Measuring financial market risk contagion using dynamic MRS-Copula models: The case of Chinese and other international stock markets

Chengji, Luo; Xie, Chi; Yu, Cong; Xu, Yan

doi:10.1016/j.econmod.2015.09.021

Cited by 41 publications

(13 citation statements)

References 52 publications

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“…No significant dependence was found between oil price and Chinese financial markets (Nguyen & Bhatti, 2012). While applying dynamic Markov Regime Switching Copula models to test for financial risk contagion, evidence of contagion effect was found between Chinese stock market and other international markets under study (Changqing, Chi, Cong, & Yan, 2015). By using time-varying copula and conditional extreme value theory method, tail dependence was studied between the Australian and other international financial markets.…”

Section: The Present State Of Artmentioning

confidence: 99%

Measuring Dependence Between the USA and the Asian Economies: A Time-varying Copula Approach

Rajwani

Kumar

2019

Global Business Review

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During the past few years, many of the financial markets have gone through devastating effects due to the crisis in one or the other economy of the world. The recent global financial crisis has triggered dramatic movements in various stock markets which may arise from interdependence or contagion between the markets. This article attempts to measure the contagion between the equity markets of Asia and the US stock market. The countries considered in the Asian group are China, India, Indonesia, South Korea, Taiwan, Hong Kong, Malaysia and Japan. Most of the Asian economies have experienced drastic higher volatility and uncertainty in the financial markets. If the markets are contagious, then the investors will be unable to reap benefits through international diversification of the portfolio. In such a case, the policymakers will further frame policies so that they can insulate themselves from inflicting heavy damage from various crises. To achieve our goal, we make use of the time-varying copula approach which helps us to study the joint behaviour of the series based on their marginal distribution. Time-varying copula approach can also capture the non-linear dependence in the series and exhibits a rich pattern of tail behaviour. Our findings support the contagion between the Asian stock markets and the US stock market during the global financial crisis. This article also highlights that the increased tail dependence is an important factor for the contagion between the Asian stock markets and the US market.

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Section: The Present State Of Artmentioning

confidence: 99%

Measuring Dependence Between the USA and the Asian Economies: A Time-varying Copula Approach

Rajwani

Kumar

2019

Global Business Review

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“…To summarize, Pericoli and Sbracia ( 2003 ) present the five most mentioned definitions in the financial literature. As for the definitions of contagion, previous studies show the use of several methods to analyze this phenomenon such as ARCH-GARCH models (Rotta and Pereira 2016 ; Alexakis and Pappas 2018 ; Pentecost et al 2019 , among others), copula models (Wen et al 2012 ; Aloui et al 2013 ; Changqing et al 2015 , among others), wavelet transform (Dewandaru et al 2016 ; Dash and Maitra 2019 ; Vácha et al 2019 , among others), network models (Zhu et al 2018 ; Sensoy et al 2019 , among others), etc.…”

Section: Introductionmentioning

confidence: 99%

Transmission of the Greek crisis on the sovereign debt markets in the euro area

2021

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“…This study may have some important implications for portfolio managers, policy makers and researchers, as it can enable them to identify the origin, direction and timing of pure financial contagion (Gómez-Puig & Sosvilla-Rivero, 2016 ). In particular, for portfolio managers, understanding the bilateral and multilateral behaviors of financial markets opens up more opportunities for portfolio diversification and resource allocation (Changqing et al, 2015 ). For policy makers, it can help them to adopt appropriate policy measures in order to reduce the vulnerability of their country to an external shock.…”

Section: Discussionmentioning

confidence: 99%

Spatial contagion between financial markets: new evidence of asymmetric measures

2021

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The objective of this paper is to identify the presence, direction and time at which the pure contagion effect occurred between financial markets. In so doing, the aim is to prove the existence of both spatial and temporal asymmetries of pure contagion effects. Firstly, a new empirical framework is proposed in order to define a spatial contagion index using the conditional cumulative distribution function as a parameter to estimate a conditional copula. This methodology enables us to estimate a dynamic conditional copula, providing information about how the market sent pure contagion effects and when. Secondly, in addition to detecting the direction of contagion, the real-time contagion effect is determined, enabling us to calculate the delay of contagion effects (spillover) between financial markets. The present empirical results show the existence of both spatial and temporal asymmetry for bilateral contagion effects for 16 mature and emerging stock markets during the 2001–2018 period. This proves the importance of taking temporal asymmetry into account when we want to detect the contagion effect of every crisis and to estimate the period of pure contagion relating to investors’ behaviors. Finally, these findings highlight the fact that contagion effects were more intensive during the subprime crisis than they were during the European debt crisis.

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Measuring financial market risk contagion using dynamic MRS-Copula models: The case of Chinese and other international stock markets

Cited by 41 publications

References 52 publications

Measuring Dependence Between the USA and the Asian Economies: A Time-varying Copula Approach

Measuring Dependence Between the USA and the Asian Economies: A Time-varying Copula Approach

Transmission of the Greek crisis on the sovereign debt markets in the euro area

Spatial contagion between financial markets: new evidence of asymmetric measures

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