TY - JOUR

T1 - A STOCHASTIC GRADIENT DESCENT ALGORITHM TO MAXIMIZE POWER UTILITY OF LARGE CREDIT PORTFOLIOS UNDER MARSHALL–OLKIN DEPENDENCE

AU - Mai, Jan Frederik

AU - Blagoeva, Aleksandra

AU - Scherer, Matthias

N1 - Publisher Copyright:
© 2023, American Institute of Mathematical Sciences. All rights reserved.

PY - 2023/12

Y1 - 2023/12

N2 - A vector of bankruptcy times with Marshall–Olkin multivariate exponential distribution implies a simple, yet reasonable, continuous-time model for dependent credit-risky assets with an appealing trade-off between tractabil-ity and realism. Within this framework, the maximization of expected power utility of terminal wealth requires the optimization of a concave function on a polygon, a numerical problem whose complexity grows exponentially in the number of considered assets. We demonstrate how this seemingly impractical numerical problem can be solved reliably and efficiently in order to prepare the model for practical use cases. To this end, we resort to a specifically designed factor construction for the Marshall–Olkin distribution that separates dependence parameters from idiosyncratic parameters, and we develop a tailor-made stochastic gradient descent algorithm with random constraint projections for the model’s numerical implementation. Finally, we explain a new method to include transaction costs and apply the model in a real-world, high-dimensional example.

AB - A vector of bankruptcy times with Marshall–Olkin multivariate exponential distribution implies a simple, yet reasonable, continuous-time model for dependent credit-risky assets with an appealing trade-off between tractabil-ity and realism. Within this framework, the maximization of expected power utility of terminal wealth requires the optimization of a concave function on a polygon, a numerical problem whose complexity grows exponentially in the number of considered assets. We demonstrate how this seemingly impractical numerical problem can be solved reliably and efficiently in order to prepare the model for practical use cases. To this end, we resort to a specifically designed factor construction for the Marshall–Olkin distribution that separates dependence parameters from idiosyncratic parameters, and we develop a tailor-made stochastic gradient descent algorithm with random constraint projections for the model’s numerical implementation. Finally, we explain a new method to include transaction costs and apply the model in a real-world, high-dimensional example.

KW - Marshall–Olkin distribution

KW - Portfolio selection

KW - credit-risk modeling

KW - power utility maximization

KW - stochastic gradient descent algorithm

UR - http://www.scopus.com/inward/record.url?scp=85184155546&partnerID=8YFLogxK

U2 - 10.3934/fmf.2023020

DO - 10.3934/fmf.2023020

M3 - Article

AN - SCOPUS:85184155546

SN - 2769-6715

VL - 2

SP - 522

EP - 553

JO - Frontiers of Mathematical Finance

JF - Frontiers of Mathematical Finance

IS - 4

ER -