Portfolio Diversification Revisited

TL;DR

This paper revisits portfolio diversification by relaxing traditional assumptions, employing a Markov-modulated Levy process to better match empirical asset return distributions, and highlights potential risks of standard assumptions.

Contribution

It introduces a calibrated Levy process model accounting for complex return distributions, improving the realism of diversification analysis.

Findings

Calibrated model matches empirical moments well

Relaxed assumptions reveal higher diversification risks

Highlights limitations of traditional models

Abstract

We relax a number of assumptions in Alexeev and Tapon (2012) in order to account for non-normally distributed, skewed, multi-regime, and leptokurtic asset return distributions. We calibrate a Markov-modulated Levy process model to equity market data to demonstrate the merits of our approach, and show that the calibrated models do a good job of matching the empirical moments. Finally, we argue that much of the related literature on portfolio diversification relies on assumptions that are in tension with certain observable regularities and which, if ignored, may lead to underestimation of risk.