Kullback-Leibler cluster entropy to quantify volatility correlation and risk diversity

TL;DR

This paper introduces a novel use of Kullback-Leibler cluster entropy to analyze volatility correlations in financial assets, demonstrating its effectiveness in constructing robust diversified portfolios with improved performance.

Contribution

It presents a new application of Kullback-Leibler cluster entropy to quantify volatility correlation and risk diversity, offering a complementary perspective to traditional entropy measures.

Findings

KL cluster entropy peaks at short time scales

Portfolio based on KL entropy outperforms traditional methods

Model of realized volatility as a fractional stochastic process

Abstract

The Kullback-Leibler cluster entropy $\mathcal{D_{C}}[P \| Q] $ is evaluated for the empirical and model probability distributions $P$ and $Q$ of the clusters formed in the realized volatility time series of five assets (SP\&500, NASDAQ, DJIA, DAX, FTSEMIB). The Kullback-Leibler functional $\mathcal{D_{C}}[P \| Q] $ provides complementary perspectives about the stochastic volatility process compared to the Shannon functional $\mathcal{S_{C}}[P]$. While $\mathcal{D_{C}}[P \| Q] $ is maximum at the short time scales, $\mathcal{S_{C}}[P]$ is maximum at the large time scales leading to complementary optimization criteria tracing back respectively to the maximum and minimum relative entropy evolution principles. The realized volatility is modelled as a time-dependent fractional stochastic process characterized by power-law decaying distributions with positive correlation ($H>1/2$). As a case study, a multiperiod portfolio built on diversity indexes derived from the Kullback-Leibler entropy measure of the realized volatility. The portfolio is robust and exhibits better performances over the horizon periods. A comparison with the portfolio built either according to the uniform distribution or in the framework of the Markowitz theory is also reported.