Chapter 9: Model-Based Feature Extraction

Volatility Models as Feature Extractors: GARCH, EGARCH, and HAR intermediate

Chapter 9 already teaches what volatility-model outputs to extract. This primer narrows to the harder layer underneath that recipe: persistence geometry, parameter interpretation, and how to tell when the fitted risk state is statistically meaningful rather than just mechanically smooth.

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References

Volatility forecast comparison using imperfect volatility proxies

Andrew J. Patton (2011) — Journal of Econometrics

Conditional Heteroskedasticity in Asset Returns: A New Approach

Daniel B. Nelson (1991) — Econometrica

A Simple Approximate Long-Memory Model of Realized Volatility

Fulvio Corsi (2009) — Journal of Financial Econometrics

Generalized autoregressive conditional heteroskedasticity

Tim Bollerslev (1986) — Journal of Econometrics

Chapter

9 Model-Based Feature Extraction

More Topics

Autoregressive, Moving-Average, and ARIMA Foundations for Feature Engineering Bayesian Inference and MCMC for Time Series Fractional Differencing: Keeping Memory Without Keeping the Unit Root Path Signatures and Log-Signatures for Financial Sequences Regime Models for Feature Engineering: HMMs, Markov Switching, and Distributional Clustering State-Space Models and Kalman Filtering for Feature Engineering Stationarity Tests: ADF, KPSS, and Rolling Stability Signals Structural Break Diagnostics and Time-Since-Break Features Uncertainty as a Feature Wavelets for Multi-Scale Diagnostics and Causal Feature Design