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A New Range-Based Regime-Switching Dynamic Conditional Correlation Model for Minimum-Variance Hedging

DOI: 10.4236/jmf.2014.43018, PP. 207-219

Keywords: Minimum-Variance Hedge Ratio, Markov-Switching, Correlation, Range-Based DCC Model, Regime Shift

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Abstract:

This study proposes a new range-based Markov-switching dynamic conditional correlation (MSDCC) model for estimating the minimum-variance hedging ratio and comparing its hedging performance with that of alternative conventional hedging models, including the naive, OLS regression, return-based DCC, range-based DCC and return-based MS-DCC models. The empirical results show that the embedded Markov-switching adjustment in the range-based DCC model can clearly delineate uncertain exogenous shocks and make the estimated correlation process more in line with reality. Overall, in-sample and out-of sample tests indicate that the range-based MS-DCC model outperforms other static and dynamic hedging models.

References

[1]  Engle, R. (2002) Dynamic Conditional Correlation—A Simple Class of Multivariate GARCH Models. Journal of Business and Economic Statistics, 20, 339-350.
http://dx.doi.org/10.1198/073500102288618487
[2]  Bollerslev, T., Engle, R. and Wooldridge, J.M. (1988) A Capital Asset Pricing Model with Time Varying Covariances. Journal of Political Economy, 96, 116-131.
http://dx.doi.org/10.1086/261527
[3]  Engle, R. and Kroner, K. (1995) Multivariate Simultaneous GARCH. Econometric Theory, 11, 122-150.
http://dx.doi.org/10.1017/S0266466600009063
[4]  Bollerslev, T. (1990) Modeling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized ARCH Model. Review of Economics and Statistics, 72, 498-505.
http://dx.doi.org/10.2307/2109358
[5]  Parkinson, M. (1980) The Extreme Value Method for Estimating the Variance of the Rate of Return. Journal of Business, 53, 61-65.
http://dx.doi.org/10.1086/296071
[6]  Rogers, C. and Satchell, S. (1991) Estimating Variance from High, Low and Closing Prices. Annals of Applied Probability, 1, 504-512.
http://dx.doi.org/10.1214/aoap/1177005835
[7]  Yang, D. and Zhang, Q. (2000) Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices. Journal of Business, 73, 477-491.
http://dx.doi.org/10.1086/209650
[8]  Alizadeh, S., Brandt, M. and Diebold, F. (2002) Range-Based Estimation of Stochastic Volatility Models. Journal of Finance, 57, 1047-1091.
http://dx.doi.org/10.1111/1540-6261.00454
[9]  Brandt, M. and Jones, C. (2006) Volatility Forecasting with Range-Based EGARCH Models. Journal of Business and Economic Statistics, 24, 470-486.
http://dx.doi.org/10.1198/073500106000000206
[10]  Chou, R.Y. (2005) Forecasting Financial Volatilities with Extreme Values: The Conditional Autoregressive Range (CARR) Model. Journal of Money Credit and Banking, 37, 561-582.
http://dx.doi.org/10.1353/mcb.2005.0027
[11]  Chou, R.Y. (2006) Modeling the Asymmetry of Stock Movements Using Price Ranges. Advances in Econometrics, 20, 231-258.
http://dx.doi.org/10.1016/S0731-9053(05)20009-9
[12]  Chou, R.Y., Wu, C.C. and Liu, N. (2009) Forecasting Time-Varying Covariance with a Range-Based Dynamic Conditional Correlation Model. Review of Quantitative Finance and Accounting, 33, 327-345.
http://dx.doi.org/10.1007/s11156-009-0113-3
[13]  Cai, Y., Chou, R.Y. and Li, D. (2009) Explaining International Stock Correlations with CPI Fluctuations and Market Volatility. Journal of Banking and Finance, 33, 2026-2035.
http://dx.doi.org/10.1016/j.jbankfin.2009.05.013
[14]  Danielsson, J. (2011) Financial Risk Forecasting: The Theory and Practice of Forecasting Market Risk with Implementation in R and Matlab. John Wiley, Hoboken.
[15]  Hamilton, J.D. (1989) A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57, 357-384.
http://dx.doi.org/10.2307/1912559
[16]  Hamilton, J.D. (1990) Analysis of Time Series Subject to Changes in Regime. Journal of Econometrics, 45, 39-70.
http://dx.doi.org/10.1016/0304-4076(90)90093-9
[17]  Pelletier, D. (2006) Regime Switching for Dynamic Correlations. Journal of Econometrics, 131, 445-473.
