Model risk

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In finance, model risk is the risk of loss resulting from using models to make decisions, initially and frequently referring to valuing financial securities.[1] However model risk is more and more prevalent in industries other than financial securities valuation, such as consumer credit score, real-time probability prediction of a fradulent credit card transaction to the probability of air flight passenger being a terrorist. Rebonato in 2002 considers alternative definitions including:

  1. After observing a set of prices for the underlying and hedging instruments, different but identically calibrated models might produce different prices for the same exotic product.
  2. Losses will be incurred because of an ‘incorrect’ hedging strategy suggested by a model.[2]

Rebonato 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."

Types of model risk[edit]

Burke regards failure to use a model (instead over-relying on expert judgment) as a type of model risk.[3] Derman describes various types of model risk that arise from using a model:[1]

Wrong model[edit]

  • Inapplicability of model.
  • Incorrect model specification.

Model implementation[edit]

  • Programming errors.
  • Technical errors.
  • Use of inaccurate numerical approximations.

Model usage[edit]

  • Implementation Risk.
  • Data issues.
  • Calibration errors.

Sources of model risk[edit]

Uncertainty on volatility[edit]

Volatility is the most important input in risk management models and pricing models. Uncertainty on volatility leads to model risk. Derman believes that products whose value depends on a volatility smile are most likely to suffer from model risk. He writes "I would think it’s safe to say that there is no area where model risk is more of an issue than in the modeling of the volatility smile."[4] Avellaneda & Paras (1995) proposed a systematic way of studying and mitigating model risk resulting from volatility uncertainty.[5]

Time inconsistency[edit]

Buraschi and Corielli formalise the concept of 'time inconsistency' with regards to no-arbitrage models that allow for a perfect fit of the term structure of the interest rates. In these models the current yield curve is an input so that new observations on the yield curve can be used to update the model at regular frequencies. They explore the issue of time-consistent and self-financing strategies in this class of models. Model risk affects all the three main steps of risk management: specification, estimation and implementation.[6]

Correlation uncertainty[edit]

Uncertainty on correlation parameters is another important source of model risk. 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.[7]

Gennheimer investigates the model risk present in pricing basket default derivatives. 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."[8]

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. This factor was cited as a major source of model risk for mortgage backed securities portfolios during the 2007 crisis.

Illiquidity and model risk[edit]

Model risk does not only exist for complex financial contracts. Frey (2000) presents a study of how market illiquidity is a source of model risk. 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."[9] Convertible bonds, mortgage backed securities, and high-yield bonds can often be illiquid and difficult to value. Hedge funds that trade these securities can be exposed to model risk when calculating monthly NAV for its investors.[10]

Quantitative approaches to Model Risk[edit]

Model averaging vs worst-case approach[edit]

Rantala (2006) mentions that "In the face of model risk, rather than to base decisions on a single selected ”best” model, the modeller can base his inference on an entire set of models by using model averaging."[11]

Another approach to model risk is the worst-case, or minmax approach, advocated in decision theory by Gilboa and Schmeidler.[12] In this approach one considers a range of models and minimizes the loss encountered in the worst-case scenario. This approach to model risk has been developed by Cont (2006).[13]

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. The problem is how to choose these benchmark models.[14] In the context of derivative pricing Cont (2006) proposes a quantitative approach to measurement of model risk exposures in derivatives portfolios: first, a set of benchmark models is specified and calibrated to market prices of liquid instruments, then the target portfolio is priced under all benchmark models. 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. Such a measure may be used as a way of determining a reserve for model risk for derivatives portfolios.

Position limits and valuation reserves[edit]

Kato and Yoshiba discuss qualitative and quantitative ways of controlling model risk. 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. 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."[15] Cont (2006) advocates the use of model risk exposure for computing such reserves.

Mitigating model risk[edit]

Theoretical basis[edit]

  • Considering key assumptions.
  • Considering simple cases and their solutions (model boundaries).
  • Parsimony.

Implementation[edit]

  • Pride of ownership.
  • Disseminating the model outwards in an orderly manner.

Testing[edit]

  • Stress testing and backtesting.
  • Avoid letting small issues snowball into large issues later on.
  • Independent validation
  • Ongoing monitoring and against market

Examples of model risk mitigation[edit]

Parsimony[edit]

Taleb wrote when describing why most new models that attempted to correct the inadequacies of the Black–Scholes model failed to become accepted:

"Traders are not fooled by the Black–Scholes–Merton model. The existence of a 'volatility surface' is one such adaptation. But they find it preferable to fudge one parameter, namely volatility, and make it a function of time to expiry and strike price, rather than have to precisely estimate another."[16]

However, Cherubini and Della Lunga describe the disadavantages of parsimony in the context of volatility and correlation modelling. Using an excessive number of parameters may induce overfitting while choosing a severely specified model may easily induce model misspecification and a systematic failure to represent the future distribution.[17]

