Return volatility is not constant — it clusters. This lecture develops the (generalised) autoregressive conditional heteroskedasticity framework: starting from a conditional mean model Rₜ = μₜ + uₜ, it builds ARCH and GARCH specifications for the conditional variance, and shows how they capture the volatility dynamics visible in real returns.
What these materials cover
- Conditional vs unconditional variance; volatility clustering
- The ARCH model: specification and properties
- GARCH: generalising ARCH with lagged conditional variance
- Estimating volatility models and interpreting the output
- Applications to risk measurement and forecasting
Download
Free to download and use for personal study. Written for my own university teaching; shared here as evidence of teaching style and depth.
Lecture slides: advanced volatility modelling (ARCH/GARCH) (PDF)
Who this is for
MSc finance and econometrics students studying volatility models, and dissertation students fitting GARCH to returns data.
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Related free resources
- Financial Econometrics — all lecture slides & problem sets
- GARCH and volatility modelling explained — study note
- Modelling the distribution of stock returns — study note
- All teaching materials — notes, exercises and solutions
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