Model risk has gained renewed importance since the 2008 financial crisis. A strong contributor to model risk is the various coding errors that financial developers are prone to. In this research paper, David Eliezer, PhD, Vice President and Head of Model Validation at Numerix, explores the most common types and sources of model risk, and then outlines the best practices that practitioners can utilize in their model validation processes.

Highlights include:

  • Sources of Model Risk: Detection and Prevention
  • Challenges with Poorly Maintained Legacy Code
  • Symptoms of Unhealthy Code
  • Practices Causing Model Risk
  • Best Practices for Preventing Unhealthy Mathematical Base Code

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