What Is Model Risk?
Let me explain to you what model risk is. It's a type of risk that emerges when you use a financial model to measure things like market risks or transaction values, and that model fails or underperforms, resulting in negative consequences for your firm.
Think of a model as a system, a quantitative method, or an approach built on assumptions from economic, statistical, mathematical, or financial theories. It takes data inputs and turns them into quantitative estimates as outputs.
You, as a financial institution or investor, rely on these models to determine theoretical stock prices and spot trading opportunities. While they're helpful for investment analysis, they come with risks from inaccurate data, programming errors, technical glitches, or misinterpreting the outputs.
Key Takeaways
- In finance, models help identify future stock values, trading opportunities, and support managers in business decisions.
- Model risk exists whenever you use an inaccurate model for decision-making.
- It can arise from poor model specifications, programming or technical errors, or issues with data and calibration.
- You can reduce model risk through management practices like testing, governance policies, and independent reviews.
Understanding Model Risk
Model risk falls under operational risk, mainly impacting the firm that builds and uses the model. If you're a trader or investor using a model, you might not fully grasp its assumptions and limits, which reduces its effectiveness.
In financial firms, this risk influences securities valuations, but it appears in other sectors too. For instance, a model might wrongly predict if an airline passenger is a terrorist or if a credit card transaction is fraudulent, due to bad assumptions, errors in programming or tech, or other issues that heighten the chance of bad results.
What Does the Concept of Model Risk Tell You?
Every model simplifies reality, and with simplification comes the risk of missing something important. Assumptions and inputs can differ widely. Financial models have grown common over the past decades, thanks to better computing, software, and new securities. Before building one, companies often do a financial forecast to set expectations for future results.
Some firms, like banks, appoint a model risk officer to run a program that cuts down on losses from model issues. This includes setting up governance and policies, and assigning people to develop, test, implement, and manage models continuously.
Real World Examples of Model Risk
Consider the Long-Term Capital Management (LTCM) failure in 1998, which stemmed from model risk. A small error in their computer models ballooned due to their highly leveraged strategy. At its peak, LTCM handled over $100 billion in assets with annual returns above 40%. Even with two Nobel Prize-winning economists as shareholders, the firm collapsed because the model didn't hold up in that market.
JPMorgan Chase
Fast forward to about 15 years later, when JPMorgan Chase faced huge trading losses from a flawed value at risk (VaR) model with formula and operational errors. VaR models estimate potential future portfolio losses. In 2012, what CEO Jamie Dimon called a 'tempest in a teapot' became a $6.2 billion loss from bad trades in their synthetic credit portfolio.
A trader built large derivative positions that the existing VaR model flagged. The chief investment officer tweaked the model, but a spreadsheet error let losses accumulate without alerts. This isn't isolated—VaR models were blamed for not predicting massive bank losses in the 2007-2008 financial crisis.
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