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Flexible covariance dynamics, high‐frequency data, and optimal futures hedging

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  • Yu‐Sheng Lai

Abstract

This paper investigates the out‐of‐sample performance of hedged portfolios constructed using a novel rotated ARCH (RARCH) model class, which enables flexible covariance dynamics for spot and futures returns. The model's empirical fit can be significantly improved when it incorporates rotated realized covariance matrix measures. The empirical results suggest that a highly risk‐averse hedger implementing the restricted RARCH model would be willing to pay substantial switching fees to capture the incremental gains generated by the flexible and informative alternative; this thus supports the economic importance of incorporating high‐frequency data into flexible RARCH modeling processes for the construction of optimal hedged portfolios.

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  • Yu‐Sheng Lai, 2019. "Flexible covariance dynamics, high‐frequency data, and optimal futures hedging," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(12), pages 1529-1548, December.
    • Handle: RePEc:wly:jfutmk:v:39:y:2019:i:12:p:1529-1548
    DOI: 10.1002/fut.22054
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    1. repec:hal:journl:peer-00815564 is not listed on IDEAS
    2. 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.
    3. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    4. Manolis Kavussanos & Ilias Visvikis, 2008. "Hedging effectiveness of the Athens stock index futures contracts," The European Journal of Finance, Taylor & Francis Journals, vol. 14(3), pages 243-270.
    5. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    6. Zhang, Lan, 2011. "Estimating covariation: Epps effect, microstructure noise," Journal of Econometrics, Elsevier, vol. 160(1), pages 33-47, January.
    7. Barndorff-Nielsen, Ole E. & Hansen, Peter Reinhard & Lunde, Asger & Shephard, Neil, 2011. "Multivariate realised kernels: Consistent positive semi-definite estimators of the covariation of equity prices with noise and non-synchronous trading," Journal of Econometrics, Elsevier, vol. 162(2), pages 149-169, June.
    8. Baillie, Richard T & Myers, Robert J, 1991. "Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(2), pages 109-124, April-Jun.
    9. Yu‐Sheng Lai, 2016. "Hedge Ratio Prediction with Noisy and Asynchronous High‐Frequency Data," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(3), pages 295-314, March.
    10. Alexander, C. & Barbosa, A., 2008. "Hedging index exchange traded funds," Journal of Banking & Finance, Elsevier, vol. 32(2), pages 326-337, February.
    11. Yu‐Sheng Lai & Her‐Jiun Sheu, 2010. "The incremental value of a futures hedge using realized volatility," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(9), pages 874-896, September.
    12. Noureldin, Diaa & Shephard, Neil & Sheppard, Kevin, 2014. "Multivariate rotated ARCH models," Journal of Econometrics, Elsevier, vol. 179(1), pages 16-30.
    13. O. E. Barndorff-Nielsen & P. Reinhard Hansen & A. Lunde & N. Shephard, 2009. "Realized kernels in practice: trades and quotes," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 1-32, November.
    14. Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
    15. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    16. Neil Shephard & Kevin Sheppard, 2010. "Realising the future: forecasting with high-frequency-based volatility (HEAVY) models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 197-231.
    17. Ole E. Barndorff-Nielsen & Neil Shephard, 2004. "Econometric Analysis of Realized Covariation: High Frequency Based Covariance, Regression, and Correlation in Financial Economics," Econometrica, Econometric Society, vol. 72(3), pages 885-925, May.
    18. Robert Engle, 2002. "New frontiers for arch models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 425-446.
    19. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    20. Chris Brooks & Olan T. Henry & Gita Persand, 2002. "The Effect of Asymmetries on Optimal Hedge Ratios," The Journal of Business, University of Chicago Press, vol. 75(2), pages 333-352, April.
    21. 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.
    22. 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.
    23. Kroner, Kenneth F & Ng, Victor K, 1998. "Modeling Asymmetric Comovements of Asset Returns," Review of Financial Studies, Society for Financial Studies, vol. 11(4), pages 817-844.
    24. Michael S. Haigh & Matthew T. Holt, 2002. "Crack spread hedging: accounting for time-varying volatility spillovers in the energy futures markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(3), pages 269-289.
    25. Peter Reinhard Hansen & Zhuo Huang & Howard Howan Shek, 2012. "Realized GARCH: a joint model for returns and realized measures of volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 877-906, September.
    26. Noureldin, Diaa & Shephard, Neil & Sheppard, Kevin, 2014. "Multivariate rotated ARCH models," Scholarly Articles 34650305, Harvard University Department of Economics.
    27. 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.
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