Robust Optimization of Credit Portfolios
We introduce a dynamic credit portfolio framework where optimal investment strategies are robust against misspecifications of the reference credit model. The risk-averse investor models his fear of credit risk misspecification by considering a set of plausible alternatives whose expected log likelih...
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Veröffentlicht in: | Mathematics of operations research 2017-02, Vol.42 (1), p.30-56 |
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creator | Bo, Lijun Capponi, Agostino |
description | We introduce a dynamic credit portfolio framework where optimal investment strategies are robust against misspecifications of the reference credit model. The risk-averse investor models his fear of credit risk misspecification by considering a set of plausible alternatives whose expected log likelihood ratios are penalized. We provide an explicit characterization of the optimal robust bond investment strategy, in terms of default state dependent value functions associated with the max-min robust optimization criterion. The value functions can be obtained as the solutions of a recursive system of Hamilton-Jacobi-Bellman (HJB) equations. We show that each HJB equation is equivalent to a suitably truncated equation admitting a unique bounded regular solution. The truncation technique relies on estimates for the solution of the master HJB equation that we establish. |
doi_str_mv | 10.1287/moor.2016.0790 |
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subjects | Analysis Credit management Credit risk Hamilton-Jacobi equations HJB equations Investment policy Investment strategy Mathematical models Mathematical optimization Methods Operations research Optimization Portfolio management recursive system Risk aversion robust control Robustness Studies |
title | Robust Optimization of Credit Portfolios |
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