Are the interdependence characteristics of the US and Canadian energy equity sectors nonlinear and asymmetric?

Nonlinear, symmetric, and asymmetric dependence characteristics in energy equity sectors matter to portfolio investors and risk managers because of the risks and diversification opportunities they entail. Specifically, nonlinear dependence dynamics between assets are harder to predict, monitor, and manage, and can make investment positions go wrong unexpectedly. In this paper, we investigate whether the dependence dynamics of US and Canadian large-capitalized energy equity portfolios are nonlinear, symmetric, or asymmetric. We draw our results by implementing a robust copula approach based on time-varying parameter copulas and vine copula methods. Both time varying parameter and vine-copula methods indicate that the Canadian energy sector portfolio is driven by nonlinear negative tail asymmetric dependence during the global financial crisis and when the full sample period is employed. On the other hand, it displays nonlinear symmetric dependence during the oil price crisis, implying the need for close monitoring and rebalancing and a more continuous assessment of long investment positions. The US energy sector portfolio is driven by positive tail asymmetric dependence, and by symmetric dependence dynamics during crisis and non-crisis periods.     

Risk spillover analysis of China’s financial sectors based on a new GARCH copula quantile regression model

This study employs a new GARCH copula quantile regression model to estimate the conditional value at risk for systemic risk spillover analysis. To be specific, thirteen copula quantile regression models are derived to capture the asymmetry and nonlinearity of the tail dependence between financial returns. Using Chinese stock market data over the period from January 2007 to October 2020, this paper investigates the risk spillovers from the banking, securities, and insurance sectors to the entire financial system. The empirical results indicate that (i) three financial sectors contribute significantly to the financial system, and the insurance sector displays the largest risk spillover effects on the financial system, followed by the banking sector and subsequently the securities sector; (ii) the time-varying risk spillovers are much larger during the global financial crisis than during the periods of the banking liquidity crisis, the stock market crash and the COVID-19 pandemic. Our results provide important implications for supervisory authorities and portfolio managers who want to maintain the stability of China’s financial system and optimize investment portfolios.     

Testing for financial spillovers in calm and turbulent periods

In this paper, we investigate financial spillovers between stock markets during calm and turbulent periods. We explicitly define financial spillovers and financial contagion in accordance with the literature and construct statistical models corresponding to these definitions in a Markov switching framework. Applying the new testing methodology based on transition matrices, we find that spillovers from the US stock market to the UK, Japanese and German markets are more frequent when the latter markets are in a crisis regime. However, we reject the hypothesis of strong financial contagion from the US to the other markets.     

Economic policy uncertainty effects for forecasting future real economic activity

Recently introduced measures for economic policy uncertainty (EPU), included in the data from 1997 to 2016, have a role in forecasting out-of-sample values for future real economic activity for both the euro area and UK economies. The inclusion of EPU measures, either for the US, the UK or for overall European economies, improves the forecasting ability of models based on standard financial market information, especially for the period before the 2008 global crisis. However, during and after the crisis period, the slope of the yield curve and excess stock market returns improves the out-of-sample forecast performance the most compared to an AR-benchmark model. Hence, the EPU information is important in times of normal business cycles, but might contain similar information components to financial market return variables during turbulent crisis periods in the financial markets and in the real economy.     

Upside/Downside spillovers between oil and Chinese stock sectors: From the global financial crisis to global pandemic

This paper examines the effects of the COVID-19 outbreak, recent oil price fall, and both global and European financial crises on dependence structure and asymmetric risk spillovers between crude oil and Chinese stock sectors. Using time-varying symmetric and asymmetric copula functions and the conditional Value at Risk measure, we provide evidence of positive tail dependence in most sectors using copula and conditional Value-at-Risk techniques. We can see the average dependence between oil and industries during the oil crisis. Moreover, we find strong evidence of bidirectional risk spillovers for all oil-sector pairs. The intensity of risk spillovers from oil to all stock sectors varies across sectors. The risk spillovers from sectors to oil are substantially larger than those from oil to sectors during COVID-19. Furthermore, the return spillover is time varying and sensitive to external shocks. The spillover strengths are higher during COVID-19 than financial and oil crises. Finally, oil do not exhibit neither hedge nor safe-haven characteristics irrespective of crisis periods.     

