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Can the VAR model outperform MRS model for asset allocation in commodity market under different risk preferences of investors?

Yue-Jun Zhang () and Jia-Juan Lin

International Review of Financial Analysis, 2019, vol. 66, issue C

Abstract: It is universally acknowledged that both linear Vector Autoregressive (VAR) models and nonlinear econometric frameworks, such as Markov Regime Switching (MRS) model, can produce appropriate portfolio allocations that hedge against the bullish and bearish dynamics in commodity markets. Then, whether the linear model can outperform the nonlinear model is worth exploring for modeling when we focus on the asset allocations in commodity markets. Therefore, this paper studies whether simple VAR models can produce commodity portfolios whose performance is superior or similar to that obtained under MRS model, and also discusses the impact of investors' different risk preferences on commodity portfolio performance. The empirical results indicate that, first, VAR models can produce portfolios with better performance than MRS model in the case of a long sample. Second, an increasing risk aversion of investors may significantly reduce the portfolio performance. Among them, investors who prefer profit-making to risk-resisting and who pay same attention to returns and risks are more likely to obtain portfolios with higher returns and lower risks. Finally, WTI is an appropriate investment target for investors who only focus on the maximization of returns, while for investors with other risk preferences, gold proves an ideal investment target.

Keywords: Commodity portfolio; VAR model; MRS model; LASSO regression; Genetic algorithm (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1016/j.irfa.2019.101395

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