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. 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."[18]

Case studies[edit]

See also[edit]

Notes[edit]

  1. ^ a b "Model Risk" (pdf). 1996. Retrieved September 10, 2013. 
  2. ^ http://www.quarchome.org/ModelRisk.pdf Theory and Practice of Model Risk Management
  3. ^ http://www.siiglobal.org/SII/WEB5/sii_files/Membership/PIFs/Risk/Model%20Risk%2024%2011%2009%20Final.pdf
  4. ^ Derman, Emanuel (May 26, 2003). "Laughter in the Dark: The Problem of the Volatility Smile". 
  5. ^ Avellaneda, M.; Levy, A.; Parás, A. (1995). "Pricing and hedging derivative securities in markets with uncertain volatilities". Applied Mathematical Finance 2 (2): 73–88. doi:10.1080/13504869500000005.  edit
  6. ^ 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.  edit
  7. ^ Cont, Rama; Romain Deguest (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. SSRN 1592531. 
  8. ^ Gennheimer, Heinrich (2002). "Model Risk in Copula Based Default Pricing Models". CiteSeerX: 10.1.1.139.2327. 
  9. ^ Frey, Rüdiger (2000). "Market Illiquidity as a Source of Model Risk in Dynamic Hedging". CiteSeerX: 10.1.1.29.6703. 
  10. ^ Black, Keith H. (2004). Managing a Hedge Fund. McGraw-Hill Professional. ISBN 978-0-07-143481-2. 
  11. ^ Rantala, J. (2006). "On joint and separate history of probability, statistics and actuarial science". In Liksi; et al. Festschrift for Tarmo Pukkila on his 60th Birthday. University of Tampere, Finland. pp. 261–284. ISBN 951-44-6620-9. 
  12. ^ Gilboa, I.; Schmeidler, D. (1989). "Maxmin expected utility with non-unique prior". Journal of Mathematical Economics 18 (2): 141. doi:10.1016/0304-4068(89)90018-9.  edit
  13. ^ Cont, Rama (2006). "Model uncertainty and its impact on the pricing of derivative instruments". Mathematical Finance 16 (3): 519–547. doi:10.1111/j.1467-9965.2006.00281.x. 
  14. ^ Sibbertsen; Stahl; Luedtke (November 2008). "Measuring Model Risk". Leibnitz University Discussion Paper No. 409. 
  15. ^ Kato, Toshiyasu; Yoshiba, Toshinao (December 2000). "Model Risk and Its Control". Monetary and Economic Studies. 
  16. ^ Taleb, Nassim (2010). Dynamic Hedging: Managing Vanilla and Exotic Options. New York: Wiley. ISBN 978-0-471-35347-8. 
  17. ^ Cherubini, Umberto; Lunga, Giovanni Della (2007). Structured Finance. Hoboken: Wiley. ISBN 978-0-470-02638-0. 
  18. ^ Fender, Ingo; Kiff, John (2004). "CDO rating methodology: Some thoughts on model and its implications". BIS Working Papers No. 163. SSRN 844225. 
  19. ^ "Model Validation and Backtesting". 
  20. ^ "Controlling Model Risk". 
  21. ^ Simmons, Katerina (1997). "Model Error". New England Economic Review: 17–28.  Evaluation of various finance models
  22. ^ "National Australia Bank chief promises review as share price drops". 
  23. ^ "Recipe for Disaster: The Formula That Killed Wall Street". Wired. February 23, 2009. 
  24. ^ http://www.federalreserve.gov/bankinforeg/srletters/sr1107a1.pdf SUPERVISORY GUIDANCE ON MODEL RISK MANAGEMENT

References[edit]

  • Avellaneda, M.; Levy, A.; Parás, A. (1995). "Pricing and hedging derivative securities in markets with uncertain volatilities". Applied Mathematical Finance 2 (2): 73–88. doi:10.1080/13504869500000005.  edit
  • Cont, R. (2006). "Model Uncertainty and Its Impact on the Pricing of Derivative Instruments". Mathematical Finance 16 (3): 519–547. doi:10.1111/j.1467-9965.2006.00281.x.  edit
  • 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.  edit
  • Cont, R.; Deguest, R.; Scandolo, G. (2010). "Robustness and sensitivity analysis of risk measurement procedures". Quantitative Finance 10 (6): 593–606. doi:10.1080/14697681003685597.  edit
  • Crouhy, Michel; Galai, Dan; Mark, Robert (2000). Risk Management. McGraw-Hill. ISBN 0-07-135731-9. 
  • Derman, Emanuel (1996). Model Risk. RISK. 
  • Lyons, T. J. (1995). "Uncertain volatility and the risk-free synthesis of derivatives". Applied Mathematical Finance 2 (2): 117–133. doi:10.1080/13504869500000007.  edit
  • Rebonato, R. (2001). "Managing Model Risk". 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.