Essays in credit portfolio management
Abstract/Contents
- Abstract
- Banks often seek to reduce the default risk exposure associated with their corporate loan portfolios by entering into credit derivative positions. Asset managers seek additional yield through single and multi-name credit derivatives, and must also manage their credit exposures according to disciplined and systematic risk-return analysis. This thesis explores the portfolio selection and risk measurement problem for credit sensitive financial instruments. In comparison to its equity counterpart, the fixed-income portfolio problem presents unique challenges: the risk of issuer default induces skewed return distributions, the correlation of defaults influences the tail of the portfolio return distribution, and credit derivative positions have complex risk-return implications. The first part of this dissertation addresses the static selection problem for a fixed-income portfolio of credit sensitive securities. We optimize the total mark-to-market value of the portfolio at the investment horizon. This value incorporates the intermediate premium and default cash flows of long and short cash and derivative positions, and the survival-contingent market value of these positions at the horizon. The selection problem is cast as a polynomial goal program that involves a two-stage constrained optimization of preference weighted moments of the portfolio mark-to-market. The decision variable is the vector of contract notionals. A capital constraint guarantees the solvency of the investor. The multi-moment formulation addresses the non-Gaussian distribution of the portfolio mark-to-market. It is also computationally tractable, because we obtain analytical expressions for the moments of the portfolio mark-to-market, which are given in terms of nested expectations under risk-neutral and actual probability measures. The expressions are valid for a broad class of intensity-based, doubly-stochastic models of correlated default timing that are widely used in portfolio credit risk and derivatives pricing. Numerical results illustrate the implications for portfolio selection of idiosyncratic default risk and default correlation. They also indicate the robustness of the optimal policies with respect to estimation errors. Although higher moments provide important characterizations of the portfolio risk profile, investment managers often need to compute specific tail percentiles of the profit and loss distribution. In the second part of the thesis we develop an analytical approximation for this distribution. The approximation is based on a small-time expansion of a transform of the portfolio value. The analytical characterization permits tractable computations of Value-at-Risk, and Value-at-risk constrained optimal portfolio selections.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2012 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Kim, June Ho |
|---|---|
| Associated with | Stanford University, Department of Management Science and Engineering |
| Primary advisor | Giesecke, Kay |
| Thesis advisor | Giesecke, Kay |
| Thesis advisor | Primbs, James |
| Thesis advisor | Weyant, John P. (John Peter) |
| Advisor | Primbs, James |
| Advisor | Weyant, John P. (John Peter) |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Jack Kim. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2012. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2012 by June Ho Kim
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