In this paper, we empirically analyze the possible advantages of modelling a time-varying risk aversion that best fits investors’ behavior in the context of the optimal portfolio choice. We build optimal dynamic portfolios by focusing on the estimation of a time-varying relative risk aversion parameter (RRA). Conditional univariate and multivariate models, such as GARCH, GARCH-M and DCC-GARCH, for modelling the optimal portfolio choice and the RRA parameter are implemented. As a model validation tool, the realized performance and downside risk exposure of these portfolios one month ahead is compared to that resulting from implementing a constant risk aversion parameter. The Ledoit and Wolf (2008) test provides robustness to our results and reveals the average outperformance of the dynamic risk aversion strategy over others as the constant risk aversion or the passive management strategies.

在本文中，我们根据经验分析了在最佳投资组合选择的背景下建立最适合投资者行为的时变风险规避模型的可能优势。我们通过关注随时间变化的相对风险规避参数（RRA）的估算来构建最佳动态投资组合。实现了用于建模最佳投资组合选择和RRA参数的条件单变量和多变量模型，例如GARCH，GARCH-M和DCC-GARCH。作为模型验证工具，将这些投资组合在未来一个月的已实现绩效和下行风险敞口与实施恒定风险规避参数所产生的绩效进行比较。