Open Access
April 2013 Optimal investment under multiple defaults risk: A BSDE-decomposition approach
Ying Jiao, Idris Kharroubi, Huyên Pham
Ann. Appl. Probab. 23(2): 455-491 (April 2013). DOI: 10.1214/11-AAP829

Abstract

We study an optimal investment problem under contagion risk in a financial model subject to multiple jumps and defaults. The global market information is formulated as a progressive enlargement of a default-free Brownian filtration, and the dependence of default times is modeled by a conditional density hypothesis. In this Itô-jump process model, we give a decomposition of the corresponding stochastic control problem into stochastic control problems in the default-free filtration, which are determined in a backward induction. The dynamic programming method leads to a backward recursive system of quadratic backward stochastic differential equations (BSDEs) in Brownian filtration, and our main result proves, under fairly general conditions, the existence and uniqueness of a solution to this system, which characterizes explicitly the value function and optimal strategies to the optimal investment problem. We illustrate our solutions approach with some numerical tests emphasizing the impact of default intensities, loss or gain at defaults and correlation between assets. Beyond the financial problem, our decomposition approach provides a new perspective for solving quadratic BSDEs with a finite number of jumps.

Citation

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Ying Jiao. Idris Kharroubi. Huyên Pham. "Optimal investment under multiple defaults risk: A BSDE-decomposition approach." Ann. Appl. Probab. 23 (2) 455 - 491, April 2013. https://doi.org/10.1214/11-AAP829

Information

Published: April 2013
First available in Project Euclid: 12 February 2013

zbMATH: 1269.91075
MathSciNet: MR3059266
Digital Object Identifier: 10.1214/11-AAP829

Subjects:
Primary: 60J75 , 91B28 , 93E20

Keywords: dynamic programming , multiple defaults , optimal investment , progressive enlargement of filtrations , quadratic backward stochastic differential equations

Rights: Copyright © 2013 Institute of Mathematical Statistics

Vol.23 • No. 2 • April 2013
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