Extending the Risk Parity Approach to Higher Moments: Is There Any Value Added?
展示了如何将高阶风险矩一致地纳入风险平价优化，并提出一种从数据中推断最优矩权重的新方法。实证发现，当数据存在显著的高阶矩时，高阶矩风险平价组合表现更优，适合极端市场环境。
The popular risk parity approach is based on volatility as the sole risk measure and therefore lacks the consideration of tail risk. This fact makes risk parity portfolios vulnerable to tail events. In this article, the authors address this issue by showing how higher-risk-moment terms can be consistently incorporated into risk parity optimization. In addition, they present a novel optimization approach in which optimal moment weightings (preferences) in the risk parity optimization are imputed from the data. In a broad-based empirical out-of-sample study and simulation analysis, the authors find superior performance of higher-moment risk parity portfolios when the underlying data exhibit significant higher moments and co-moments. According to the authors, this makes higher-moment risk parity portfolios ideal candidates for worst-case regimes. <b>TOPICS:</b>Portfolio construction, tail risks, risk management