Phase 12
Time-Series Analysis
Phase 12 of the Quant Academy curriculum.
12.1Full Lesson
Stationarity, Autocorrelation, and Partial Autocorrelation
What makes a series analyzable, how the ACF and PACF fingerprint its memory, and why financial series so rarely cooperat
12.2Full Lesson
AR, MA, and ARMA Models
The three linear building blocks of stationary time series, their stationarity and invertibility conditions, and how to
12.3Full Lesson
ARIMA and Forecasting
Differencing away a unit root, the Box-Jenkins loop, and how to produce point forecasts with honest, widening prediction
12.4Full Lesson
Volatility Models: ARCH and GARCH
Why returns are unpredictable in the mean but not in the variance, how GARCH captures volatility clustering, and what it
12.5Full Lesson
State-Space Models and the Kalman Filter
A unifying framework for dynamic latent variables, and the recursive predict-update algorithm that is optimal for linear
12.6Full Lesson
Cointegration, VAR, and Backtest Pitfalls
Modeling several series together, the long-run equilibrium of cointegrated pairs, and the research-integrity traps that