Extremal Dependence in International Output Growth: Tales from the Tails

This article explores the comovement of the economic activity of several OECD countries during periods of large negative and positive growth. Extremal dependence measures are here applied to assess the degree of cross‐country tail dependence of output growth rates. Our main empirical findings are: (i) cross‐country tail dependence is much stronger during periods of large negative growth, than during the ones of large positive growth; (ii) cross‐country growth is asymptotically independent; (iii) cross‐country tail dependence is considerably stronger than the one arising from a Gaussian dependence model. In addition, our results suggest that, among the typical determinants for explaining international output growth synchronization, only economic specialization similarity seems to play a role during such extreme periods.     

Market risks that change domestic diversification benefits

We study the options-implied market risks and correlations to identify factors that affect U.S. stock correlations during 2007–2018. We discover that U.S. stock- and bond-market uncertainty, equity tail risk, European equity risk, and global credit risk are dominant contributors to changing correlations. While correlations rise universally with rising U.S. and European stock-market uncertainty, other market risks show diverging effects on correlations in crisis and non-crisis periods. Rising equity tail risk and global credit risk raise correlations in crisis times. Our results disentangle the risks of stock and bond markets that change the domestic stock diversification benefits.     

Analysing contagion and bailout effects with copulae

In this paper, a new methodical framework that combines elements of event studies and copula methodology is proposed in the context of the analysis of bank contagion. Furthermore, to the best knowledge of the author, this paper is the first one to analyse changes in the dependence structure of banks around bailout announcements. The results of the empirical study show that significant contagion effects could be detected both in the German banking sector after the onset of the subprime crisis as well as in the Japanese banking sector in the mid-nineties. I find that announcements of crisis at struggling banks induce a significant increase of lower tail dependence in the banking sector. The analysed bailouts and rescue measures by the central bank proved to be effective in reducing this increased lower tail dependence while increasing tail independence of bank stock returns at the same time. In both data samples, I find that the bailout announcements did not simply restore the pre-crisis dependence structure, but rather only decreased the likelihood of a joint crash of bank stocks without increasing the chances of a joint boom.     

Dependence structures between Chinese stock markets and the international financial market: Evidence from a wavelet-based quantile regression approach

In this study, we investigate the dependence structures between six Chinese stock markets and the international financial market including possible safe haven assets and global economic factors under different market conditions and investment horizons. The research is conducted by combining a quantile regression approach with a wavelet decomposition analysis. Although we find little or insignificant dependence under short investment horizons, we detect the strong asymmetric dependence of oil prices and the US dollar index on the six Chinese stock markets in the medium and long terms. Moreover, not only is crude oil not a safe haven, it may damage Chinese stock markets as it increases over the long term, even in bull markets. Meanwhile, appreciation of the US dollar (depreciation of RMB) damages (boosts) Chinese stock markets during bull (bear) market conditions under long investment horizons. Moreover, we find that VIX (volatility index)-related derivatives may serve as good risk management tools under any market condition, while gold is a safe haven asset only during crisis periods.     

Multiscale features of extreme risk spillover networks among global stock markets

This paper studies the multiscale features of extreme risk spillover among global stock markets over various time–frequency horizons. We propose multiscale risk spillover indexes based on GARCH-EVT-VaR, maximal overlap discrete wavelet transform method, and forecast-error-variance decompositions. We further construct multiscale risk spillover networks to visualize risk spillovers at different scales. Our findings show that the US and the UK are detected as the centers of risk spillovers, while Asian stock markets are mainly at the edge of the risk spillover network. The topological properties are unevenly spread over each time scale. The network tends to be closer not only at the short-term scale but also during the financial crisis. For individual features, the US and the UK are super-spreaders of risk spillover at each time scale, while most developing markets mainly act as absorbers. The role of European stock markets is complex at different scales.     

