Mutual excitation between OECD stock and oil markets: A conditional intensity extreme value approach

We analyze the degree of mutual excitation that exists between extreme events across the stock markets of OECD member nations and the Brent and WTI crude oil markets. For this analysis, marked point process models are proposed which are able to capture the dynamics of the intensity of occurrence and comovement during periods of crisis. The results show a significant, negative interdependence between most OECD markets, especially those of the USA, Japan and France. These major oil importing countries display links between equity market losses and positive returns in both oil markets. However, positive interdependence is not observed between any of the OECD countries except for South Korea. The great advantage of this methodology is that, apart from using the size distribution of extreme events, it also uses the occurrence times of extreme events as a source of information. With this information, these models are better able to capture the stylized facts of extreme events in financial markets such as clustering behavior and cross-excitation.     

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.     

Systemic risk of Chinese financial institutions and asset price bubbles

Although there has not been a large-scale systemic crisis in China, high-risk financial events have occurred continuously in recent years. This research thus creatively analyzes the determinants of systemic risk for Chinese financial institutions from the view of asset price bubbles. First, we identify bubbles in the China stock and real estate markets on the basis of the generalized sup Augmented Dickey-Fuller (GSADF) model and explain the reasons for bubble formations according to the stage of China's economic development and policies implementation. At this stage, considering the differences in economic development levels of different cities, the real estate bubbles in the first, second and third tier cities and the whole country were innovatively identified. Second, on the basis of the DCG-GARCH-CoVaR model to measure the systemic risk of listed financial institutions in China and to classify institutions, the results show that the main source of such risk is the banking sector. Furthermore, by constructing regression models, stock market bubbles and real estate bubbles both positively correlate with systemic risk throughout the sample period. Meanwhile, the impact of bubbles on the systemic risk of different types of financial institutions was taken into account so that regulators prioritized different types of institutions with different characteristics when faced with decisions. Finally, we provide macro-prudential policy advice to regulators in order to weaken the impact of bubbles on financial stability to avoid systemic crises.     

Equity market contagion during the global financial crisis: Evidence from the world's eight largest economies

The global financial crisis (2007–2009) saw sharp declines in stock markets around the world, affecting both advanced and emerging markets. In this paper we test for the existence of equity market contagion originating from the US to advanced and emerging markets during the crisis period. Using a latent factor model, we provide strong evidence of contagion effects in both advanced and emerging equity markets. In the aggregate equity market indices, contagion from the US explains a large portion of the variance in stock returns in both advanced and emerging markets. However, in the financial sector indices we find less evidence of contagion than in the aggregate indices, and this is particularly the case for the advanced markets. The results suggest that contagion effects are not strongly related to high levels of global integration.     

Long memory in the Ukrainian stock market and financial crises

This paper examines persistence in the Ukrainian stock market during the recent financial crisis. Using two different long memory approaches (R/S analysis and fractional integration) we show that this market is inefficient and the degree of persistence is not the same at different stages of the financial crisis. Therefore trading strategies might have to be modified. We also show that data smoothing is not advisable in the context of R/S analysis.     

Global imbalances and dynamics of international financial markets

We provide an overview of the special issue “Global Imbalances and dynamics of international financial markets”. This special issue, which is associated with the 7th International Finance Conference, features research papers dealing with the impact of global imbalances, market complexity, and the impact of the recent global financial crisis on the conduct of monetary policies, financial market dynamics, financial stability, and risk management models.     

中国股票市场国际联动性研究

本文选取2000~2015年全球40支股票指数日收盘价,通过建立收益率网络和DCC MVGARCH模型波动率网络对中国股票市场国际联动性进行实证分析。研究表明,随着经济全球化的加深,全球股市收益率和波动率联动逐渐增强;全球金融危机和欧债危机期间,收益率联动网络具有小世界性;中国与全球股市长期处于割裂状态,但在全球金融危机期间与其他市场联系加强。在全球经济形势复杂多变的情况下,中国应针对性采取措施促进股市发展,以分享全球金融一体化利益。     

