Time-series theory applied to finance: define an MA(1) process and derive its mean, variance and autocorrelation; then take an AR(1) and construct its one- and two-step-ahead predictors. These are the exact derivations that appear in financial econometrics exams, applied to return modelling.
What these materials cover
- MA(1): definition, mean, variance and autocorrelation function
- AR(1) processes with conditional-mean innovations
- Deriving one-step and multi-step predictors
- How ARMA structure translates into return predictability
- Links back to the course lectures for each derivation
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Free to download and use for personal study. Written for my own university teaching; shared here as evidence of teaching style and depth.
Problem set 2: ARMA processes for returns (PDF)
Who this is for
MSc students practising ARMA derivations in a finance context, ahead of exams or empirical projects.
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Related free resources
- Financial Econometrics — all lecture slides & problem sets
- AR, MA and ARMA processes explained — study note
- Time series econometrics part 2 (technical)
- All teaching materials — notes, exercises and solutions
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