-of-sample the 2009 corporate default crisis. Using a LASSO multivariate model, it is further shown that the systemic risk indicators can forecast out-of-sample both the 2009 default rate and the default rates during the buildup before the crisis and in the aftermath of the crisis. Institution-specific and volatility systemic risk measures are the most relevant for modeling U.S. corporate bond default rates, with the Conditional VaR measure of Adrian and Brunnermeier (2016) exhibiting the best performance.     

Industry characteristics and financial risk contagion

This article proposes a new measure of tail risk spillover: the conditional coexceedance (CCX), defined as the number of joint occurrences of extreme negative returns in an industry, conditional on an extreme negative return in the financial sector. The empirical application provides evidence of significant volatility and tail risk spillovers from the financial sector to many real sectors in the U.S. economy from 2001 to 2011. These spillovers increase in crisis periods. The CCX in a given sector is positively related to its amount of debt financing and negatively related to its valuation and investment. Therefore, real economy sectors—which require relatively high debt financing and whose value and investment activity are relatively lower—are prime candidates for stock price volatility and depreciation in the wake of a financial sector crisis. Evidence also suggests that the higher the industry’s degree of competition, the stronger the tail risk spillover from the financial sector.     

Economic evaluation of asymmetric and price range information in gold and general financial markets

Gold is widely perceived as a good diversification or safe haven tool for general financial markets, especially in market turmoil. To fully understand the potential, this study constructs an asymmetric multivariate range-based volatility model to investigate the dependence and volatility structures of gold, stock, and bond markets and further to compare the difference between the financial crisis and post-financial crisis periods. We find a striking explanatory ability to volatility structures provided by the price range information and significant evidence of asymmetric dependence across gold, stock, and bond markets. We implement an asset-allocation strategy incorporating asymmetric dependence and price range information to explore their economic importance. The out-of-sample results show that between 35 and 517 basis points and between 90 and 1111 basis points are earned annually when acknowledging asymmetric dependence and price range information, respectively. These economic benefits are inversely related to the level of investors’ risk aversion and are particularly significant in the period of the global financial crisis.     

Financial crisis and extreme market risks: Evidence from Europe

This study draws attention to the proliferation of extreme risks in financial markets prior to and during the course of the recent global financial crisis. It examines the level of such “tail” risks in selected equity, interbank lending and foreign exchange markets in selected EU Member States in relation to the United States. The extent of tail risks is assessed by applying general error distribution (GED) parameterization in GARCH volatility tests of the examined variables. The empirical tests prove that extreme market risks were pronounced across all of the examined European financial markets throughout the crisis. They were also significant prior to the crisis outbreak. The analyzed interbank lending markets exhibited more extreme volatility outbursts than the equity and foreign exchange markets did.     

系统性金融风险文献综述:现状、发展与展望

防范风险是我国金融业的永恒主题。如何有效应对金融系统的异常波动、缓释国际市场的外部冲击、精准处置重点领域风险,仍将是我国“十四五”期间的重要任务。本文分别基于系统性金融风险的有效测度、传染溢出、驱动因素、前瞻预测,以及其与宏观经济的相互作用关系、风险调控政策与政策的有效性、监管理念发展等不同视角,对272篇国内外顶级(权威)文献进行了全面、深入的梳理与总结,并展望该领域的重点研究方向,从而为我国构建金融风险防控长效机制、完善“双循环”新格局下的金融监管体系提供参考,以牢牢守住不发生系统性金融风险的底线,推动经济社会高质量发展。     

系统性风险与企业财务危机预警——基于前沿机器学习的新视角

本文采用Logit回归模型以及随机森林模型、梯度提升模型等前沿机器学习方法,深入考察系统性风险指标对我国企业财务危机的预测能力。结果表明,系统性风险对中下游企业的财务危机具有显著的预测能力,而基于因子分析构建的系统性风险指标,结合随机森林模型可取得更好的预测效果。本文进一步区分财务危机的不同成因并发现,基于随机森林模型和Logit回归模型的预测框架能够对我国大多数财务危机事件进行有效预警。在此基础上,本文对我国上市企业监管提出相关建议,从而为完善金融风险处置机制提供一定参考。     

The Core,Periphery, and Beyond: Stock Market Comovements among EU and Non‐EU Countries

Using linear and nonlinear correlations, copulas, quantile dependence and lower tail dependence, we find that (1) equity markets of the advanced European Union (EU) countries comove more closely with each other than with the peripheral economies, (2) comovements with non‐EU countries are lower, (3) relative comovement structure before, during, and after the global financial crisis has been very stable, and (4) the level of comovements remained virtually the same between the crisis and post‐crisis periods. Our results are robust to controlling for Fama‐French, U.S. and global risk factors, as well as monetary policy, market interest rates, exchange rates, and uncertainty.     

The effect of exchange rate variability on US shareholder wealth

We examine the relationship between financial crisis exchange rate variability and equity return volatility for US multinationals. Empirical analysis of the major financial crises of the last decades reveals that stock return variability increases significantly in the aftermath of a crisis, even relative to the increase in stock return volatility for other firms belonging to the same industry and market capitalization class. In conjunction with this increase in total volatility, there is also an increase in stock market risk (β) for multinational firms. Moreover, trade and service oriented industries appear to be particularly sensitive to these changing exchange rate conditions.     

European equity markets volatility spillover: Destabilizing energy risk is the new normal

While energy risk is increasingly recognized as a systemic risk, there is limited comprehensive analysis of the risk propagation in regional contexts. In this study, we examine oil and natural gas price changes and shocks in relation to equity market returns and volatility for 24 European Economic Area (EEA) countries. In addition to traditional panel regressions, we also deploy the Diebold-Yilmaz (2014) spillover index for a closed network analysis. We differentiate in the cross-section across the core EU block, PIIGS countries, EU enlargement countries joining after 2004, and other non-EU countries, to provide insights into the ongoing debates on the European energy market stability. While we find evidence of the manifestation of energy risk throughout the sample period, we find that until 2019 the primary sources of volatility spillover in the EEA economic network arose from economic or political uncertainty. Energy risks, measured by large crude oil and natural gas price shocks also significantly contributed to equity market volatility, with increasing volatility risk arising from natural gas, a green labelled energy source after 2019. Last, we show that CEEC equity markets are more sensitive to oil and natural gas price shocks when domestic currencies depreciate against the Euro.     

Multivariate FIAPARCH modelling of financial markets with dynamic correlations in times of crisis

This paper applies the vector AR-DCC-FIAPARCH model to eight national stock market indices' daily returns from 1988 to 2010, taking into account the structural breaks of each time series linked to the Asian and the recent Global financial crisis. We find significant cross effects, as well as long range volatility dependence, asymmetric volatility response to positive and negative shocks, and the power of returns that best fits the volatility pattern. One of the main findings of the model analysis is the higher dynamic correlations of the stock markets after a crisis event, which means increased contagion effects between the markets. The fact that during the crisis the conditional correlations remain on a high level indicates a continuous herding behaviour during these periods of increased market volatility. Finally, during the recent Global financial crisis the correlations remain on a much higher level than during the Asian financial crisis.