VALUE-AT-RISK PREDICTION FOR ARCH(1) AND SVAR(1) MODELS

<br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> Value-at-Risk is a risk measure to predict maximum loss of assets. In this thesis, we are c...

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Main Author: TYAS RAHMADANI (NIM :10108098); Pembimbing : Khreshna I.A. Syuhada, M.Sc, Ph.D, NURUL
Format: Final Project
Language:Indonesia
Online Access:https://digilib.itb.ac.id/gdl/view/16760
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:16760
spelling id-itb.:167602017-09-27T11:42:59ZVALUE-AT-RISK PREDICTION FOR ARCH(1) AND SVAR(1) MODELS TYAS RAHMADANI (NIM :10108098); Pembimbing : Khreshna I.A. Syuhada, M.Sc, Ph.D, NURUL Indonesia Final Project INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/16760 <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> Value-at-Risk is a risk measure to predict maximum loss of assets. In this thesis, we are concerned with VaR prediction using volatility models, namely Autoregressive Conditional Heteroscedastic (ARCH) and Stochastic Volatility Autoregressive (SVAR). Both models have differences in estimating parameters and determining VaR prediction. Maximum likelihood method is used for estimating parameters of ARCH(1) model, while SVAR(1) model uses maximum likelihood-efficient important sampling method. Then, we will do backtesting to evaluate VaR prediction using coverage probability and correct VaR. Simulations have carried out to estimate parameters and predict the VaR for ARCH(1) and SVAR(1) models. 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 <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> <br /> Value-at-Risk is a risk measure to predict maximum loss of assets. In this thesis, we are concerned with VaR prediction using volatility models, namely Autoregressive Conditional Heteroscedastic (ARCH) and Stochastic Volatility Autoregressive (SVAR). Both models have differences in estimating parameters and determining VaR prediction. Maximum likelihood method is used for estimating parameters of ARCH(1) model, while SVAR(1) model uses maximum likelihood-efficient important sampling method. Then, we will do backtesting to evaluate VaR prediction using coverage probability and correct VaR. Simulations have carried out to estimate parameters and predict the VaR for ARCH(1) and SVAR(1) models.
format Final Project
author TYAS RAHMADANI (NIM :10108098); Pembimbing : Khreshna I.A. Syuhada, M.Sc, Ph.D, NURUL
spellingShingle TYAS RAHMADANI (NIM :10108098); Pembimbing : Khreshna I.A. Syuhada, M.Sc, Ph.D, NURUL
VALUE-AT-RISK PREDICTION FOR ARCH(1) AND SVAR(1) MODELS
author_facet TYAS RAHMADANI (NIM :10108098); Pembimbing : Khreshna I.A. Syuhada, M.Sc, Ph.D, NURUL
author_sort TYAS RAHMADANI (NIM :10108098); Pembimbing : Khreshna I.A. Syuhada, M.Sc, Ph.D, NURUL
title VALUE-AT-RISK PREDICTION FOR ARCH(1) AND SVAR(1) MODELS
title_short VALUE-AT-RISK PREDICTION FOR ARCH(1) AND SVAR(1) MODELS
title_full VALUE-AT-RISK PREDICTION FOR ARCH(1) AND SVAR(1) MODELS
title_fullStr VALUE-AT-RISK PREDICTION FOR ARCH(1) AND SVAR(1) MODELS
title_full_unstemmed VALUE-AT-RISK PREDICTION FOR ARCH(1) AND SVAR(1) MODELS
title_sort value-at-risk prediction for arch(1) and svar(1) models
url https://digilib.itb.ac.id/gdl/view/16760
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