Dynamic investment portfolio optimization using a Multivariate Merton Model with Correlated Jump Risk

TL;DR

This paper develops a dynamic portfolio optimization method using a multivariate Merton model with correlated jumps, approximating CVaR to maximize expected wealth, supported by numerical and real data analysis.

Contribution

It introduces a novel approach combining multivariate jump risk modeling with CVaR approximation for dynamic portfolio optimization.

Findings

Effective optimization of portfolios with correlated jump risks.

Improved expected terminal wealth through the proposed method.

Validation on real datasets demonstrates practical applicability.

Abstract

In this paper, we are concerned with the optimization of a dynamic investment portfolio when the securities which follow a multivariate Merton model with dependent jumps are periodically invested and proceed by approximating the Condition-Value-at-Risk (CVaR) by comonotonic bounds and maximize the expected terminal wealth. Numerical studies as well as applications of our results to real datasets are also provided.