Model financial time series with stationarity tests, ARIMA, GARCH, cointegration and state-space methods, and evaluate forecasts with disciplined out-of-sample testing.
Duration8 weeks
LevelIntermediate
Modules8
Lessons32
This course teaches the econometric methods used to model and forecast financial time series. You will analyze stationarity and autocorrelation, fit ARIMA and GARCH-family models, study cointegration and vector autoregressions, and use state-space models with the Kalman filter. The course emphasizes diagnostic checking, regime awareness and out-of-sample evaluation so that models are understood as tools with clear assumptions.
8 modules · 32 lessons · 8 weeks. Curriculum preview; the final syllabus may change before enrollment opens.
Fit and compare ARIMA models for a macroeconomic series and document the diagnostic process
Build a GARCH-based volatility forecaster and evaluate it against realized volatility
Test pairs of related assets for cointegration and track a time-varying hedge ratio with a Kalman filter
Quantzivo certificates are completion credentials issued by Quantzivo, not accredited qualifications. Courses teach technical and research skills; they do not promise investment returns or provide investment advice.
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