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Dynamic Correlations and Optimal Hedge Ratios

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Author Info
Charles S. Bos () (Vrije Universiteit Amsterdam)
Phillip Gould (Vrije Universiteit Amsterdam)

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Abstract

The focus of this article is using dynamic correlation models for the calculation of minimum variance hedge ratios between pairs of assets. Finding an optimal hedge requires not only knowledge of the variability of both assets, but also of the co-movement between the two assets. For this purpose, use is made of industry standard methods, like the naive hedging or the CAPM approach, more advanced GARCH techniques including estimating BEKK or DCC models and alternatively through the use of unobserved components models. This last set comprises models with stochastically varying variances and/or correlations, denoted by the TVR, SCSV and DCSV models, and an approximation to these with a single-source-of-error setup. Modelling the correlation explicitly is shown to produce the best hedges when applied to the simulated data. For financial time series on the daily S&P 500 cash versus futures returns, and also on weekly S&P 500 versus FTSE 100 returns, the correlations are compared to a realised correlation measure, extracted from high frequency data. Apart from the comparison of correlations, the reduction in portfolio variance produced by different hedging strategies is examined. The data suggests that the most important factor in reducing portfolio variance is the use of a flexible model for time varying volatility, rather than capturing time variation in correlations. GARCH-based models with time varying correlation are found to perform not as good on the present set of measures as the stochastic volatility models, with or without dynamic correlation.

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Publisher Info
Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 07-025/4.

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Date of creation: 20 Feb 2007
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Handle: RePEc:dgr:uvatin:20070025

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Related research
Keywords: Dynamic correlation; multivariate GARCH; stochastic volatility; hedge ratio;

Other versions of this item:

Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing
G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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  2. Yacine Aït-Sahalia, 2005. "How Often to Sample a Continuous-Time Process in the Presence of Market Microstructure Noise," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 18(2), pages 351-416. [Downloadable!] (restricted)
    Other versions:
  3. Sandmann, Gleb & Koopman, Siem Jan, 1998. "Estimation of stochastic volatility models via Monte Carlo maximum likelihood," Journal of Econometrics, Elsevier, vol. 87(2), pages 271-301, September. [Downloadable!] (restricted)
  4. J Keith Ord & Ralph D Snyder & Anne B Koehler & Rob J Hyndman & Mark Leeds, 2005. "Time Series Forecasting: The Case for the Single Source of Error State Space," Monash Econometrics and Business Statistics Working Papers 7/05, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
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  6. Young-Hye Cho & Robert F. Engle, 1999. "Time-Varying Betas and Asymmetric Effect of News: Empirical Analysis of Blue Chip Stocks," NBER Working Papers 7330, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  7. Robert W. Faff & David Hillier & Joseph Hillier, 2000. "Time Varying Beta Risk: An Analysis of Alternative Modelling Techniques," Journal of Business Finance & Accounting, Blackwell Publishing, vol. 27(5&6), pages 523-554. [Downloadable!] (restricted)
  8. Chris Brooks & Olan T. Henry & Gita Persand, 2002. "The Effect of Asymmetries on Optimal Hedge Ratios," Journal of Business, University of Chicago Press, vol. 75(2), pages 333-352, April. [Downloadable!]
  9. Borus Jungbacker & Siem Jan Koopman, 2005. "On Importance Sampling for State Space Models," Tinbergen Institute Discussion Papers 05-117/4, Tinbergen Institute. [Downloadable!]
  10. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 1994. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 371-89, October.
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  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. [Downloadable!] (restricted)
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