摘要(英) |
This thesis proposes a multivariate Markov switching model with two regimes indicating the bull and bear market, respectively. Following the seminal mean-variance analysis framework cite{Mark}, we propose a method to calculate the optimal portfolio weight based on the conditional means and variance under specific regimes, and provide a Quasi likelihood estimation for parameter estimation in the proposed Markov switching model. For comparison, we consider the equal-weighted portfolio, the standard mean-variance portfolio, and the mean-variance portfolio with the Markov switching model. Empirical studies are implemented using six exchange traded funds (ETFs) in Taiwan market and nineteen ETFs in the US market at various levels of risk aversion. Out of sample rebalancing experiments show that the mean-variance portfolio with the proposed multivariate Markov switching model outperforms the others in terms of various strategy summary statistics. |
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