Did Vietnam stock market avoid the “contagion risk” from China and the U.S.? The contagion effect test with dynamic correlation coefficients

This paper examines the Vietnamese stock market with an extension of the recent investigation of risk contagion effects. Daily data spanning October 9, 2006–June 19, 2009 are sourced for the empirical validation of the risk contagion between the stock markets in Vietnam, China, and the U.S. To facilitate the validation of contagion effects with market related coefficients, this paper constructs a bivariable EGARCH model of dynamic condition correlation coefficients. First, we examine whether there are contagion effects when there is a financial crisis in the Vietnamese stock market. Next, we verify whether the contagion risk triggered by the crisis can affect the Vietnamese market and examine which market influences the Vietnamese market the most. We find that compared to the U.S. stock market, the Chinese stock market brings more contagion risk to the Vietnamese market, and these effects gain more significance after the sub-prime mortgage crisis.     

International stock market integration: Central and South Eastern Europe compared

We examine the international stock market comovements between Western Europe vis-à-vis Central (Czech Republic, Hungary and Poland) and South Eastern Europe (Croatia, Macedonia and Serbia) using multivariate GARCH models in the period 2006–2011. Comparing these two groups, we find that the degree of comovements is much higher for Central Europe. The correlation of South Eastern European stock markets with developed markets is essentially zero. An exemption to this regularity is Croatia, with its stock market displaying a greater degree of integration toward Western Europe recently, but still below the levels typical for Central Europe. All stock markets fall strongly at the beginning of the global financial crisis and we do not find that the crisis altered the degree of stock market integration between these groups of countries.     

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.     

Structural break in different stock index markets in China

This paper first presents a two-stage change point estimation approach in the framework of online analysis to detect the Chinese stock market abrupt variations during the period from 4 January 2005 to 10 December 2021. As a check, the pruned exact linear time (PELT) algorithm method is applied to detect structural changes in the framework of offline analysis in terms of all data. We select four representative indices in Chinese markets to find some important time-stamp tags. The results show that all indices can detect some common events, while the small-cap and small-mid-cap indices can identify local risks such as China’s market freezing. Besides, we find some events such as the global financial crisis and China’s market freezing can incur the inverse anomaly with higher volatility in lower reward.     

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.     

The impact of VIX on China’s financial market: A new perspective based on high-dimensional and time-varying methods

Based on the new perspective of high-dimensional and time-varying methods, this paper analyzes the contagion effects of US financial market volatility on China’s nine financial sub-markets. The results show evidence of non-linear Granger causality from the US financial volatility (VIX) to the China’s financial markets. Increased US financial volatility has a negative next-day impact on the stock, bond, fund, interest rate, foreign exchange, industrial product and agricultural product markets, and a positive next-day impact on the gold and real estate markets. US financial volatility has the greatest impact on industrial product market, following by stock, agricultural product, fund, real estate, bond, gold, foreign exchange, and interest rates. Major risk events such as the global financial crisis can cause an enhanced contagion effect of US financial volatility to China's financial markets. This paper supports the achievements of China's actions to prevent and resolve major financial risks in the period of the COVID-19 epidemic.     

EVT and tail-risk modelling: Evidence from market indices and volatility series

Value-at-Risk (VaR) has become the universally accepted risk metric adopted internationally under the Basel Accords for banking industry internal control, capital adequacy and regulatory reporting. The recent extreme financial market events such as the Global Financial Crisis (GFC) commencing in 2007 and the following developments in European markets mean that there is a great deal of attention paid to risk measurement and risk hedging. In particular, to risk indices and attached derivatives as hedges for equity market risk. The techniques used to model tail risk such as VaR have attracted criticism for their inability to model extreme market conditions. In this paper we discuss tail specific distribution based Extreme Value Theory (EVT) and evaluate different methods that may be used to calculate VaR ranging from well known econometrics models of GARCH and its variants to EVT based models which focus specifically on the tails of the distribution. We apply Univariate Extreme Value Theory to model extreme market risk for the FTSE100 UK Index and S&P-500 US markets indices plus their volatility indices. We show with empirical evidence that EVT can be successfully applied to financial market return series for predicting static VaR, CVaR or Expected Shortfall (ES) and also daily VaR and ES using a GARCH(1,1) and EVT based dynamic approach to these various indices. The behaviour of these indices in their tails have implications for hedging strategies in extreme market conditions.     

