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

Jan Frederik Mai, Aleksandra Blagoeva, Matthias Scherer

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Pages (from-to)522-553
Number of pages32
JournalFrontiers of Mathematical Finance
Volume2
Issue number4
DOIs
StatePublished - Dec 2023

Keywords

  • Marshall–Olkin distribution
  • Portfolio selection
  • credit-risk modeling
  • power utility maximization
  • stochastic gradient descent algorithm

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