VOLATILITY CHANGES THROUGH MARKOV SWITCHING ARCH MODEL FOR VALUE-AT-RISK PREDICTION

Markov Swithcing Autoregressive Conditional Heteroscedastic (MSARCH) model provides a description of return uctuation for low and high volatilities. Return behavior with volatility changes is interesting topic, in particular for Value-at-Risk (VaR) prediction. In this thesis, we employ a Markov...

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Main Author: Nugroho, Setyo
Format: Theses
Language:Indonesia
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Online Access:https://digilib.itb.ac.id/gdl/view/33940
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Institution: Institut Teknologi Bandung
Language: Indonesia
id id-itb.:33940
spelling id-itb.:339402019-01-31T10:49:50ZVOLATILITY CHANGES THROUGH MARKOV SWITCHING ARCH MODEL FOR VALUE-AT-RISK PREDICTION Nugroho, Setyo Matematika Indonesia Theses heteroscedastic process, Markov chain, risk measure, volatility i INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/33940 Markov Swithcing Autoregressive Conditional Heteroscedastic (MSARCH) model provides a description of return uctuation for low and high volatilities. Return behavior with volatility changes is interesting topic, in particular for Value-at-Risk (VaR) prediction. In this thesis, we employ a Markov chain to compute the transition probability of volatility changes. Then, a volatility model MSARCH of order (p,1) is used to predict risk measure. Simulation results show that MSARCH(1,1) dominates in calculating VaR prediction. 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
topic Matematika
spellingShingle Matematika
Nugroho, Setyo
VOLATILITY CHANGES THROUGH MARKOV SWITCHING ARCH MODEL FOR VALUE-AT-RISK PREDICTION
description Markov Swithcing Autoregressive Conditional Heteroscedastic (MSARCH) model provides a description of return uctuation for low and high volatilities. Return behavior with volatility changes is interesting topic, in particular for Value-at-Risk (VaR) prediction. In this thesis, we employ a Markov chain to compute the transition probability of volatility changes. Then, a volatility model MSARCH of order (p,1) is used to predict risk measure. Simulation results show that MSARCH(1,1) dominates in calculating VaR prediction.
format Theses
author Nugroho, Setyo
author_facet Nugroho, Setyo
author_sort Nugroho, Setyo
title VOLATILITY CHANGES THROUGH MARKOV SWITCHING ARCH MODEL FOR VALUE-AT-RISK PREDICTION
title_short VOLATILITY CHANGES THROUGH MARKOV SWITCHING ARCH MODEL FOR VALUE-AT-RISK PREDICTION
title_full VOLATILITY CHANGES THROUGH MARKOV SWITCHING ARCH MODEL FOR VALUE-AT-RISK PREDICTION
title_fullStr VOLATILITY CHANGES THROUGH MARKOV SWITCHING ARCH MODEL FOR VALUE-AT-RISK PREDICTION
title_full_unstemmed VOLATILITY CHANGES THROUGH MARKOV SWITCHING ARCH MODEL FOR VALUE-AT-RISK PREDICTION
title_sort volatility changes through markov switching arch model for value-at-risk prediction
url https://digilib.itb.ac.id/gdl/view/33940
_version_ 1821996634079232000