Model risk

What is 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. However, model risk is more and more prevalent in activities other than financial securities valuation, such as assigning consumer credit scores, real-time probability prediction of fraudulent credit card transactions, and computing the probability of air flight passenger being a terrorist. Rebonato in 2002 considers alternative definitions including:

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.
Losses will be incurred because of an ‘incorrect’ hedging strategy suggested by a model.
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.”

A model is “a quantitative method, system, or approach that applies statistical, economic, financial, or mathematical theories, techniques, and assumptions to process input data into quantitative estimates.” Model risk is the risk associated with using financial models that are inherently flawed. It occurs when a model used to measure a value does not perform as planned. It is a subset of operational risk; it mainly impacts the firm that develops and utilizes the model.

Model risk is a type of risk that occurs when a financial model used to measure a firm’s market risks or value transactions does not perform the tasks or capture the risks it was designed to. Model risk is considered a subset of operational risk, as model risk mostly affects the firm that creates and uses the model.

Model risk is a probability of loss resulting from the weaknesses in the financial model used in assessing and managing a risk.

Types of Model risk

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

Wrong model:

  • Inapplicability of model.
  • Incorrect model specification.

Model implementation:

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

Model usage:

  • Implementation risk.
  • Data issues.
  • Calibration errors.

Sources of Model risk

Uncertainty on volatility

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.” Avellaneda & Paras (1995) proposed a systematic way of studying and mitigating model risk resulting from volatility uncertainty.

Time inconsistency

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.

Correlation uncertainty

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.

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.”


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

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.” 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.

Quantitative approaches to Model risk

Model averaging vs worst-case approach

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.”

Another approach to model risk is the worst-case, or minmax approach, advocated in decision theory by Gilboa and Schmeidler. 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).

Quantifying model risk exposure

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. 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

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.” Cont (2006) advocates the use of model risk exposure for computing such reserves.

Mitigating Model risk

Theoretical basis:

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


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


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