Time series
Autocorrelation, ARMA models, unit roots, GARCH and the Kalman filter.
12 planned
No page in this subject is open yet. The planned list below shows what is coming, in learning order.
Planned
In learning order. These are not pages yet.
- Autocorrelation: how a series remembers its pastIntro
- Stationarity: what has to stay fixed over timeAdvanced undergrad
- MA models and the ACF cutoffAdvanced undergrad
- Is the market a random walk? Testing return predictability with variance ratiosAdvanced undergrad
- AR(1): persistence and the φ = 1 boundaryAdvanced undergrad
- Spectral density: a time series as a mix of frequenciesGraduate
- Identifying an ARMA model with ACF and PACFAdvanced undergrad
- Forecasting and prediction intervalsAdvanced undergrad
- GARCH(1,1): why calm and turbulent periods clusterAdvanced undergrad
- Unit roots and spurious regressionAdvanced undergrad
- The Kalman filter: blending a model with noisy observationsGraduate
- Cointegration: two random walks on a leashAdvanced undergrad
How it connects
Builds on Stochastic processes Probability Regression Statistical inference Complex analysis Bayesian statistics
Leads to Market risk Computational statistics
Subjects marked “connection” are linked by a related idea, not by a prerequisite.