PREDICTING AND DECOMPOSING THE RISK OF DATA-DRIVEN PORTFOLIOS

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 ...

Author(s):

Nabil Bouamara

Kris Boudt

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, authors develop the methodology to predict, dissect and interpret the h-day financial risk in data-driven portfolios. Their 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, they 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. Their main empirical findings rely on the two-decade performance of a portfolio insurance investment strategy. Rather than looking at total portfolio risk, they conclude that it is more informative to look inside the portfolio.

Type: EDHEC Publication
Date: le 08/03/2020
Research Cluster : Finance

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