Three essays on non-parametric pseudo random disturbance simulations and Monte Carlo approach

博士 === 國立中央大學 === 企業管理研究所 === 99 === This paper utilized a proposed historical simulation, where the effects of a GARCH (1,1) model on an asset’s price path were considered. The Monte Carlo approach was also used to examine the difference in option payoff values between the simulation approaches and...

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Main Authors: Shu-Shian Lin, 林書賢
Other Authors: Keng-Hsin Lo
Format: Others
Language:en_US
Published: 2010
Online Access:http://ndltd.ncl.edu.tw/handle/62954273244762657897
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spelling ndltd-TW-099NCU051210012015-10-30T04:10:15Z http://ndltd.ncl.edu.tw/handle/62954273244762657897 Three essays on non-parametric pseudo random disturbance simulations and Monte Carlo approach Three essays on non-parametric pseudo random disturbance simulations and Monte Carlo approach Shu-Shian Lin 林書賢 博士 國立中央大學 企業管理研究所 99 This paper utilized a proposed historical simulation, where the effects of a GARCH (1,1) model on an asset’s price path were considered. The Monte Carlo approach was also used to examine the difference in option payoff values between the simulation approaches and the original path. Furthermore, this paper used the root mean squared pricing error (RMSE) to show which simulation model would have a smaller RMSE by examining the RMSE difference between the approaches. This paper applied the approaches to simulate option payoff values on three security indexes series in China from January 4, 2000 to December 31, 2009, using the common back-testing approach. The results showed that the estimated option values were significantly different from the actual option payoff values for the observed period. Finally, it was found that the RMSE of the adjusted historical simulation was less than that of the other two simulation approaches. Keng-Hsin Lo 羅庚辛 2010 學位論文 ; thesis 77 en_US
collection NDLTD
language en_US
format Others
sources NDLTD
description 博士 === 國立中央大學 === 企業管理研究所 === 99 === This paper utilized a proposed historical simulation, where the effects of a GARCH (1,1) model on an asset’s price path were considered. The Monte Carlo approach was also used to examine the difference in option payoff values between the simulation approaches and the original path. Furthermore, this paper used the root mean squared pricing error (RMSE) to show which simulation model would have a smaller RMSE by examining the RMSE difference between the approaches. This paper applied the approaches to simulate option payoff values on three security indexes series in China from January 4, 2000 to December 31, 2009, using the common back-testing approach. The results showed that the estimated option values were significantly different from the actual option payoff values for the observed period. Finally, it was found that the RMSE of the adjusted historical simulation was less than that of the other two simulation approaches.
author2 Keng-Hsin Lo
author_facet Keng-Hsin Lo
Shu-Shian Lin
林書賢
author Shu-Shian Lin
林書賢
spellingShingle Shu-Shian Lin
林書賢
Three essays on non-parametric pseudo random disturbance simulations and Monte Carlo approach
author_sort Shu-Shian Lin
title Three essays on non-parametric pseudo random disturbance simulations and Monte Carlo approach
title_short Three essays on non-parametric pseudo random disturbance simulations and Monte Carlo approach
title_full Three essays on non-parametric pseudo random disturbance simulations and Monte Carlo approach
title_fullStr Three essays on non-parametric pseudo random disturbance simulations and Monte Carlo approach
title_full_unstemmed Three essays on non-parametric pseudo random disturbance simulations and Monte Carlo approach
title_sort three essays on non-parametric pseudo random disturbance simulations and monte carlo approach
publishDate 2010
url http://ndltd.ncl.edu.tw/handle/62954273244762657897
work_keys_str_mv AT shushianlin threeessaysonnonparametricpseudorandomdisturbancesimulationsandmontecarloapproach
AT línshūxián threeessaysonnonparametricpseudorandomdisturbancesimulationsandmontecarloapproach
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