Objectif du cours
1. Basic principles of financial Time Series (stationarity and forecasting). Examples. 2. Simple Autoregressive Models. Lags, Autocorrelation Function. AR(p) models and their identifying/parameter estimation. Condition for stationarity. Forecasting. 3. Basic ideas of Non-Linear Optimisation. Gradient Descent. Maximal Likelihood method. Examples. 4. Simple Moving Average Models, MA(q). Estimation of coeffitients. Forecasting. Examples. 5. Different forms of ARMA(q,p) models, inversibility. Examples. 6. Artificial Neural Networks and their applications to Time Series analysis. Examples 7. Random Walks. Fractal characteristics of Times Series. 8. Discrete Choice Models. Probit and Logit. Estimation of parameters. Examples.
Linear Algebra. Matrices. Functions of several variables. Differential calculus. Random variables and probability theory. Linear Regression.
Lecturer : Alexeï Tsygvintsev
- Kennedy, “A Guide to Econometrics”
- Jeffrey Wooldridge, “Introductory Econometrics. A Modern Approach”
- William H. Greene, “Econometric analysis”