ISSN 0253-2778

CN 34-1054/N

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Dynamic dependence of return and volatility between BRICS stock markets based on TV-Copula-X model

  • 1.

    School of Management, University of Science and Technology of China, Hefei 230026, China

  • 2.

    International Institute of Finance, University of Science and Technology of China, Hefei 230601,China

Cite this:
https://doi.org/10.3969/j.issn.0253-2778.2020.05.009
  • Received Date: 22 April 2019
  • Accepted Date: 22 May 2019
  • Rev Recd Date: 22 May 2019
  • Publish Date: 31 May 2020
    Abstract

  • Abstract

    The TV-Copula-X model was constructed with the addition of an exogenous variable the dynamic Copula function. Based on the definition of ‘volatility surprise’, the dependence structures of the BRICS were studied from the perspectives of mean spillover and volatility spillover, and whether the structures would be affected by the US stock market. The data of the BRICS and the US stock markets was selected for empirical research. The results show that the BRICS have significant dependence from the aspects of return and volatility. There are asymmetric dependent structures between the volatility of the BRICS but only some countries of BRICS have asymmetric dependent structures between their yields. The US stock market has a certain impact on the interdependence of some BRICS countries, and the correlation between the BRICS stock markets will increase when a financial crisis or positive events occurs.

    Abstract

    The TV-Copula-X model was constructed with the addition of an exogenous variable the dynamic Copula function. Based on the definition of ‘volatility surprise’, the dependence structures of the BRICS were studied from the perspectives of mean spillover and volatility spillover, and whether the structures would be affected by the US stock market. The data of the BRICS and the US stock markets was selected for empirical research. The results show that the BRICS have significant dependence from the aspects of return and volatility. There are asymmetric dependent structures between the volatility of the BRICS but only some countries of BRICS have asymmetric dependent structures between their yields. The US stock market has a certain impact on the interdependence of some BRICS countries, and the correlation between the BRICS stock markets will increase when a financial crisis or positive events occurs.
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    • References(18)
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    王海龙.金砖国家股市联动性实证分析 [D].沈阳：辽宁大学，2016.
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    CHAN-LAU J A, IVASCHENKO I. Asian flu or Wall Street virus? Tech and non-tech spillovers in the United States and Asia[J]. Multinational Financial Management, 2003, 13(4-5): 303-322.
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    ABOURA S, CHEVALLIER J. Cross-market spillovers with ‘volatility surprise’[J]. Review of Financial Economics, 2014, 23(4): 194-207.
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    BOLLERSLEV T. Modelling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model [J]. The Review of Economics and Statistics, 1990, 72(3): 498-505.
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    ENGLE R. Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroscedasticity models[J]. Journal of Business and Economic Statistics, 2002, 20(3): 339-350.
    [14]
    SKLAR M. Fonctions de répartition an dimensions et leurs marges[J]. Publications de l’Institut de Statistique de l’Université de Paris, 1959, 8: 229-231.
    [15]
    EMBRECHTS P, McNeil A J, Straumann D. Correlation and dependence in risk management: Properties and pitfalls[C]//Risk Management: Value at Risk and Beyond.Cambridge University Press, 1999: 176-223.
    [16]
    张尧庭.连接函数(Copula)技术与金融风险分析[J].统计研究，2002(4): 48-51.
    [17]
    PATTON A J. Modelling asymmetric exchange rate dependence[J]. International Economic Review, 2006, 47(2): 527-555.
    [18]
    REBOREDO J C, UGOLINI A. Systemic risk in European sovereign debt markets: A CoVaR-copula approsch[J]. International Money and Finance, 2015, 51: 214-244.
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    ROSS S A. Information and volatility: The no-arbitrage martingale approach to timing and resolution irrelevancy[J]. The Journal of Finance, 1989, 44(1): 1-17.)
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    [1]
    王海龙.金砖国家股市联动性实证分析 [D].沈阳：辽宁大学，2016.
    [2]
    王雅.中国与其他金砖国家股市的联动效应研究 [D].吉林：吉林财经大学，2016.
    [3]
    欧阳敏华.“金砖四国”股票市场间相依结构分析[J].技术经济与管理研究， 2012，(8): 111-115.
    [4]
    陈鼎玉，谢梦洁，唐德丽.金砖国家股票市场联动性的实证分析[J].产业与科技论坛，2018，17(17): 145-146.
    [5]
    张延良，赵晓琦，胡晓燕.金砖国家股票市场收益波动性比较研究[J].南亚研究，2014,(3): 97-103.
    [7]
    王璐，黄登仕，乔高秀，等.美国股市会影响金砖国家股市之间的相关 性吗? ——线性和非线性条件 Granger 因果检验[J].系统工程，2018，36 (5): 13-22.
    [8]
    ENGLE R. Technical note: Statistical models for financial volatility [J]. Financial Analysts Journal, 1993, 49:72-78.
    [9]
    HAMAO Y, MASULIS R W, NG V. Correlations in price changes and volatility across international stock markets[J]. The Review of Financial Studies, 1990, 3(2): 281-307.
    [10]
    CHAN-LAU J A, IVASCHENKO I. Asian flu or Wall Street virus? Tech and non-tech spillovers in the United States and Asia[J]. Multinational Financial Management, 2003, 13(4-5): 303-322.
    [11]
    ABOURA S, CHEVALLIER J. Cross-market spillovers with ‘volatility surprise’[J]. Review of Financial Economics, 2014, 23(4): 194-207.
    [12]
    BOLLERSLEV T. Modelling the coherence in short-run nominal exchange rates: A multivariate generalized ARCH model [J]. The Review of Economics and Statistics, 1990, 72(3): 498-505.
    [13]
    ENGLE R. Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroscedasticity models[J]. Journal of Business and Economic Statistics, 2002, 20(3): 339-350.
    [14]
    SKLAR M. Fonctions de répartition an dimensions et leurs marges[J]. Publications de l’Institut de Statistique de l’Université de Paris, 1959, 8: 229-231.
    [15]
    EMBRECHTS P, McNeil A J, Straumann D. Correlation and dependence in risk management: Properties and pitfalls[C]//Risk Management: Value at Risk and Beyond.Cambridge University Press, 1999: 176-223.
    [16]
    张尧庭.连接函数(Copula)技术与金融风险分析[J].统计研究，2002(4): 48-51.
    [17]
    PATTON A J. Modelling asymmetric exchange rate dependence[J]. International Economic Review, 2006, 47(2): 527-555.
    [18]
    REBOREDO J C, UGOLINI A. Systemic risk in European sovereign debt markets: A CoVaR-copula approsch[J]. International Money and Finance, 2015, 51: 214-244.
    [19]
    ROSS S A. Information and volatility: The no-arbitrage martingale approach to timing and resolution irrelevancy[J]. The Journal of Finance, 1989, 44(1): 1-17.)
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