التنبؤ باستعمال نماذج الانحدار الذاتي العامة المشروطة بعدم تجانس التباين (GARCH) الموسمية مع تطبيق عملي == Forecasting The Use of Generalized Autoregressive Conditional Heteroscedastic Models (GARCH) Seasonality With Practical Application
Author name:
بريدة برهان كاظم
Supervisor name:
فارس طاھر حسن الكواز
General topic:
Administration and Economics
Specific topic:
Statistics
Degree:
Master
University:
University of Baghdad - Faculty Of Administration And Economics - Department Of Statistics
Language:
Arabic
University location:
Baghdad
First pages:
07T3489 - p.pdf
Abstract:
مما لا شك فيه، تحظى نماذج GARCH) ) بالفاعلية والشعبية الكبيرة في نمذجة البيانات الاقتصادية والمالية، اذ تسمح للتباين المشروط بالتغير عبر الزمن، مما يجعلها اكثر واقعية في المجال الاقتصادي. وتتوفر ميزة اخرى مهمة في عالم الاقتصاد، ممثلة بالموسمية، الت | Un doubtedly , The GARCH model is very popular and effectiveness in economic and financial data , since it allows the conditional variance to vary over time , which makes them more realistic for the economic world. And there is another important characteristic in the economic world , Represented seasonality , that exist in high frequency data such as daily series , it can be seen in the real data of the exchange rate IQD/USD , Because there are seasonal conditional heteroscedasticity clearly shows in this data , Thereby are dealt with this type of data using Multiplicative seasonal generalized autoregressive conditional heteroscedastic models , Because it is proven effective to express their seasonal phenomenon on the contrary GARCH models which do not contain seasonal vehicle. hence the aim of the research reaching a better model represents the seasonal data with proof of the effectiveness of the seasonal model in preference to the usual model. it has been used to detect seasonal presence in the data first , after that was diagnosed a problem of heteroscedasticity passing through the phase estimation using the conditional maximum likelihood and assuming normal distribution of errors , then determine the appropriate rank of the model using a number of special criterian Represented each of the Akaike Information Criterion (AIC), Schwartz Information Criterion (SIC) , Hannan Quinn Information Criterion (H - Q), down to the stage to predict , using two method to predict the first is the prediction in the sample , which objective was to infer the efficiency of the preferred model and the second way forecasting out of sample any prediction of future values.it is found through the application on the study data stages that the best model for predicting volatility is SGARCH (1,0)(1,0).