Extreme risk spillovers between crude palm oil prices and exchange rates

This study is the first attempt to examine the extreme risk spillovers between Malaysian crude palm oil (CPO) and foreign exchange currencies of the three largest CPO importers: India, the European Union and China throughout the global financial crisis. Using daily data of three currencies, CPO spot and futures from 2000 to 2018, our results show: First, before the crisis, the unexpected change in foreign exchange rates is the primary driver of risk spillover to the CPO market. Second, during the crisis, the extreme movement of CPO spot returns is dominant in the Malaysian exchange rates relative to the euro. Third, after the crisis, the spillover flows from the CPO market to the foreign exchange market. Overall, our findings show the importance of CPO pricing dynamics in mitigating foreign exchange risk over the crisis period. This paper contributes to the extant literature by recognizing the effect of risk spillover on the targeted foreign exchange rate for portfolio allocation.     

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.     

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.     

Herding behavior,market sentiment and volatility: Will the bubble resume?

This paper aims to investigate herding behavior and its impact on volatility under uncertainty. We apply a cross-sectional absolute deviation approach as well as Quantile Regression methods to capture the herding behavior in daily and monthly frequencies in US markets over several time-periods including the global financial crisis. In a novel attempt we modify the empirical CSAD herding modeling by introducing implied volatility as a measure of agent risk expectations. Our findings indicate that herding tends to be intense under extreme market conditions, as depicted in the upper high quantile range of the conditional distribution of returns. During crisis periods herding is observed at the beginning of the crisis and becomes insignificant towards the end. The US market herding behavior exhibits time-varying dynamic trading patterns that can be attributed e.g., to overconfidence or excessive “flight to quality” features, mostly observed in the aftermath of the global financial crisis. Moreover, implied volatility reveals asymmetric patterns and plays a key role in enforcing irrational behavior.     

How can investors build a better portfolio in small open economies? Evidence from Asia’s Four Little Dragons

This study investigates the role of hedging and portfolio design among stocks, exchange rates, and gold in small open economies (SOEs) from 4 January 2000 to 31 March 2020. We adopt the trivariate dynamic conditional correlation-fractionally integrated asymmetric power ARCH model and unconditional quantile regression model, and our findings show that the hedging role of the U.S. dollar (USD) and gold against stocks differs under regular and extreme market conditions. The USD can act as a powerful hedge asset for stocks in regular market periods. Moreover, during the global financial crisis and COVID-19 outbreak, the safe-haven effect of gold becomes stronger for almost all stocks, whereas the USD can serve as a strong safe haven against stock markets of Korea, Taiwan, and Singapore when stock returns are extremely low. In terms of portfolio designing, we find that adding the USD and gold to portfolios improves their hedging effectiveness, and the optimally weighted stock-USD-gold portfolio is the best portfolio strategy, irrespective of referring to return or risk.     

Switching generalized autoregressive score copula models with application to systemic risk

Recent financial disasters have emphasized the need to accurately predict extreme financial losses and their consequences for the institutions belonging to a given financial market. The ability of econometric models to predict extreme events strongly relies on their flexibility to account for the highly nonlinear and asymmetric dependence patterns observed in financial time series. In this paper, we develop a new class of flexible copula models where the dependence parameters evolve according to a Markov switching generalized autoregressive score (GAS) dynamics. Maximum likelihood estimation is performed using a two‐step procedure where the second step relies on the expectation–maximization algorithm. The proposed switching GAS copula models are then used to estimate the conditional value at risk and the conditional expected shortfall, measuring the impact on an institution of extreme events affecting another institution or the market. The empirical investigation, conducted on a panel of European regional portfolios, reveals that the proposed model is able to explain and predict the evolution of the systemic risk contributions over the period 1999–2015.