(Objectives / الأهداف)
- To learn the basic features of financial time series data.
- To learn some of the tools of financial econometrics employed in finance for the purpose of forecasting return volatility and examining the co-integration and correlation betyouen variables.
- To learn various metrics for evaluating the performance of several models for forecasting.
- To learn new techniques in quantitative finance used for the purpose of decomposing the data into different frequencies (Mixed data sampling).
(Contents / المحتويات)
- The Econometrics of Financial Returns and the main models for volatility forecasting:
- Calculating the return and the main descriptive statistics.
- Steps involved in formulating an econometric model.
- Types of data.
- Hypothesis testing in EViews ---hedging.
- Forecasting the stock market return volatility with the generalized autoregressive conditional heteroskedasticity model, the EGARCH (exponential GARCH) model, the CGARCH (component GARCH) model and the TGRACH (threshold GARCH) model.
- Univariate timeseries modelling and forecasting:
- Observing the main differences between the forecasting for daily the return and the monthly return.
- Performing the out-of-sample test and comparing between the forecasting performances of different GARCH models.
- Examining the effect of the macroeconomic news on the volatility.
- Constructing the autoregressive moving average (ARMA) model in EViews.
- Forecasting using ARMA models in EViews.
- Multivariate model :
- Impulse responses and variance decompositions.
- Applying the Vector autoregressive modelto examine the co-integration and causality betyouen the stock market return in one side the macroeconomic news in the other side.
- Applying the causality tests.
- Modelling long-run relationships betyouen variables in finance:
- Stationarity and unit root testing.
- Testing for cointegration and modelling cointegrated systems using EViews.
- Applying the error correction models in EViews.
- Regime switching model. The model is widely used in finance to examine how such a relation can turn from one state to another based on the economic conditions in the market:
- Modelling seasonality in financial data.
- Estimating Markov switching models.
- Threshold autoregressive models.
- Moldelling dynamic correlation betyouen the finance (economic) variables:
- Estimating the static and diagonal Dynamic conditional correlation GARCH model.
- The time mixed data sampling (MIDAS) model.
- The (MIDAS) model is a method of estimating and forecasting from models where the dependent variable is recorded at a lower frequency than one or more of the independent variables. Unlike the traditional aggregation approach, MIDAS uses information from every observation in the higher frequency space.
(Participants / المشاركون)
- This course is targeted at both econometrics and non-econometricsstudents that have an understanding of basic statistics and time-series analysis.
- Bankers who have interest in forecasting real data in order to make more accurate decisions in the future.
- People working in the financial markets will find this course interesting due to employing time-series data from both emerging and developed markets.