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Evaluating the impact of market reforms on Value-at-Risk forecasts of Chinese A and B shares

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  • da Veiga, Bernardo
  • Chan, Felix
  • McAleer, Michael

Abstract

This paper analyses the time-varying conditional correlations between Chinese A and B share returns using the Dynamic Conditional Correlation (DCC) model of Engle [Engle, R.F. (2002), "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models", Journal of Business and Economic Statistics, 20, 339-350.]. The results show that the conditional correlations increased substantially following the B share market reform, whereby Chinese investors were permitted to purchase B shares. However, this increase in correlations was found to have begun well before the B share market reform. This result has significant implication relating to the structure of the information flow between the markets for the two classes of shares. Value-at-Risk (VaR) threshold forecasts are used to analyse the importance of accommodating dynamic conditional correlations between Chinese A and B shares, and thus reflects the impact of the changes in information flow on the risk evaluation of a diversified portfolio. The competing VaR forecasts are analysed using the Unconditional Coverage, Serial Independence and Conditional Coverage tests of Christoffersen [Christoffersen (1998), "Evaluating Interval Forecasts", International Economic Review, 39, 841-862], and the Time Until First Failure Test of Kupiec [Kupiec, P.H., (1995), "Techniques for Verifying the Accuracy of Risk Measurements Models", Journal of Derivatives, 73-84]. The results offer mild support for the DCC model over its constant conditional correlation counterpart.

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Bibliographic Info

Article provided by Elsevier in its journal Pacific-Basin Finance Journal.

Volume (Year): 16 (2008)
Issue (Month): 4 (September)
Pages: 453-475

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Handle: RePEc:eee:pacfin:v:16:y:2008:i:4:p:453-475

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Web page: http://www.elsevier.com/locate/pacfin

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References

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  1. Jose Lopez, 1998. "Methods for evaluating value-at-risk estimates," Research Paper 9802, Federal Reserve Bank of New York.
  2. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  3. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
  4. Bailey, Warren, 1994. "Risk and return on China's new stock markets: Some preliminary evidence," Pacific-Basin Finance Journal, Elsevier, vol. 2(2-3), pages 243-260, May.
  5. Tse, Y K & Tsui, Albert K C, 2002. "A Multivariate Generalized Autoregressive Conditional Heteroscedasticity Model with Time-Varying Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 351-62, July.
  6. Michael Mcaleer & Bernardo da Veiga, 2008. "Forecasting value-at-risk with a parsimonious portfolio spillover GARCH (PS-GARCH) model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(1), pages 1-19.
  7. Robert Brooks & Vanitha Ragunathan, 2003. "Returns and volatility on the Chinese stock markets," Applied Financial Economics, Taylor & Francis Journals, vol. 13(10), pages 747-752.
  8. Belton Fleisher & Dongwei Su, 1998. "Why Does Return Volatility Differ in Chinese Stock Markets?," Working Papers 98-03, Ohio State University, Department of Economics.
  9. Chien-Liang Chiu & Mingchih Lee & Chun-Da Chen, 2005. "Removal of an investment restriction: the 'B' share experience from China's stock markets," Applied Financial Economics, Taylor & Francis Journals, vol. 15(4), pages 273-285.
  10. McAleer, Michael, 2005. "Automated Inference And Learning In Modeling Financial Volatility," Econometric Theory, Cambridge University Press, vol. 21(01), pages 232-261, February.
  11. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
  12. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(01), pages 122-150, February.
  13. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
  14. Engle, Robert, 2002. "Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 339-50, July.
  15. Jeantheau, Thierry, 1998. "Strong Consistency Of Estimators For Multivariate Arch Models," Econometric Theory, Cambridge University Press, vol. 14(01), pages 70-86, February.
  16. Chan, Wing H & Maheu, John M, 2002. "Conditional Jump Dynamics in Stock Market Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 377-89, July.
  17. Ma, Xianghai, 1996. "Capital controls, market segmentation and stock prices: Evidence from the Chinese stock market," Pacific-Basin Finance Journal, Elsevier, vol. 4(2-3), pages 219-239, July.
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Cited by:
  1. Bernardo da Veiga & Felix Chan & Michael McAleer, 2009. "It Pays to Violate: How Effective are the Basel Accord Penalties?," CIRJE F-Series CIRJE-F-683, CIRJE, Faculty of Economics, University of Tokyo.
  2. Christos Agiakloglou & Charalampos Agiropoulos, 2011. "The sensitivity of Value-at-Risk estimates using Monte Carlo approach," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, vol. 61(1-2), pages 7-12, January -.

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