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Calculating Value-at-Risk Using the Granularity Adjustment Method in the Portfolio Credit Risk Model with Random Loss Given Default

Author

Listed:
  • Yi-Ping Chang

    (Department of Financial Engineering and Actuarial Mathematics, Soochow University, Taiwan)

  • Jing-Xiu Lin

    (Department of Financial Engineering and Actuarial Mathematics, Soochow University, Taiwan)

  • Chih-Tun Yu

    (Department of Statistics, National Chengchi University, Taiwan)

Abstract

According to the Basel Committee on Banking Supervision (BCBS), the internal ratings-based approach of Basel II and Basel III allows a bank to calculate the Valueat-Risk (VaR) for portfolio credit risk by using its own credit risk model. In this paper we use the Granularity Adjustment (GA) method proposed by Martin and Wilde (2002) to calculate VaR in the portfolio credit risk model with random loss given default. Moreover, we utilize a Monte Carlo simulation to study the impact of concentration risk on VaR.

Suggested Citation

  • Yi-Ping Chang & Jing-Xiu Lin & Chih-Tun Yu, 2016. "Calculating Value-at-Risk Using the Granularity Adjustment Method in the Portfolio Credit Risk Model with Random Loss Given Default," Journal of Economics and Management, College of Business, Feng Chia University, Taiwan, vol. 12(2), pages 157-176, August.
    • Handle: RePEc:jec:journl:v:12:y:2016:i:2:p:157-176
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    References listed on IDEAS

    as
    1. Bruche, Max & González-Aguado, Carlos, 2010. "Recovery rates, default probabilities, and the credit cycle," Journal of Banking & Finance, Elsevier, vol. 34(4), pages 754-764, April.
    2. Gourieroux, C. & Laurent, J. P. & Scaillet, O., 2000. "Sensitivity analysis of Values at Risk," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 225-245, November.
    3. Gordy, Michael B., 2003. "A risk-factor model foundation for ratings-based bank capital rules," Journal of Financial Intermediation, Elsevier, vol. 12(3), pages 199-232, July.
    4. Simone Farinelli & Mykhaylo Shkolnikov, 2012. "Two Models of Stochastic Loss Given Default," Papers 1205.5369, arXiv.org, revised May 2012.
    5. Edward I. Altman & Brooks Brady & Andrea Resti & Andrea Sironi, 2005. "The Link between Default and Recovery Rates: Theory, Empirical Evidence, and Implications," The Journal of Business, University of Chicago Press, vol. 78(6), pages 2203-2228, November.
    6. Susanne Emmer & Dirk Tasche, 2003. "Calculating credit risk capital charges with the one-factor model," Papers cond-mat/0302402, arXiv.org, revised Jan 2005.
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    Cited by:

    1. Ruili Sun & Tiefeng Ma & Shuangzhe Liu & Milind Sathye, 2019. "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review," JRFM, MDPI, vol. 12(1), pages 1-34, March.
    2. Chui-Chun Tsai & Tsun-Siou Lee, 2017. "Liquidity-Adjusted Value-at-Risk for TWSE Leverage/ Inverse ETFs: A Hellinger Distance Measure Research," Journal of Economics and Management, College of Business, Feng Chia University, Taiwan, vol. 13(1), pages 53-81, February.

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    More about this item

    Keywords

    granularity adjustment method; loss given default; portfolio credit risk model; Value-at-Risk;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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