معهد الدراسات المصرفية

المملكة الأردنية الهاشمية

E-Views


المحاضر تاريخ البداية تاريخ النهاية توقيت البداية توقيت النهاية مكان الانعقاد عدد الساعات سجّل الآن
2018-11-18 2018-11-22 16:30 20:00 عمان 16
سجّل الآن

(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.