The purpose of this course is to introduce students to various time series modeling/techniques and their applications in financial markets and management decisions. These techniques are useful for making forecast of time series variables. In doing so, the instructor will touch upon the following popular time series forecasting techniques: ARIMA Models, ARCH model, GARCH Model, ARCH-M model, TARCH model, EGARCH model, PARCH model, VAR Models, and Vector Error Correction Models.
The instructor initially will introduce all basics of time series including its characteristics, internal structures, stationary and non-stationary series, trend and difference stationary series, lag and lead operators, correlogram, dynamic multiplier effects, world’s decomposition theorem and invertibility condition, random walk series, stochastic and deterministic trends, features of time series like volatility clustering, leverage effect and leptokurtosis and co integrated series.
The instructor will also explain various test statistics used in deciding the model, testing for stationarity, co-integration etc. which include unit root test, info criteria, ARCH test, LLH Ratio test, Lag Exclusion Test, JB test, White test, LM test, Granger Causality test, Block exogeneity test, and Johansen test. He will also explain the tests and models with appropriate examples.
The instructor will also introduce the E-views econometric software and demonstrate how these software can be used to estimate variety of time series models and make forecast of the given financial time series variables. For this purpose, the instructor will use real world data so that students will learn the current trend of various financial variables and their forecasts in future periods.
