Exploring Optimisation Strategies Under Jump-Diffusion Dynamics

This paper addresses the portfolio optimisation problem within the jump-diffusion stochastic differential equations (SDEs) framework. We begin by recalling a fundamental theoretical result concerning the existence of solutions to the Black–Scholes–Merton partial differential equation (PDE), which serves as the cornerstone for subsequent analysis. Then, we explore a range of financial applications, spanning scenarios characterised by the absence of jumps, the presence of jumps following a log-normal distribution, and jumps following a distribution of greater generality. Additionally, we delve into optimising more complex portfolios composed of multiple risky assets alongside a risk-free asset, shedding new light on optimal allocation strategies in these settings. Our investigation yields novel insights and potentially groundbreaking results, offering fresh perspectives on portfolio management strategies under jump-diffusion dynamics.

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Publication Details

Journal
Mathematics
Published
2025-02-06
DOI
https://doi.org/10.3390/math13030535
Primary Topic
Stochastic processes and financial applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Exploring Optimisation Strategies Under Jump-Diffusion Dynamics

Nicola Fraccarolo, Luca Di Persio
Mathematics
Stochastic processes and financial applications
article

Exploring Optimisation Strategies Under Jump-Diffusion Dynamics

Nicola Fraccarolo, Luca Di Persio
article en

Abstract

This paper addresses the portfolio optimisation problem within the jump-diffusion stochastic differential equations (SDEs) framework. We begin by recalling a fundamental theoretical result concerning the existence of solutions to the Black–Scholes–Merton partial differential equation (PDE), which serves as the cornerstone for subsequent analysis. Then, we explore a range of financial applications, spanning scenarios characterised by the absence of jumps, the presence of jumps following a log-normal distribution, and jumps following a distribution of greater generality. Additionally, we delve into optimising more complex portfolios composed of multiple risky assets alongside a risk-free asset, shedding new light on optimal allocation strategies in these settings. Our investigation yields novel insights and potentially groundbreaking results, offering fresh perspectives on portfolio management strategies under jump-diffusion dynamics.

MathematicsVol. 13(3)
University of Verona (IT), University of Trento (IT)
European Commission
Openalex Percentile: Top 97%
Stochastic processes and financial applications
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