What Is Model Risk?
Model risk is a type of risk that occurs when a financial model is used to measure quantitative information such as a firm’s market risks or value transactions, and the model fails or performs inadequately and leads to adverse outcomes for the firm.
A model is a system, quantitative method, or approach that relies on assumptions and economic, statistical, mathematical, or financial theories and techniques. The model processes data inputs into a quantitative-estimate type of output.
Financial institutions and investors use models to identify the theoretical value of stock prices and to pinpoint trading opportunities. While models can be useful tools in investment analysis, they can also be prone to various risks that can occur from the usage of inaccurate data, programming errors, technical errors, and misinterpretation of the model’s outputs.
KEY TAKEAWAYS
In finance, models are used extensively to identify potential future stock values, pinpoint trading opportunities, and help company managers make business decisions.
Model risk is present whenever an insufficiently accurate model is used to make decisions.
Model risk can stem from using a model with bad specifications, programming or technical errors, or data or calibration errors.
Model risk can be reduced with model management such as testing, governance policies, and independent review.
Understanding Model Risk
Model risk is considered a subset of operational risk, as model risk mostly affects the firm that creates and uses the model. Traders or other investors who use a given model may not completely understand its assumptions and limitations, which limits the usefulness and application of the model itself.
In financial companies, model risk can affect the outcome of financial securities valuations, but it’s also a factor in other industries. A model can incorrectly predict the probability of an airline passenger being a terrorist or the probability or a fraudulent credit card transaction. This can be due to incorrect assumptions, programming or technical errors, and other factors that increase the risk of a poor outcome.
JPMorgan Chase
Almost 15 years later, JPMorgan Chase (JPM) suffered massive trading losses from a value at risk (VaR) model that contained formula and operational errors. Risk managers use VaR models to estimate the future losses a portfolio could potentially incur. In 2012, CEO Jamie Dimon’s proclaimed “tempest in a teapot” turned out to be a $6.2 billion loss resulting from trades gone wrong in its synthetic credit portfolio (SCP).2
A trader had established large derivative positions that were flagged by the VaR model that existed at the time. In response, the bank’s chief investment officer made adjustments to the VaR model, but due to a spreadsheet error in the model, trading losses were allowed to pile up without warning signals from the model.
This was not the first time that VaR models have failed. In 2007 and 2008, VaR models were criticized for failing to predict the extensive losses many banks suffered during the global financial crisis.