Predicting and Decomposing the Risk of Data-driven Portfolios

Abstract

Sophisticated algorithmic techniques are complementing human judgement across the fund industry. Whatever the type of rebalancing that occurs in the course of a longer horizon, it probably violates the buy-and-hold assumption. In this article, we develop the methodology to predict, dissect and interpret the h-day financial risk in data-driven portfolios. Our risk budgeting approach is based on a flexible risk factor model that accommodates the dynamics in portfolio composition directly within the risk factors. Once these factors are defined, we cast portfolio risk measures, such as value-at-risk, into an additive mean-variance-skewness-kurtosis format. The simulation study confirms the gains in accuracy compared to the widespread square-root-of-time rule. Our main empirical findings rely on the two-decade performance of a portfolio insurance investment strategy. Rather than looking at total portfolio risk, we conclude that it is more informative to look inside the portfolio.