for financial institutions, risk modeling is common practice to identify, assess, control, and monitor risk. mathematical risk models and statistical methods applied in matlab® (e.g., regression, monte carlo simulation, and copulas) are used by risk professionals to quantify the impact of risk, optimize capital allocation, accelerate regulatory submission, and enable more risk-based service offerings.
this ebook is a practical guide to modeling financial risk with matlab and provides access to applied examples, documentation, and user stories. learn more about:
- types of financial risk models in matlab, including credit risk, market risk, operational risk, systemic risk, liquidity risk, concentration risk, capital risk, and value at risk
- how to improve your product offerings through automated risk-integrated service improvements
- how you can adapt risk models in matlab to conform to new regulations and address new types of risk factors, reducing project time
- real-world application of mathematical modeling and statistical methods with matlab