Bulla, Jan and Mergner, Sascha and Bulla, Ingo and Sesboüé, André and Chesneau, Christophe (2010): Markovswitching Asset Allocation: Do Profitable Strategies Exist?

PDF
MPRA_paper_21154.pdf Download (448kB)  Preview 
Abstract
This paper proposes a straightforward Markovswitching asset allocation model, which reduces the market exposure to periods of high volatility. The main purpose of the study is to examine the performance of a regimebased asset allocation strategy under realistic assumptions, compared to a buy and hold strategy. An empirical study, utilizing daily return series of major equity indices in the US, Japan, and Germany over the last 40 years, investigates the performance of the model. In an outofsample context, the strategy proves profitable after taking transaction costs into account. For the regional markets under consideration, the volatility reduces on average by 41%. Additionally, annualized excess returns attain 18.5 to 201.6 basis points.
Item Type:  MPRA Paper 

Original Title:  Markovswitching Asset Allocation: Do Profitable Strategies Exist? 
Language:  English 
Keywords:  Hidden Markov model; Markovswitching model; asset allocation; timing; volatility regimes; daily returns 
Subjects:  C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C13  Estimation: General G  Financial Economics > G1  General Financial Markets > G11  Portfolio Choice ; Investment Decisions G  Financial Economics > G1  General Financial Markets > G15  International Financial Markets C  Mathematical and Quantitative Methods > C2  Single Equation Models ; Single Variables > C22  TimeSeries Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes C  Mathematical and Quantitative Methods > C1  Econometric and Statistical Methods and Methodology: General > C15  Statistical Simulation Methods: General E  Macroeconomics and Monetary Economics > E4  Money and Interest Rates > E44  Financial Markets and the Macroeconomy 
Item ID:  21154 
Depositing User:  Jan Bulla 
Date Deposited:  07. Mar 2010 00:27 
Last Modified:  30. Apr 2015 16:09 
References:  Ammann, M. and M. Verhofen (2006). The effect of market regimes on style allocation. Financ. Markets Portf. Manage. 20(3), 309–337. Ang, A. and G. Bekaert (2002). International asset allocation with regime shifts. Rev. Finan. Stud. 15(4), 1137–1187. Ang, A. and G. Bekaert (2004). Timing and diversification: A statedependent asset allocation approach. Financial Analysts J. 60(2), 86–99. Bauer, R., R. Haerden, and R. Molenaar (2004). Timing and diversification: A statedependent asset allocation approach. J. Investing 13(3), 72–80. Baum, L. E. and T. Petrie (1966). Statistical inference for probabilistic functions of finite state Markov chains. Ann. Math. Statist. 37, 1554–1563. Baum, L. E., T. Petrie, G. Soules, and N. Weiss (1970). A maximization technique occurring in the statistical analysis of probabilistic functions of Markov chains. Ann. Math. Statist. 41, 164–171. Bialkowski, J. (2003). Modelling returns on stock indices for western and central european stock exchanges  Markov switching approach. Southeast. Eur. J. Econ. 2(2), 81–100. Brinson, G., B. D. Singer, and G. L. Beebower (1991). Determinants of portfolio performance ii: An update. Fin. Anal. J. 47(3), 40–48. Bulla, J. and A. Berzel (2008). Computational issues in parameter estimation for stationary hidden Markov models. Computation. Stat. 23(1), 1–18. Cappé, O., E. Moulines, and T. Ryden (2007). Inference in Hidden Markov Models. Springer Series in Statistics. New York  Heidelberg  Berlin: SpringerVerlag. Carhart, M. M. (1997). On persistence in mutual fund performance. J. Finance 52(1), 57–82. Dacco, R. and S. Satchell (1999). Why do regimeswitching models forecast so badly? J. Forecasting 18(1), 1–16. Dempster, A. P., N. M. Laird, and D. B. Rubin (1977). Maximum likelihood from incomplete data via the EM algorithm. J. Roy. Statist. Soc. Ser. B 39(1), 1–38. Goldfeld, S.M. and R. E. Quandt (1973). A markovmodel for switching regressions. J. Econometrics 1(1), 3–16. Graflund, A. and B. Nilsson (2003). Dynamic portfolio selection: The relevance of switching regimes and investment horizon. Europ. Finan. Manage. 9(2), 179– 200. Guidolin, M. and A. Timmermann (2005). Economic implications of bull and bear regimes in UK stock and bond returns. Econ. J. 115(500), 111–143. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57(2), 357–384. Hess, M. K. (2006). Timing and diversification: A statedependent asset allocation approach. Europ. J. Finance 12(3), 189–204. Linne, T. (2002). A Markov switching model of stock returns: an application to the emerging markets in central and eastern europe. In in: East European Transition and EU Enlargement, pp. 371–384. PhysicaVerlag. MacDonald, I. L. and W. Zucchini (2009). Hidden Markov for Time Series: An Introduction Using R. CRC Monographs on Statistics and Applied Probability. London: Chapman & Hall. Michaud, R. O. (1989). The markowitz optimization enigma: Is ’optimized’ optimal? Financial Analysts J. 45(1), 31–42. Quandt, R. E. (1958). The estimation of the parameters of a linear regression system obeying two separate regimes. J. Amer. Statistical Assoc. 53(284), 873–880. Quandt, R. E. (1972). A new approach to estimating switching regressions. J. Amer. Statistical Assoc. 67(338), 306–310. Rabiner, L. (1989). A tutorial on hidden Markov models and selected applications in speech recognition. IEEE Trans. Inf. Theory 77(2), 257–284. Rydén, T., T. Terasvirta, and S. Asbrink (1998). Stylized facts of daily return series and the hidden Markov model. J. Appl. Econom. 13(3), 217–244. Schwert, G. W. (1989). Why does stock market volatility change over time. J. Financ. 44(5), 1115–1153. Turner, C. M., R. Startz, and C. R. Nelson (1989). A Markov model of heteroskedasticity, risk, and learning in the stock market. J. Finan. Econ. 25(1), 3–22. Viterbi, A. J. (1967). Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inform. Theory 13(2), 260–269. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/21154 