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Title: 以Noncausal Cauchy AR(1) with Gaussian Component分析台灣股價指數
Apply noncausal Cauchy AR(1) with Gaussian component to Taiwan Stock Price Index
Authors: 温元駿
Contributors: 徐士勛
Keywords: 台灣股價指數
Noncausal Cauchy AR(1) with Gaussian component
Taiwan stock price index
Bubble effect
Noncausal Cauchy AR(1) with Gaussian component
Date: 2017
Issue Date: 2017-07-24 12:16:28 (UTC+8)
Abstract: 過去實證研究多以時間序列模型搭配 GARCH 模型針對台灣股價指數進行分析。然而,Gourieroux and Zakoian(2017) 提出,當一時間序列具有泡沫現象時,noncausal Cauchy AR(1) process 是可能的優選模型。此外,Sarno and Taylor(1999) 的研究認為,台灣股價指數具有泡沫現象,故我們以 noncausal Cauchy AR(1) with Gaussian component 分析台灣股價指數,進而判斷其泡沫效果係來自 noncausal linear process 之 local explosive,並根據 noncausal Cauchy AR(1) 與 Gaussian component 之係數變動,捕捉泡沫效果之形成與來源。
Most of the previous studies focused on analyzing Taiwan Stock Price Index using time series models with GARCH effects. However, Gourieroux and Zakoian (2017) have demonstrated that noncausal Cauchy AR(1) process may be a possible model in which the bubbles are observed. Besides, according to the studies of Sarno and Taylor (1991), some bubbles exactly existed in Taiwan Stock Price Index before 1990. Accordingly, this study aims at investigating the possible bubbles in Taiwan Stock Price Index from 2005 to 2015 by employing noncausal Cauchy AR(1) with Gaussian component method. As a result, we find out he bubbles which modeled by the noncausal linear process are local explosive. And based on the changes of the coefficients from noncausal Cauchy AR(1) and Gaussian component, this study successfully captures the form of bubbles.
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