Financial econometrics starts with a simple question: what do stock returns actually look like statistically? This lecture builds the distribution-theory toolkit — moments, skewness, kurtosis, sampling and moment estimation — and applies it to real financial return data, setting up the fat-tails and volatility-clustering facts the rest of the course explains.
What these materials cover
- What financial econometrics is, with real data examples
- Distribution theory: moments, skewness, kurtosis
- Sampling and moment estimation for returns data
- The stylised facts of stock returns the course will model
- Foundation for ARCH/GARCH volatility modelling later in the course
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: distribution theory & stock returns (week 1) (PDF)
Who this is for
MSc finance and financial econometrics students, and quantitative-finance students who need the statistical foundations of return modelling.
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Related free resources
- Financial Econometrics — all lecture slides & problem sets
- Modelling the distribution of stock returns — study note
- Distributions in econometrics: normal, t, chi-squared and F
- All teaching materials — notes, exercises and solutions
One-to-one help
For help with this material — or the module it belongs to — see financial econometrics tuition or finance tuition. The first consultation is free, with no obligation.
Free worked video lectures: @economaths on YouTube.