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Volatility-dependent correlations: further evidence of when, where and how

Author

Listed:
  • Adam Clements

    (Queensland University of Technology)

  • Ayesha Scott

    (Auckland University of Technology)

  • Annastiina Silvennoinen

    (Queensland University of Technology)

Abstract

This paper expands on the usefulness of conditioning correlations on market volatility to generate forecasts of the covariance matrix in two contexts: within a single market and between several international markets. The dynamic conditional correlation family provides an illustration of the relationship between volatility and correlation. We use a portfolio allocation problem to compare covariance forecasts over a range of portfolio sizes and sub-samples of high and low market volatility. Findings confirm recent results for these models in comparable examples and extend these results through the two comprehensive out-of-sample analyses including large dimensional and international settings. This study furthers our understanding of the linkage between volatility and correlations and provides guidance for exploiting correlation’s dependence on volatility, emphasising its importance for differing market states and portfolio characteristics.

Suggested Citation

  • Adam Clements & Ayesha Scott & Annastiina Silvennoinen, 2019. "Volatility-dependent correlations: further evidence of when, where and how," Empirical Economics, Springer, vol. 57(2), pages 505-540, August.
  • Handle: RePEc:spr:empeco:v:57:y:2019:i:2:d:10.1007_s00181-018-1473-0
    DOI: 10.1007/s00181-018-1473-0
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    1. Sébastien Laurent & Jeroen V. K. Rombouts & Francesco Violante, 2012. "On the forecasting accuracy of multivariate GARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 934-955, September.
    2. Adam Clements & Ayesha Scott & Annastiina Silvennoinen, 2013. "On the Benefits of Equicorrelation for Portfolio Allocation," NCER Working Paper Series 99, National Centre for Econometric Research.
    3. Alessandro Beber & Michael W. Brandt & Kenneth A. Kavajecz, 2009. "Flight-to-Quality or Flight-to-Liquidity? Evidence from the Euro-Area Bond Market," The Review of Financial Studies, Society for Financial Studies, vol. 22(3), pages 925-957, March.
    4. BAUWENS, Luc & otranto, EDOARDO, 2013. "Modeling the dependence of conditional correlations on volatility," LIDAM Discussion Papers CORE 2013014, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Becker, R. & Clements, A.E. & Doolan, M.B. & Hurn, A.S., 2015. "Selecting volatility forecasting models for portfolio allocation purposes," International Journal of Forecasting, Elsevier, vol. 31(3), pages 849-861.
    6. Caporin, Massimiliano & McAleer, Michael, 2014. "Robust ranking of multivariate GARCH models by problem dimension," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 172-185.
    7. Gian Piero Aielli, 2013. "Dynamic Conditional Correlation: On Properties and Estimation," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 282-299, July.
    8. Luc Bauwens & Edoardo Otranto, 2016. "Modeling the Dependence of Conditional Correlations on Market Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 254-268, April.
    9. repec:taf:jnlbes:v:30:y:2012:i:2:p:212-228 is not listed on IDEAS
    10. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2005. "There is a risk-return trade-off after all," Journal of Financial Economics, Elsevier, vol. 76(3), pages 509-548, June.
    11. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    12. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    13. Jeff Fleming & Chris Kirby & Barbara Ostdiek, 2001. "The Economic Value of Volatility Timing," Journal of Finance, American Finance Association, vol. 56(1), pages 329-352, February.
    14. Longin, Francois & Solnik, Bruno, 1995. "Is the correlation in international equity returns constant: 1960-1990?," Journal of International Money and Finance, Elsevier, vol. 14(1), pages 3-26, February.
    15. Christoffersen, Peter & Errunza, Vihang & Jacobs, Kris & Jin, Xisong, 2014. "Correlation dynamics and international diversification benefits," International Journal of Forecasting, Elsevier, vol. 30(3), pages 807-824.
    16. Ramchand, Latha & Susmel, Raul, 1998. "Volatility and cross correlation across major stock markets," Journal of Empirical Finance, Elsevier, vol. 5(4), pages 397-416, October.
    17. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    18. Bracker, Kevin & Koch, Paul D., 1999. "Economic determinants of the correlation structure across international equity markets," Journal of Economics and Business, Elsevier, vol. 51(6), pages 443-471.
    19. Cappiello, Lorenzo & Gérard, Bruno & Kadareja, Arjan & Manganelli, Simone, 2006. "Financial integration of new EU Member States," Working Paper Series 683, European Central Bank.
    20. Cavit Pakel & Neil Shephard & Kevin Sheppard & Robert F. Engle, 2021. "Fitting Vast Dimensional Time-Varying Covariance Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 652-668, July.
    21. 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 339-350, July.
    22. Fleming, Jeff & Kirby, Chris & Ostdiek, Barbara, 2003. "The economic value of volatility timing using "realized" volatility," Journal of Financial Economics, Elsevier, vol. 67(3), pages 473-509, March.
    23. Sarwar, Ghulam, 2014. "U.S. stock market uncertainty and cross-market European stock returns," Journal of Multinational Financial Management, Elsevier, vol. 28(C), pages 1-14.
    24. Clements, A. & Silvennoinen, A., 2013. "Volatility timing: How best to forecast portfolio exposures," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 108-115.
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    Cited by:

    1. Bauwens, Luc & Otranto, Edoardo, 2020. "Nonlinearities and regimes in conditional correlations with different dynamics," Journal of Econometrics, Elsevier, vol. 217(2), pages 496-522.
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    3. L. Bauwens & E. Otranto, 2020. "Modelling Realized Covariance Matrices: a Class of Hadamard Exponential Models," Working Paper CRENoS 202007, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
    4. Xi Zhang & Xu Wu & Linlin Zhang & Zhonglu Chen, 2022. "The Evaluation of Mean-Detrended Cross-Correlation Analysis Portfolio Strategy for Multiple risk Assets," Evaluation Review, , vol. 46(2), pages 138-164, April.

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    More about this item

    Keywords

    Volatility; Multivariate GARCH; Portfolio allocation; VIX; VSTOXX;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G1 - Financial Economics - - General Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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