dc.contributor.advisor | Tatlıdil, Hüseyin | |
dc.contributor.author | Altun, Emrah | |
dc.date.accessioned | 2018-03-20T10:35:43Z | |
dc.date.available | 2018-03-20T10:35:43Z | |
dc.date.issued | 2018 | |
dc.date.submitted | 2018 | |
dc.identifier.uri | http://hdl.handle.net/11655/4406 | |
dc.description.abstract | Most of the Value-at-Risk models assume that financial returns are normally distributed, despite the fact that they are commonly known to be left skewed, fat-tailed and excess kurtosis. Forecasting Value-at-Risk with misspecified model leads to the underestimation or overestimation of the true Value-at-Risk. This study proposes new conditional models to forecast the daily Value-at-Risk by employing the new fat-tailed and skewed distributions to GARCH models. Empirical results show that the fat-tailed and skewed distributions provide superior fit to the conditional distribution of the logreturns among others. Backtesting methodology and loss functions are used to compare the out-of-sample performance of Value-at-Risk models. We conclude that the effects of skewness and fat-tails are more important than only the effect of the fat-tails on accuracy of Value-at-Risk forecasts. | tr_TR |
dc.language.iso | en | tr_TR |
dc.publisher | Fen Bilimleri Enstitüsü | tr_TR |
dc.rights | info:eu-repo/semantics/closedAccess | tr_TR |
dc.subject | GARCH modelleri | tr_TR |
dc.subject | Riske Maruz Değer | tr_TR |
dc.subject | Geriye Dönük Test | tr_TR |
dc.title | THE IMPORTANCE OF FAT-TAILED AND SKEWED DISTRIBUTIONS IN MODELING VALUE-AT-RISK | tr_TR |
dc.type | info:eu-repo/semantics/doctoralThesis | tr_TR |
dc.description.ozet | Most of the Value-at-Risk models assume that financial returns are normally distributed, despite the fact that they are commonly known to be left skewed, fat-tailed and excess kurtosis. Forecasting Value-at-Risk with misspecified model leads to the underestimation or overestimation of the true Value-at-Risk. This study proposes new conditional models to forecast the daily Value-at-Risk by employing the new fat-tailed and skewed distributions to GARCH models. Empirical results show that the fat-tailed and skewed distributions provide superior fit to the conditional distribution of the logreturns among others. Backtesting methodology and loss functions are used to compare the out-of-sample performance of Value-at-Risk models. We conclude that the effects of skewness and fat-tails are more important than only the effect of the fat-tails on accuracy of Value-at-Risk forecasts. | tr_TR |
dc.contributor.department | İstatistik | tr_TR |