Dynamic asset allocation for varied financial markets under regime switching framework

Asset allocation among diverse financial markets is essential for investors especially under situations such as the financial crisis of 2008. Portfolio optimization is the most developed method to examine the optimal decision for asset allocation. We employ the hidden Markov model to identify regimes in varied financial markets; a regime switching model gives multiple distributions and this information can convert the static mean–variance model into an optimization problem under uncertainty, which is the case for unobservable market regimes. We construct a stochastic program to optimize portfolios under the regime switching framework and use scenario generation to mathematically formulate the optimization problem. In addition, we build a simple example for a pension fund and examine the behavior of the optimal solution over time by using a rolling-horizon simulation. We conclude that the regime information helps portfolios avoid risk during left-tail events. Portfolio optimization is the most researched and practiced method to examine the optimal decision for asset allocation. The mean–variance model introduced by Markowitz (1952) is the basis for portfolio selection, which finds the optimal portfolio by computing the risk-return tradeoff using the estimated mean vector and the covariance matrix of asset returns. One of the advantages of the Markowitz model is that there are no restrictions on the type of assets that can be included in the model. For example, commodity futures, which has become drastically popular among investors as a major asset class, like stocks and bonds can be easily included in the model to solve the portfolio selection problem. However, the Markowitz model is a single-period model without stochastic characteristics and also assumes that the multi-dimensional return series of assets have constant mean vector and covariance matrix. In this paper, we extend the traditional Markowitz portfolio model to address the changing nature of the covariance matrix under differing market conditions. Certainly, one of the severe issues arising during 2008 crash was the increase in correlation (contagion) that occurred and the ensuing lack of diversification by many investors (even those applying Markowitz models). The regime detection methodology provides an intuitive and practical way to anticipate changing correlation conditions. As such, the research is on the pathway of the original Markowitz tradition. There have been many studies indicating the existence of multiple regimes in financial markets especially the stock market. The hidden Markov model (HMM) is a popular method for regime identification, which has been widely used in engineering and …