(CONDITIONAL) VALUE-AT-RISK PREDICTION ON RETURN HETEROSCEDASTIC MODEL
Risk in financial sector could be predicted using risk measure. Two risk measures that are commonly used, especially in finance, are Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). In practice, Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) are used for predicting return that...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/84155 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Risk in financial sector could be predicted using risk measure. Two risk measures that
are commonly used, especially in finance, are Value-at-Risk (VaR) and Conditional
Value-at-Risk (CVaR). In practice, Value-at-Risk (VaR) and Conditional Value-at-Risk
(CVaR) are used for predicting return that has special properties such as fat-tailed and
heteroscedasticity property. These properties, however, could be used for modelling
return volatility. Two most commonly used for volatility modelling are Autoregressive
Conditional Heteroscedasticity (ARCH) and Generalized Autoregressive Conditional
Heteroscedasticity (GARCH). These model will be used for VaR and CVaR future
prediction. Distribution assumptions for predictions are normal distribution and tstudent
distribution. Evaluation on these predictions shows that t-student distribution is
more appropriate to predict extreme value as it has a heavy tail property. |
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