Value-at-Risk Models Applied to Taiwan's Stock Market

In this paper, the parametric normal method, the historical simulation method and the Monte Carlo simulation method are applied to Taiwan's stock marekt estimating the one-day 95% and 99% VaRs for the electronic and banking & insurance sector indices. Then, the basic frequency backtesting a...

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Bibliographic Details
Main Author: Lin, Ching-Li
Format: Dissertation (University of Nottingham only)
Language:English
Published: 2008
Subjects:
VAR
Online Access:https://eprints.nottingham.ac.uk/22336/
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author Lin, Ching-Li
author_facet Lin, Ching-Li
author_sort Lin, Ching-Li
building Nottingham Research Data Repository
collection Online Access
description In this paper, the parametric normal method, the historical simulation method and the Monte Carlo simulation method are applied to Taiwan's stock marekt estimating the one-day 95% and 99% VaRs for the electronic and banking & insurance sector indices. Then, the basic frequency backtesting and the conditional coverage testing are used to examine the performances of these VaR models. We find that all the approaches in question generate the accurate 95th percentile risk measures for both sector indices, while only the 250-day and 1000-day historical simulation methods produce the reasonable 99th percentile risk measures for both sector indices.
first_indexed 2025-11-14T18:43:40Z
format Dissertation (University of Nottingham only)
id nottingham-22336
institution University of Nottingham Malaysia Campus
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language English
last_indexed 2025-11-14T18:43:40Z
publishDate 2008
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spelling nottingham-223362018-02-20T11:28:32Z https://eprints.nottingham.ac.uk/22336/ Value-at-Risk Models Applied to Taiwan's Stock Market Lin, Ching-Li In this paper, the parametric normal method, the historical simulation method and the Monte Carlo simulation method are applied to Taiwan's stock marekt estimating the one-day 95% and 99% VaRs for the electronic and banking & insurance sector indices. Then, the basic frequency backtesting and the conditional coverage testing are used to examine the performances of these VaR models. We find that all the approaches in question generate the accurate 95th percentile risk measures for both sector indices, while only the 250-day and 1000-day historical simulation methods produce the reasonable 99th percentile risk measures for both sector indices. 2008 Dissertation (University of Nottingham only) NonPeerReviewed application/pdf en https://eprints.nottingham.ac.uk/22336/1/08MAlixcll.pdf Lin, Ching-Li (2008) Value-at-Risk Models Applied to Taiwan's Stock Market. [Dissertation (University of Nottingham only)] (Unpublished) VAR
spellingShingle VAR
Lin, Ching-Li
Value-at-Risk Models Applied to Taiwan's Stock Market
title Value-at-Risk Models Applied to Taiwan's Stock Market
title_full Value-at-Risk Models Applied to Taiwan's Stock Market
title_fullStr Value-at-Risk Models Applied to Taiwan's Stock Market
title_full_unstemmed Value-at-Risk Models Applied to Taiwan's Stock Market
title_short Value-at-Risk Models Applied to Taiwan's Stock Market
title_sort value-at-risk models applied to taiwan's stock market
topic VAR
url https://eprints.nottingham.ac.uk/22336/