model risk

 

  • In finance, model risk is the risk of loss resulting from using insufficiently accurate models to make decisions, originally and frequently in the context of valuing financial
    securities.

  • [7] Complexity[edit] Complexity of a model or a financial contract may be a source of model risk, leading to incorrect identification of its risk factors.

  • He writes “Understanding the robustness of models used for hedging and risk-management purposes with respect to the assumption of perfectly liquid markets is therefore an
    important issue in the analysis of model risk in general.

  • Further, they provide axioms of model risk measures and define several practical examples of superposed model risk measures in the context of financial risk management and
    contingent claim pricing.

  • In the case of risk measurement models, scenario analysis can be undertaken for various fluctuation patterns of risk factors, or position limits can be established based on
    information obtained from scenario analysis.

  • Illiquidity and model risk[edit] Model risk does not only exist for complex financial contracts.

  • Cont and Deguest propose a method for computing model risk exposures in multi-asset equity derivatives and show that options which depend on the worst or best performances
    in a basket (so called rainbow option) are more exposed to model uncertainty than index options.

  • They write “From a quantitative perspective, in the case of pricing models, we can set up a reserve to allow for the difference in estimations using alternative models.

  • They introduce superposed risk measures that incorporate model risk and enables consistent market and model risk management.

  • He prices these derivatives with various copulas and concludes that “… unless one is very sure about the dependence structure governing the credit basket, any investors
    willing to trade basket default products should imperatively compute prices under alternative copula specifications and verify the estimation errors of their simulation to know at least the model risks they run”.

  • [19] Model risk premium[edit] Fender and Kiff (2004) note that holding complex financial instruments, such as CDOs, “translates into heightened dependence on these assumptions
    and, thus, higher model risk.

  • Rebonato in 2002 defines model risk as “the risk of occurrence of a significant difference between the mark-to-model value of a complex and/or illiquid instrument, and the
    price at which the same instrument is revealed to have traded in the market”.

  • Their methodology enables to harmonize market and model risk management and define limits and required capitals for risk positions.

  • A measure of exposure to model risk is then given by the difference between the current portfolio valuation and the worst-case valuation under the benchmark models.

  • Quantifying model risk exposure[edit] To measure the risk induced by a model, it has to be compared to an alternative model, or a set of alternative benchmark models.

  • Such a measure may be used as a way of determining a reserve for model risk for derivatives portfolios.

  • Model risk affects all the three main steps of risk management: specification, estimation and implementation.

  • As this risk should be expected to be priced by the market, part of the yield pick-up obtained relative to equally rated single obligor instruments is likely to be a direct
    reflection of model risk.

 

Works Cited

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Buraschi, A.; Corielli, F. (2005). “Risk management implications of time-inconsistency: Model updating and recalibration of no-arbitrage models”. Journal of Banking & Finance. 29 (11): 2883. doi:10.1016/j.jbankfin.2005.02.002.
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“Model Validation and Backtesting”. Archived from the original on 2009-04-03. Retrieved 2008-12-01.
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• Cont,
R. (2006). “Model Uncertainty and Its Impact on the Pricing of Derivative Instruments” (PDF). Mathematical Finance. 16 (3): 519–547. doi:10.1111/j.1467-9965.2006.00281.x. S2CID 16075069.
• Cont, R.; Deguest, R. (2013). “Equity Correlations Implied
by Index Options: Estimation and Model Uncertainty Analysis”. Mathematical Finance. 23 (3): 496–530. doi:10.1111/j.1467-9965.2011.00503.x. S2CID 43322093.
• Cont, R.; Deguest, R.; Scandolo, G. (2010). “Robustness and sensitivity analysis of risk
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Handbook of Risk Management. FT-Prentice Hall.
• Taleb, Nassim (2006). Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets. Wiley. ISBN 1-4000-6793-6.
• US Federal Reserve Policy http://www.federalreserve.gov/bankinforeg/srletters/sr1107a1.pdf
SUPERVISORY GUIDANCE ON MODEL RISK MANAGEMENT
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