Financial Time Series: Exploration, Linear and Volatile
This course is concerned with the statistical theory and practice of financial time series modelling and forecasting. It is in three parts. Part 1 covers exploratory statistical methods for various features, such as trend, drift, seasonality, linear and non-linear dependence, short and long memory, dependence, directionality, volatility and volatility-dependence. Part 2 is concerned mostly with traditional linear modelling. Part 3 presents nonlinear models in a mathematically accessible manner, firstly just involving volatility, and then with extensions incorporating local behaviour in both level and volatility. Explicit results for nonlinear models mean that they are treated in parallel detail to those for linear models. Illustrations throughout use stock prices, index series, interest rate series and exchange rate series, and guidance on the use of easily availble packages is included.