Dynamic Portfolio Allocation using the Markov Switching Model: The Case of Thailand
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Abstract
This paper employed the Markov-switching model for estimating the expected return, variance, and covariance of the investment portfolio including large capitalization stock, small capitalization stock and government bond. This model allows for changes in market regimes, i.e. bull and bear markets. The empirical results showed that the large capitalization stock and small capitalization stock have higher risk-adjusted return and could be allocated in portfolio during the bull regime. However, almost all investment weights are allocated to government bonds, which are characterized as safe haven assets, during the bear regime. Comparing the portfolio performance, we found that dynamic portfolio allocation according to the parameter estimation in the Markov-switching model has better performance based on cumulative return, volatility and Sharpe ratio than those of equal weight portfolio and Markowitz mean-variance portfolio for both in-sample and out-of-sample periods.
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References
Ang, A., & Bekaert, G. (2004). How regimes affect asset allocation. Financial Analysts Journal, 60(2), 86-99. https://doi.org/10.2469/faj.v60.n2.2612
Ang, A., & Chen, J. (2002). Asymmetric correlations of equity portfolios. Journal of Financial Economics, 63(3), 443-494. https://doi.org/10.1016/S0304-405X(02)00068-5
Bossaerts, P., & Hillion, P. (1999). Implementing statistical criteria to select return forecasting models: what do we learn?. The Review of Financial Studies, 12(2), 405-428. https://doi.org/10.1093/rfs/12.2.405
Bulla, J., Mergner, S., Bulla, I., Sesboüé, A., & Chesneau, C. (2011). Markov-switching asset allocation: Do profitable strategies exist?. Journal of Asset Management, 12(5), 310-321. https://doi.org/10.1057/jam.2010.27
Chusil, S. (2003). Investigation on regime switching in stock market: case studies of Thailand and US [Doctoral dissertation]. Thammasat University Library.
De la Torre-Torres, O. V., Galeana-Figueroa, E., & Álvarez-García, J. (2021). A Markov-switching VSTOXX trading algorithm for enhancing EUR stock portfolio performance. Mathematics, 9(9), 1030. https://doi.org/10.3390/math9091030
Fama, E. F., & French, K. R. (1992). The Cross-Section of Expected Stock Returns. Journal of Finance, 47(2), 427-465. https://doi.org/10.1111/j.1540-6261.1992.tb04398.x
Franses, P. H., & Van Dijk, D. (2000). Non-linear time series models in empirical finance. Cambridge university press.
Guidolin, M., & Timmermann, A. (2006). An econometric model of nonlinear dynamics in the joint distribution of stock and bond returns. Journal of Applied Econometrics, 21(1), 1-22. https://doi.org/10.1002/jae.824
Guidolin, M., & Timmermann, A. (2007). Asset allocation under multivariate regime switching. Journal of Economic Dynamics and Control, 31(11), 3503-3544. https://doi.org/10.1016/j.jedc.2006.12.004
Khanthavit, A. (2001). A Markov-Switching model for mutual fund performance evaluation [Unpublished manuscript]. Thammasat University and Stock Exchange of Thailand.
Liow, K. H., & Zhu, H. (2007). Regime switching and asset allocation: Evidence from international real estate security markets. Journal of Property Investment & Finance. https://doi.org/10.1108/14635780710746920
Nuanboon, L. (2009). Predicting Bear Stock Market in Thailand with Macroeconomic Variables Via Markov Switching Model [Master’ s thesis]. Thammasat University Library.
Opdyke, J. D. J. (2007). Comparing Sharpe ratios: so where are the p-values?. Journal of Asset Management, 8(5), 308-336. https://doi.org/10.1057/palgrave.jam.2250084
Shibata, M. (2012). Identifying bull and bear markets in Japan. Asia-Pacific Financial Markets, 19(2), 99-117. https://doi.org/10.1007/s10690-011-91
