THE USE OF HETEROSCEDASTIC MODEL FOR RISK PREDICTION
The first and the second order of GARCH model can be used in volatility modeling of stock price return, whose the risk then will be predicted. Being presented, firstly, the stationarity and parameter estimation of the model. It turns out stationarity and parameter estimation are able to a effect the...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/21744 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | The first and the second order of GARCH model can be used in volatility modeling of stock price return, whose the risk then will be predicted. Being presented, firstly, the stationarity and parameter estimation of the model. It turns out stationarity and parameter estimation are able to a effect the accuracy of the prediction obtained. Secondly, the empirical facts of return and volatility in statistical framework that should be incorporated in a model. First Order of GARCH model is then proven to be able to capture these empirical facts discussed. Hence, GARCH is an adequate model to result an accurate one-step-ahead Value-at-Risk (VaR) prediction. By involving one of the returns empirical fact, i.e. heavy tail distribution, the more accurate VaR prediction is obtained. This result could better accommodate extreme risk value. |
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