ARCH EFFECTS AND VOLATILITY MODEL FOR RISK PREDICTION
An asset loss is defined as negative return from its asset. The loss value that ever changing over time can be modelled by using stochastic model. Volatility model can be used to accommodate the changing loss value. One of the common assumption in volatility modelling is Heteroscedastic. A stochasti...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/28230 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| id |
id-itb.:28230 |
|---|---|
| spelling |
id-itb.:282302018-05-11T14:05:09ZARCH EFFECTS AND VOLATILITY MODEL FOR RISK PREDICTION ANDREAS (NIM: 10114052), JANSEN Indonesia Final Project INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/28230 An asset loss is defined as negative return from its asset. The loss value that ever changing over time can be modelled by using stochastic model. Volatility model can be used to accommodate the changing loss value. One of the common assumption in volatility modelling is Heteroscedastic. A stochastic process exhibiting dependence in the return and heteroscedasticity is said to have ARCH effects. In this final project, Lagrange Multiplier and Ljung-Box test are used to assess the ARCH effects. GARCH(1,1) is one of the popular volatility model that use the past information including the loss and volatility value. The aim of using GARCH(1,1) is to produce a volatility prediction that it will be used in risk measure prediction. Value-at-Risk (VaR) is going to be used as risk measure and it will be evaluated by coverage probability. text |
| institution |
Institut Teknologi Bandung |
| building |
Institut Teknologi Bandung Library |
| continent |
Asia |
| country |
Indonesia Indonesia |
| content_provider |
Institut Teknologi Bandung |
| collection |
Digital ITB |
| language |
Indonesia |
| description |
An asset loss is defined as negative return from its asset. The loss value that ever changing over time can be modelled by using stochastic model. Volatility model can be used to accommodate the changing loss value. One of the common assumption in volatility modelling is Heteroscedastic. A stochastic process exhibiting dependence in the return and heteroscedasticity is said to have ARCH effects. In this final project, Lagrange Multiplier and Ljung-Box test are used to assess the ARCH effects. GARCH(1,1) is one of the popular volatility model that use the past information including the loss and volatility value. The aim of using GARCH(1,1) is to produce a volatility prediction that it will be used in risk measure prediction. Value-at-Risk (VaR) is going to be used as risk measure and it will be evaluated by coverage probability. |
| format |
Final Project |
| author |
ANDREAS (NIM: 10114052), JANSEN |
| spellingShingle |
ANDREAS (NIM: 10114052), JANSEN ARCH EFFECTS AND VOLATILITY MODEL FOR RISK PREDICTION |
| author_facet |
ANDREAS (NIM: 10114052), JANSEN |
| author_sort |
ANDREAS (NIM: 10114052), JANSEN |
| title |
ARCH EFFECTS AND VOLATILITY MODEL FOR RISK PREDICTION |
| title_short |
ARCH EFFECTS AND VOLATILITY MODEL FOR RISK PREDICTION |
| title_full |
ARCH EFFECTS AND VOLATILITY MODEL FOR RISK PREDICTION |
| title_fullStr |
ARCH EFFECTS AND VOLATILITY MODEL FOR RISK PREDICTION |
| title_full_unstemmed |
ARCH EFFECTS AND VOLATILITY MODEL FOR RISK PREDICTION |
| title_sort |
arch effects and volatility model for risk prediction |
| url |
https://digilib.itb.ac.id/gdl/view/28230 |
| _version_ |
1822922511511191552 |