Sources of time varying return comovements during different economic regimes: evidence from the emerging Indian equity market

We study the economic and non-economic sources of stock return comovements of the emerging Indian equity market and the developed equity markets of the US, UK, Germany, France, Canada and Japan. Our findings show that the probability of extreme comovements in the economic contraction regime is relatively higher than in the economic expansion regime. We show that international interest rates, inflation uncertainty and dividend yields are the main drivers of the asymmetric return comovements. Findings reported in the paper imply that the impact of interest rates and inflation on return comovements could be used for anticipating financial contagion and/or spillover effects. This is particularly critical since during extreme market conditions, the tail return comovements can potentially reveal critical information for active portfolio management

Linkages between Shanghai and Hong Kong stock indices

This paper examines the dynamics of the linkages between Shang- hai and Hong Kong stock indices. While the volatility linkage is anal- ysed by a multivariate GARCH framework, the linkage of returns is examined using a copula approach. Eight different copula functions are applied in this study including two time-varying copulas which capture the time varying process of the linkage. The results show sig- nificant tail dependence of the returns in the two markets.

Pricing bivariate option under GARCH-GH model with dynamic copula : application for Chinese market

This paper develops the method for pricing bivariate contingent claims under General Autoregressive Conditionally Heteroskedastic (GARCH) process. In order to provide a general framework being able to accommodate skewness, leptokurtosis, fat tails as well as the time varying volatility that are often found in financial data, generalized hyperbolic (GH) distribution is used for innovations. As the association between the underlying assets may vary over time, the dynamic copula approach is considered. Therefore, the proposed method proves to play an important role in pricing bivariate option. The approach is illustrated for Chinese market with one type of better-of-two-markets claims : call option on the better performer of Shanghai Stock Composite Index and Shenzhen Stock Composite Index. Results show that the option prices obtained by the GARCH-GH model with time-varying copula differ substantially from the prices implied by the GARCH-Gaussian dynamic copula model. Moreover, the empirical work displays the advantage of the suggested method.Call-on-max option, GARCH process, generalized hyperbolic (GH) distribution, normal inverse Gaussian (NIG) distribution, copula, dynamic copula.

Pricing bivariate option under GARCH-GH model with dynamic copula: application for Chinese market

This paper develops the method for pricing bivariate contingent claims under General Autoregressive Conditionally Heteroskedastic (GARCH) process. In order to provide a general framework being able to accommodate skewness, leptokurtosis, fat tails as well as the time varying volatility that are often found in financial data, generalized hyperbolic (GH) distribution is used for innovations. As the association between the underlying assets may vary over time, the dynamic copula approach is considered. Therefore, the proposed method proves to play an important role in pricing bivariate option. The approach is illustrated for Chinese market with one type of better-of-two-markets claims : call option on the better performer of Shanghai Stock Composite Index and Shenzhen Stock Composite Index. Results show that the option prices obtained by the GARCH-GH model with time-varying copula differ substantially from the prices implied by the GARCH-Gaussian dynamic copula model. Moreover, the empirical work displays the advantage of the suggested method.Call-on-max option - GARCH process - generalized hyperbolic (GH) distribution - normal inverse Gaussian (NIG) distribution - copula - dynamic copula

A new Copula-CoVaR approach incorporating the PSO-SVM for identifying systemically important financial institutions

The effective identification of systemically important financial institutions (SIFIs) is key to preventing and resolving systemic financial risks; thus, it is of great research significance for emerging countries to supervise SIFIs and manage systemic financial risks. Since traditional research on identifying SIFIs does not consider emerging machine learning models, it is difficult to properly fit the characteristics of actual financial institutions’ asset distribution. This paper proposes a new method for measuring SIFIs, integrating the PSO-SVM model into the Copula-CoVaR model. This new PSO-SVM-Copula-CoVaR model is meant to evaluate China’s SIFIs based on the publicly traded price data of Chinese listed financial institutions. The empirical results show that, compared with the traditional parameter method (GARCH model) and the nonparametric method (kernel density estimation), the marginal distribution estimation method using the PSO-SVM method can better fit the distribution of an institution’s financial asset return sequence. That is, the model proposed in this paper helps regulatory authorities improve the list of SIFIs more reasonably and implement effective regulatory measures

Estimating portfolio value at risk by a conditional copula approach in BRICS countries

