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Abstract
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.
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