Wavelet coherence analysis of returns,volatility and interdependence of the US and the EU money markets: Pre & post crisis

This research analyse the US and the EU money markets interdependence from 2004 to 2018. The study explains to what extent the volatility of the chosen money markets instruments in two regions is inter-correlated before, during and after the financial crisis of 2008. We apply the econometric analysis and estimate time-series models of class GARCH to study the historical dynamics of interbank rates and bond returns. The study demonstrates that correlation between returns of analogous money market instruments in the EU and US is not stable over time. We find that correlation rises in periods when countries are exposed to the same external shocks as global financial crisis. Wavelet coherence analysis suggests that investors do not get any advantages of portfolio diversification investing only in US treasuries with different maturities for more than 256 days and do not get any advantages at all investing only in European bonds.     

An analysis of dependence between Central and Eastern European stock markets

We examine the dependence structure between four Central and Eastern European (CEE) stock markets (Czech Republic, Hungary, Poland and Romania) using static and dynamic copula functions with different forms of tail dependence. We find evidence of positive dependence between all CEE stock markets, although this dependence is stronger between the Hungarian, Czech and Polish markets than between these markets and the Romanian market. We also find evidence of symmetric tail dependence, although not for the Hungarian and Czech markets. The dependence is time-varying and intensified after the onset of the recent global financial crisis. These results confirm that CEE stock markets are gradually coupling, a fact that has risk management implications for policymakers and investors.     

Great Places to Work®: Resilience in Times of Crisis

We study the resilience of the “100 Best Companies to Work for in America” in times of financial crisis by analyzing their long‐term financial performance. Apart from implementing methods that tackle the statistical problems of stock returns, we use a conditional model to measure financial performance in periods of market growth (bull markets) and market downturn (bear markets). We find that best places to work are indeed resilient in times of crisis since neither their financial performance nor their systematic risk are affected during bear markets: top companies continue to outperform the market during periods of crisis, and the performance of lower‐ranked great workplaces does not deteriorate. Moreover, we find that previous studies were overestimating performance, and only great workplaces on the top half of the rankings exhibit positive excessive returns. © 2015 Wiley Periodicals, Inc.     

To What Extent Does the Interest Burden Affect Firm Survival? Evidence from a Panel of UK Firms during the Recent Financial Crisis

Using a panel of mainly unquoted UK firms over the period 2000–09, we document a significant effect of changes in the interest burden from debt‐servicing on firm survival. The effect is found to be stronger during the recent financial crisis compared with more tranquil periods. Furthermore, the survival chances of bank‐dependent, younger, and non‐exporting firms are most affected by changes in the interest burden, especially during the crisis. Our results are robust to using different estimation methods and different interest burden measures They suggest that one way for policymakers to mitigate the effects of financial crises by limiting firm failures would be to prevent financing costs from rising, especially for those firms more likely to face liquidity constraints.     

Identifying contagion

Identifying contagion effects during periods of financial crisis is known to be complicated by the changing volatility of asset returns during periods of stress. To untangle this we propose a GARCH (generalized autoregressive conditional heteroskedasticity) common features approach, where systemic risk emerges from a common factor source (or indeed multiple factor sources) with contagion evident through possible changes in the factor loadings relating to the common factor(s). Within a portfolio mimicking factor framework this can be identified using moment conditions. We use this framework to identify contagion in three illustrations involving both single and multiple factor specifications: to the Asian currency markets in 1997–1998, to US sectoral equity indices in 2007–2009 and to the CDS (credit default swap) market during the European sovereign debt crisis of 2010–2013. The results reveal the extent to which contagion effects may be masked by not accounting for the sources of changed volatility apparent in simple measures such as correlation.     

