Common Univariate Random Variables Explained for Risk Managers

Описание: Link to the Presentation: https://riskcorner.gumroad.com/l/srrftv

In this episode of The Risk Manager’s Corner, we break down the most common univariate random variables — the core building blocks of every quantitative risk model.

If you work in credit, market, operational, or insurance risk, understanding these distributions isn’t optional — it’s fundamental. We go beyond definitions and explain when to use each model, what assumptions they rely on, and how they behave in real-world financial data.

You’ll learn:
✅ The intuition and practical use of Bernoulli, Binomial, Poisson, Uniform, Normal, and Lognormal distributions
✅ Why heavy-tailed models like Student T and mixtures capture real risk better than normal assumptions
✅ How Beta and Exponential distributions fit probability and waiting-time models
✅ The statistical backbone behind Chi-squared and F tests used in model validation
✅ Practical rules for model selection, validation, and stress testing

By the end, you’ll understand how to match the right distribution to your data and risk exposure — and stop making the common mistake of forcing normality on everything.

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