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A framework to measure integrated risk

Author

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  • Elena Medova
  • Robert Smith

Abstract

A framework underlying various models that measure the credit risk of a portfolio is extended in this paper to allow the integration of credit risk with a range of market risks using Monte Carlo simulation. A structural model is proposed that allows interest rates to be stochastic and provides closed-form expressions for the market value of a firm's equity and its probability of default. This model is embedded within the integrated framework and the general approach illustrated by measuring the risk of a foreign exchange forward when there is a significant probability of default by the counterparty. For this example moving from a market risk calculation to an integrated risk calculation reduces the expected future value of the instrument by an amount that could not be calculated using the common pre-settlement exposure technique for estimating the credit risk of a derivative.

Suggested Citation

  • Elena Medova & Robert Smith, 2005. "A framework to measure integrated risk," Quantitative Finance, Taylor & Francis Journals, vol. 5(1), pages 105-121.
    • Handle: RePEc:taf:quantf:v:5:y:2005:i:1:p:105-121
    DOI: 10.1080/14697680500117583
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    Cited by:

    1. Božović, Miloš & Ivanović, Jelena, 2017. "Adverse risk interaction: An integrated approach," Economic Modelling, Elsevier, vol. 65(C), pages 67-74.
    2. Breuer, Thomas & Jandacka, Martin & Rheinberger, Klaus & Summer, Martin, 2010. "Does adding up of economic capital for market- and credit risk amount to conservative risk assessment?," Journal of Banking & Finance, Elsevier, vol. 34(4), pages 703-712, April.
    3. Zhu, Xiaoqian & Wei, Lu & Li, Jianping, 2021. "A two-stage general approach to aggregate multiple bank risks," Finance Research Letters, Elsevier, vol. 40(C).
    4. Raupach, Peter, 2015. "Calculating trading book capital: Is risk separation appropriate?," Discussion Papers 19/2015, Deutsche Bundesbank.

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