Modelling contagion of financial crises

Inspired by the empirical findings, we include international traders to capture linkage between markets and propose a two-market heterogeneous agents model to simulate financial crisis with contagion effect. This paper manages to calibrate sudden crash behavior of US and UK stock markets during “Black Monday” of 1987 besides smooth crisis and disturbing crisis categorized in literature. It is implied that financial crisis and its contagion could be endogenous, which supports a scenario of over-valuation causing a financial crisis. In addition, the model shows that financial system could be fragile in which small shock(s) hitting individual market’s fundamental could cause financial crisis spreading to the other market. This also supports a scenario of external shock triggering a financial crisis. Lastly, to demonstrate the relevance of our model to financial markets, we manage to match typical stylized facts, especially cross-correlation which is exclusive to a multiple-market case.     

Extreme US stock market fluctuations in the wake of 9/11

We apply extreme value analysis to US sectoral stock indices in order to assess whether tail risk measures like value‐at‐risk and extremal linkages were significantly altered by 9/11. We test whether semi‐parametric quantile estimates of ‘downside risk’ and ‘upward potential’ have increased after 9/11. The same methodology allows one to estimate probabilities of joint booms and busts for pairs of sectoral indices or for a sectoral index and a market portfolio. The latter probabilities measure the sectoral response to macro shocks during periods of financial stress (so‐called ‘tail‐βs’). Taking 9/11 as the sample midpoint we find that tail‐βs often increase in a statistically and economically significant way. This might be due to perceived risk of new terrorist attacks. Copyright © 2008 John Wiley & Sons, Ltd.     

Is the government in danger of inflating a housing bubble?

Despite very strong inflation prior to the financial crisis, the correction in UK house prices has been relatively modest and much smaller than those in countries such as the US, Ireland and Spain. The relative resilience of the UK market appears to be a reflection of very tight supply which has effectively put a floor under prices.…     

Modeling Financial Sector Joint Tail Risk in the Euro Area

We develop a novel high‐dimensional non‐Gaussian modeling framework to infer measures of conditional and joint default risk for numerous financial sector firms. The model is based on a dynamic generalized hyperbolic skewed‐t block equicorrelation copula with time‐varying volatility and dependence parameters that naturally accommodates asymmetries and heavy tails, as well as nonlinear and time‐varying default dependence. We apply a conditional law of large numbers in this setting to define joint and conditional risk measures that can be evaluated quickly and reliably. We apply the modeling framework to assess the joint risk from multiple defaults in the euro area during the 2008–2012 financial and sovereign debt crisis. We document unprecedented tail risks between 2011 and 2012, as well as their steep decline following subsequent policy actions. Copyright © 2016 John Wiley & Sons, Ltd.     

The extension from independence to dependence between jump frequency and jump size in Markov-modulated jump diffusion models

We set out in this study to investigate the relationship between jump frequency and jump size for the 30 component stocks of the Dow Jones Industrial Average (DJIA) index, extending the Markov-modulated jump diffusion model from independence to dependence between jump frequency and jump size. We propose an estimation method for the parameters of the Markov-modulated jump diffusion model based upon dependence between jump frequency and size, with our results indicating that when abnormal events occur, the Markov-modulated jump diffusion models with both state-independent jump sizes (MJMI) and state-dependent jump sizes (MJMD) outperform the pure jump diffusion (JD) model in terms of capturing the risks of jump frequency and jump size. Based upon Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC), our results further indicate that for 23 of the component stocks, the MJMD model may be better suited, as compared to the MJMI model. Finally, our empirical observations reveal that the behavior of jump risks in the stock markets, including jump frequency and jump size, is not independent, since these phenomena are found to coincide during both financial crisis periods and stock market crashes, with the largest jump size risks, during certain periods, being accompanied by either systematic or idiosyncratic risks.