Asymmetric Nonstationary Nonlinear Heteroskedasticity Model

碩士 === 國立臺灣大學 === 經濟學研究所 === 92 === We extend the nonstationary nonlinear heteroskedasticity (NNH) model proposed by Park (2002) to construct a new volatility model, namely, the asymmetric NNH (ANNH) model, in which a particular nonlinear transformation of a standardized random walk is used to depic...

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Main Authors: O-Chia Chuang, 莊額嘉
Other Authors: 管中閔
Format: Others
Language:en_US
Published: 2004
Online Access:http://ndltd.ncl.edu.tw/handle/17372213622286306082
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spelling ndltd-TW-092NTU053890332016-06-10T04:15:58Z http://ndltd.ncl.edu.tw/handle/17372213622286306082 Asymmetric Nonstationary Nonlinear Heteroskedasticity Model 非對稱的條件變異數NNH模型 O-Chia Chuang 莊額嘉 碩士 國立臺灣大學 經濟學研究所 92 We extend the nonstationary nonlinear heteroskedasticity (NNH) model proposed by Park (2002) to construct a new volatility model, namely, the asymmetric NNH (ANNH) model, in which a particular nonlinear transformation of a standardized random walk is used to depict the evolution of volatility. Compared with the NNH model, the ANNH model is able to capture asymmetric reactions to good and bad shocks and exhibits long memory in volatility while maintaining finite unconditional variance. In addition, this model also improves on efficiency in parameter estimation. As the NNH model, the ANNH model also exhibits properties of volatility clustering and leptokurtosis, which are usually observed in economical and financial time series data. Simulation results show that if we use time series generated by the ANNH model to fit a GARCH(1,1) model, the sum of parameters alpha_1 and beta_1 will be close to 1, likes the IGARCH(1,1) model, which does not have finite second and fourth moment. 管中閔 2004 學位論文 ; thesis 22 en_US
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language en_US
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sources NDLTD
description 碩士 === 國立臺灣大學 === 經濟學研究所 === 92 === We extend the nonstationary nonlinear heteroskedasticity (NNH) model proposed by Park (2002) to construct a new volatility model, namely, the asymmetric NNH (ANNH) model, in which a particular nonlinear transformation of a standardized random walk is used to depict the evolution of volatility. Compared with the NNH model, the ANNH model is able to capture asymmetric reactions to good and bad shocks and exhibits long memory in volatility while maintaining finite unconditional variance. In addition, this model also improves on efficiency in parameter estimation. As the NNH model, the ANNH model also exhibits properties of volatility clustering and leptokurtosis, which are usually observed in economical and financial time series data. Simulation results show that if we use time series generated by the ANNH model to fit a GARCH(1,1) model, the sum of parameters alpha_1 and beta_1 will be close to 1, likes the IGARCH(1,1) model, which does not have finite second and fourth moment.
author2 管中閔
author_facet 管中閔
O-Chia Chuang
莊額嘉
author O-Chia Chuang
莊額嘉
spellingShingle O-Chia Chuang
莊額嘉
Asymmetric Nonstationary Nonlinear Heteroskedasticity Model
author_sort O-Chia Chuang
title Asymmetric Nonstationary Nonlinear Heteroskedasticity Model
title_short Asymmetric Nonstationary Nonlinear Heteroskedasticity Model
title_full Asymmetric Nonstationary Nonlinear Heteroskedasticity Model
title_fullStr Asymmetric Nonstationary Nonlinear Heteroskedasticity Model
title_full_unstemmed Asymmetric Nonstationary Nonlinear Heteroskedasticity Model
title_sort asymmetric nonstationary nonlinear heteroskedasticity model
publishDate 2004
url http://ndltd.ncl.edu.tw/handle/17372213622286306082
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