Abstract : This thesis used daily log returns of indices of BRICS countries from the period of March 11th 2013 to May 16th 2017. Its main focus was to estimate the value at risk (VaR) of a portfolio of the BRICS financial markets using a conditional copula approach. A useful starting point was to apply the model of AR (1)-GARCH (1,1) with t-distribution and AR (1)-GARCH (1,1), using returns of the normal errors for the marginal distribution models in the copula framework. Two copulas, the normal and the symmetric Joe Clayton (SJC) copulas, were estimated as both constant and time-varying. The log likelihood of the time-varying copula was significantly more suitable than the constant copula. The comparison of the performance of the copula models to the benchmark AR (1)-GARCH (1,1) was done using the Christoffersen test. The 99% VaR appeared fairly accurate, suggesting that the VaR models were dependable. The standard level of comparison AR (1)-GARCH (1,1) did not perform well compared to the SJC copula; i.e. the time-varying SJC copula performed better than the benchmark model. The time-varying SJC copula model used to estimate the portfolio VaR also showed a minimum number of exceptions in the back-test. This copula thus meets regulatory capital requirement for investors as stipulated in Basel II.M.Com. (Financial Economics

Modelling cross-market linkages between global markets and China’s A-, B- and H-shares

One of the biggest challenges in quantifying joint risk and forming effective policies in financial management and investment strategies is to fully understand the characteristics of market associations in low and high volatility periods. Market interdependence, therefore, is a hot topic that has received interest from academics and industry experts, especially since the Asian Financial Crisis in 1997. China, being the world’s second-largest economy, has been the centre of many studies investigating stock market dependencies. While China has three major share types, namely A-, B- and H-shares, with different market players, market characteristics and operating efficiency, the number of studies on each of these share types remains conservative in comparison to the vast literature on the financial modelling of market interdependencies. Given the need for a more comprehensive understanding of the influence between these share types and other global markets, especially during market turbulences, this thesis examines the cross-market linkages between A-, B- and H-shares in China and several major emerging and advanced markets from 2002 to 2017, which is divided into two non-crisis periods and two crisis periods. This thesis assesses market integration among 17 markets, including asymmetries and leverage effect in the marginal distributions, volatility spillover and tail dependence. The thesis aims to: 1) investigate the univariate asymmetries and leverage effect in the distributional volatility of each time series and to detect volatility spillover between China and other studied markets; 2) assess the dynamic multivariate dependence between China and other studied markets; 3) evaluate the bivariate dependence structure for each of China’s markets and other studied markets using seven different copula functions; and 4) study the multivariate joint tail dependence structure of all studied markets using vine copulas. There are various findings from the thesis. Many advanced and emerging markets experienced leverage effect and asymmetries in volatility. China’s markets were much more prone to local shocks than external shocks and in many cases, there is evidence that China’s markets diverged from the global trends especially during the crisis periods. Besides, segmentation between China’s markets and the United States is clearly evident. In addition, regional dependence is stronger than intra-regional dependence. The thesis also found the existence of contagion effect between each of China’s markets and various markets in the sample in the Global Financial Crisis. Finally, heterogeneity was found for A-, B- and H-shares in various aspects, from distributional asymmetries to joint behaviour in both crisis and non-crisis periods. A novel aspect of this thesis is that it closes the gap in the literature of market linkages for A-, B- and H-shares with other global markets by assessing volatility spillover, time-varying co-movement, and tail dependence among the studied markets. This thesis provides various implications in both theoretical and empirical contexts in many areas including measuring joint risk at the tails, constructing an optimal portfolio, hedging, and managing financial exposures and contagious volatility from other markets. The thesis provides some recommendations and suggestions regarding the policies implemented in China

Dependence analysis in BRICS stock markets : a vine copula approach

Abstract : This study makes use of three types of vine copulas, c-vine, d-vine and r-vine copulas, to investigate the dependence structure in the BRICS stock markets using daily stock market price data spanning from 28-12-2000 to 10-08-2018. To account for the dynamic effects in dependence measures, the study divides the sample period into three sub-samples: the pre-crisis period (from 28-12- 2000 to 31-01-2007), the crisis period (from 01-02-2007 to 29-12-2011), and the post-crisis period (from 04-01-2012 to 10-08-2018). The price data is first converted to return series and filtered using different ARIMA-GARCH models in order to remove the autocorrelation and heteroscedasticity effects. During this process, it was found that most of the return series exhibited leverage effects, an indication that bad news in the stock markets leads to larger spikes in volatility than good news does. To understand the implication of this effect on the dependence structure of stock markets in the BRICS countries, the c-vine, d-vine and r-vine copulas are used. The use of vine copulas has some significant advantages over traditional copulas as they model the dependence in the BRICS using pairwise copula constructions. The results show that the three types of vine copula models suggest that Student’s t and the SBB7 copulas best describe the dependence structure in the BRICS markets. Unlike other studies, our findings show the existence of a very strong dependence between South Africa and Russia, South Africa and India, and South Africa and Brazil during the pre-crisis, the crisis and the post-crisis periods, suggesting a financial integration between these three countries. Furthermore, we find strong dependence between China and the rest of BRICS markets only during a financial crisis. The study identifies two types of dependence in the BRICS stock markets: the first is among small economies (South Africa, Brazil and Russia) and the second one among large economies (China and India). Small economies tend to co-move during bull and bear markets while large economies co-move with the rest only during bear market periods.M.Com. (Financial Economics