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Regime-switching in volatility and correlation structure using range-based models with Markov-switching

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  • Miao, Daniel Wei-Chung
  • Wu, Chun-Chou
  • Su, Yi-Kai

Abstract

This study examines latent shifts in the conditional volatility and correlation for the U.S. stock and T-bond data using the two-state Markov-switching range-based volatility and correlation models. This paper comes up with clear evidence of volatility regime-switching in stock indices and T-bond over the crisis period. As regards the process of correlation, we also find evidence of regime changes in correlations between stock indices and T-bond over several financial crises. We conclude that the phenomena of both volatility and correlation regime-switching are triggered by these financial crises. In addition, the range-based volatility and correlation model with regime-switching method could explicitly point out the true date of structure changes in the data generating process for volatility and correlation variables.

Suggested Citation

  • Miao, Daniel Wei-Chung & Wu, Chun-Chou & Su, Yi-Kai, 2013. "Regime-switching in volatility and correlation structure using range-based models with Markov-switching," Economic Modelling, Elsevier, vol. 31(C), pages 87-93.
  • Handle: RePEc:eee:ecmode:v:31:y:2013:i:c:p:87-93
    DOI: 10.1016/j.econmod.2012.11.013
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    References listed on IDEAS

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    1. Sun, Licheng, 2005. "Regime shifts in interest rate volatility," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 418-434, June.
    2. Kalbaska, A. & Gątkowski, M., 2012. "Eurozone sovereign contagion: Evidence from the CDS market (2005–2010)," Journal of Economic Behavior & Organization, Elsevier, vol. 83(3), pages 657-673.
    3. Parkinson, Michael, 1980. "The Extreme Value Method for Estimating the Variance of the Rate of Return," The Journal of Business, University of Chicago Press, vol. 53(1), pages 61-65, January.
    4. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    5. Ray Chou & Chun-Chou Wu & Nathan Liu, 2009. "Forecasting time-varying covariance with a range-based dynamic conditional correlation model," Review of Quantitative Finance and Accounting, Springer, vol. 33(4), pages 327-345, November.
    6. Brandt, Michael W. & Jones, Christopher S., 2006. "Volatility Forecasting With Range-Based EGARCH Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 470-486, October.
    7. Chou, Ray Yeutien & Liu, Nathan, 2010. "The economic value of volatility timing using a range-based volatility model," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2288-2301, November.
    8. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
    9. Haas, Markus, 2010. "Covariance forecasts and long-run correlations in a Markov-switching model for dynamic correlations," Finance Research Letters, Elsevier, vol. 7(2), pages 86-97, June.
    10. Gray, Stephen F., 1996. "Modeling the conditional distribution of interest rates as a regime-switching process," Journal of Financial Economics, Elsevier, vol. 42(1), pages 27-62, September.
    11. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range-Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1047-1091, June.
    12. Hamilton, James D., 1990. "Analysis of time series subject to changes in regime," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 39-70.
    13. Dueker, Michael J, 1997. "Markov Switching in GARCH Processes and Mean-Reverting Stock-Market Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 26-34, January.
    14. Lamoureux, Christopher G & Lastrapes, William D, 1990. "Persistence in Variance, Structural Change, and the GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 225-234, April.
    15. Cai, Jun, 1994. "A Markov Model of Switching-Regime ARCH," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 309-316, July.
    16. Chou, Ray Yeutien, 2005. "Forecasting Financial Volatilities with Extreme Values: The Conditional Autoregressive Range (CARR) Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(3), pages 561-582, June.
    17. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
    18. Liu, Xinyi & Margaritis, Dimitris & Wang, Peiming, 2012. "Stock market volatility and equity returns: Evidence from a two-state Markov-switching model with regressors," Journal of Empirical Finance, Elsevier, vol. 19(4), pages 483-496.
    19. Sebastian Edwards & Raul Susmel, 2003. "Interest-Rate Volatility in Emerging Markets," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 328-348, May.
    20. Ang, Andrew & Bekaert, Geert, 2002. "Regime Switches in Interest Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 163-182, April.
    21. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
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    Cited by:

    1. Henning Fischer & Ángela Blanco‐FERNÁndez & Peter Winker, 2016. "Predicting Stock Return Volatility: Can We Benefit from Regression Models for Return Intervals?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(2), pages 113-146, March.
    2. Changqing, Luo & Chi, Xie & Cong, Yu & Yan, Xu, 2015. "Measuring financial market risk contagion using dynamic MRS-Copula models: The case of Chinese and other international stock markets," Economic Modelling, Elsevier, vol. 51(C), pages 657-671.
    3. repec:eee:ecmode:v:64:y:2017:i:c:p:349-356 is not listed on IDEAS

    More about this item

    Keywords

    Range-based model; Markov-switching method; Volatility; Correlation;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G01 - Financial Economics - - General - - - Financial Crises
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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