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題名:利用小波轉換分析方法探討股票市場之相關議題
作者:周靖秦
作者(外文):Chou, Ching-Chin
校院名稱:國立臺北大學
系所名稱:經濟學系
指導教授:陳秀淋 博士
陳淑華 博士
學位類別:博士
出版日期:2012
主題關鍵詞:股票市場小波轉換Stock MarketWavelet Transform
原始連結:連回原系統網址new window
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本篇論文利用小波轉換分析方法以探討股票市場之相關議題。首先,第二章為文獻回顧,主要介紹小波轉換方法在經濟議題應用上的探討;接著,第三章則是小波方法論的介紹。本文的第四章以傳統財務理論的股價現值模型(Present Value Model)為基礎,並利用小波轉換方法分析,探討未預料到的美國總體指標與美國道瓊工業指數之關係。我們發現,與股票報酬波動有顯著相關的指標會隨著時間—頻率的不同(短期、中期、長期)而有所改變,而未預料到的非農就業薪水與個人所得這兩項總體指標與股價波動則有持續且顯著的相關性。最後,根據實證的結果選取相關的未預料到總體指標與股價報酬進行驗證,藉此作為實證結果的可信度再驗證。本文的第五章則以Blanchard and Watson (1983)架構為基礎,加入股價指數並結合小波轉換分析方法,探討房價與股價兩市場在不同時頻之下的因果關係。有別於過去文獻分析結果,房價與股價兩市場不是單純分離或整合的關係。我們的結果發現,房價與股價兩個市場的因果關係是會隨著觀察尺度的不同而有所改變的。在短期,兩市場是分離的;在中期與長期,兩市場是整合的。第六章則是結合依時間轉換機率馬可夫狀態轉換模型(MS-TVTP)與小波轉換方法,檢視在不同時頻之下,美國總體的好壞消息對不同狀態美股報酬是否會有不對稱的影響。與過去文獻有所不同的是,除了從時頻的角度去探討外,我們同時將兩種狀態之間的轉換機率依好壞消息而改變,如此更能捕捉到未預料到的總體消息對股價報酬行為的影響。研究結果發現,好消息對股價報酬結構性轉變有持續且顯著的影響,而壞消息只會在某一特定期間(中期)有顯著影響。因此,好壞消息對股票在不同時頻之下的影響性是不對稱的。
The goal of this dissertation is to analyze and investigate the issues of stock market by utilizing wavelet-based techniques. In Chapter 2, we review literatures which apply wavelet transform to economic analysis. In Chapter 3, we introduce the methodology based on wavelet transform. In Chapter 4, we use present value model and employ wavelet transform to investigate the correlation coefficients of volatility components between each sub-band of stock prices and unexpected macroeconomic news indices and then discuss the connection between the two variables. The results indicate that significant indices would be different from sub-band to sub-band. In Chapter 5, we use Blanchard and Watson (1983) model and wavelet-based technique to analyze time series. Then we test the causality relationship between stock price index and REITs index of each sub-band under the concept of multi-resolution representation. The results reveal that the relationship between stock and real estates markets is neither simply segmented nor purely integrated; the behaviors would vary not only over various observation time scales but also with different REITs index. In Chapter 6, we model stock returns and discuss regime changes by introducing stock returns’ wavelet coefficients into the Markov switching regime model with time-varying transition probabilities (TVTP). The results demonstrates that good news and bad news would affect the regime changes in Dow Jones stock return asymmetrically.
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