Parametric Risk Parity

TL;DR

This paper introduces a risk parity optimization method leveraging market factor independence and advanced distribution modeling, leading to improved out-of-sample performance and diversification.

Contribution

It derives analytical higher order moments for risk decomposition using the Mixed Tempered Stable distribution, enhancing risk parity optimization.

Findings

Improved out-of-sample performance

Greater diversification

Effective modeling of skewed and heavy-tailed risks

Abstract

Any optimization algorithm based on the risk parity approach requires the formulation of portfolio total risk in terms of marginal contributions. In this paper we use the independence of the underlying factors in the market to derive the centered moments required in the risk decomposition process when the modified versions of Value at Risk and Expected Shortfall are considered. The choice of the Mixed Tempered Stable distribution seems adequate for fitting skewed and heavy tailed distributions. The ensuing detailed description of the optimization procedure is due to the existence of analytical higher order moments. Better results are achieved in terms of out of sample performance and greater diversification.