http://dx.doi.org/10.1016/j.jeconom.2005.01.013
[18]  Billio, M. and Caporin, M. (2005) Multivariate Markov Switching Dynamic Conditional Correlation GARCH Representations for Contagion Analysis. Statistical Method and Applications, 14, 145-161.
http://dx.doi.org/10.1007/s10260-005-0108-8
[19]  Chen, S., Lee, C. and Shreatha, K. (2003) Futures Hedge Ratios: A Review. The Quarterly Review of Economics and Finance, 43, 433-465.
http://dx.doi.org/10.1016/S1062-9769(02)00191-6
[20]  Lien, D. and Tse, Y. (2002) Some Recent Developments in Futures Hedging. Journal of Economic Surveys, 16, 357-396.
http://dx.doi.org/10.1111/1467-6419.00172
[21]  Baillie, R.T. and Myers, R. (1991) Bivariate GARCH Estimation of the Optimal Commodity Futures Hedge. Journal of Applied Econometrics, 6, 109-124.
http://dx.doi.org/10.1002/jae.3950060202
[22]  Kroner, K.F. and Sultan, J. (1993) Time-Varying Distributions and Dynamic Hedging with Foreign Currency Futures. Journal of Finance and Quantitative Analysis, 28, 535-551.
http://dx.doi.org/10.2307/2331164
[23]  Tong, W.H.S. (1996) An Examination of Dynamic Hedging. Journal of International Money and Finance, 15, 19-35.
http://dx.doi.org/10.1016/0261-5606(95)00040-2
[24]  Choudhry, T. (2003) Short Run Deviations and Optimal Hedge Ratio: Evidence from Stock Futures. Journal of Multinational Financial Management, 13, 171-192.
http://dx.doi.org/10.1016/S1042-444X(02)00042-7
[25]  Alizadeh, A. and Nomikos, N. (2004) A Markov Regime Switching Approach for Hedging Stock Indexes. Journal of Futures Markets, 24, 649-674.
http://dx.doi.org/10.1002/fut.10130
[26]  Lien, D., Tse, Y. and Tsui, A. (2002) Evaluating the Hedging Performance of the Constant-Correlation GARCH Model. Applied Financial Economics, 12, 791-798.
http://dx.doi.org/10.1080/09603100110046045
[27]  Copeland, L. and Zhu, Y. (2006) Hedging Effectiveness in the Index Futures Market. Cardiff Economics Working Papers from Cardiff University, Cardiff Business School, Economics Section, Colum Drive, Cardiff.
[28]  Alexander, C. and Barbosa, A. (2007) Effectiveness of Minimum Variance Hedging. Journal of Portfolio Management, 33, 46-59.
http://dx.doi.org/10.3905/jpm.2007.674793
[29]  Lien, D. (2005) The Use and Abuse of the Hedging Effectiveness Measure. International Review of Financial Analysis, 14, 277-282.
http://dx.doi.org/10.1016/j.irfa.2004.11.001
[30]  Lien, D. (2009) A Note on the Hedging Effectiveness of GARCH Models. International Review of Economics and Finance, 18, 110-112.
http://dx.doi.org/10.1016/j.iref.2007.07.004
[31]  Lien, D. (2010) A Note on the Relationship between the Variability of the Hedge Ratio and Hedging Performance. Journal of Futures Markets, 30, 1100-1104.
http://dx.doi.org/10.1002/fut.20455
[32]  Ederington, L.H. (1979) The Hedging Performance of the New Futures Markets. Journal of Finance, 34, 157-170.
http://dx.doi.org/10.1111/j.1540-6261.1979.tb02077.x
[33]  Bollerslev, T. and Wooldridge, J.M. (1992) Quasi Maximum Likelihood Estimation and Inference in Dynamic Models with Time Varying Covariances. Econometric Reviews, 11, 143-172.
http://dx.doi.org/10.1080/07474939208800229
[34]  Engle, R. (2001) GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics. Journal of Economic Perspectives, 15, 157-168.
http://dx.doi.org/10.1257/jep.15.4.157
[35]  Alizadeh, A., Nomikos, N. and Pouliasis, P.K. (2008) A Markov Regime Switching Approach for Hedging Energy Commodities. Journal of Banking and Finance, 32, 1970-1983.
http://dx.doi.org/10.1016/j.jbankfin.2007.12.020
[36]  Engel, C. (1994) Can the Markov Switching Model Forecast Exchange Rates? Journal of International Economics, 36, 151-165.
http://dx.doi.org/10.1016/0022-1996(94)90062-0
[37]  Marsh, I.W. (2000) High Frequency Markov Switching Models in the Foreign Exchange Market. Journal of Forecasting, 19, 123-134.
http://dx.doi.org/10.1002/(SICI)1099-131X(200003)19:2<123::AID-FOR750>3.0.CO;2-C
[38]  Cotter, J. and Hanly, J. (2006) Reevaluating Hedging Performance. Journal of Futures Markets, 26, 677-702.
http://dx.doi.org/10.1002/fut.20212

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