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題名：馬可夫轉換模型應用性與合用性探討
作者：黎明淵
作者(外文)：Ming-Yuan Li
校院名稱：國立政治大學
系所名稱：國際貿易學系
指導教授：饒秀華
林修葳
學位類別：博士
出版日期：2000
主題關鍵詞：馬可夫轉換模型；混合常態分配模型；GARCH模型；風險值；景氣循環；東南亞金融風暴；Markov-switching Models；Mixing-normal Models；Garch Models；Value at Risk；Business Cycle；Asian Fincial Crisis
原始連結：連回原系統網址new window
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Hamilton (1989)發展出馬可夫轉換模型(Markov-switching Model)，由於該模型允許母體參數在不同時期，具有間斷性跳動性質，且跳動次數並不限定為一，並利用馬可夫鏈(Markov chain)的機制來掌控狀態間切換，解決混合分配模型狀態跳動毫無規則的問題，將可適可掌握金融與經濟變數所面臨的結構改變，以及解決在計測風險值(valued at risk)過程中，所存在報酬分配的高峰厚尾問題。
本文非僅是嘗試另一種方法，而是我們在探討股市報酬波動與景氣循環變數行為後，推判它較能夠捕捉實際的報酬波動與景氣循環行為。我們除作過去文獻較未顧及的，系統性地分析各種潛在風險值計測方法所適用與不適用報酬率變異情境，並嘗試使用允許參數來自不同波動狀態，對傳統ARCH模型加以修正之SWARCH模型，希對股市報酬波動提供更佳的分析。在景氣循環探討，針對馬可夫轉換模型加以修正，掌握台灣與南韓經濟結構與美國及日本等國迥異的問題。此外，我們也回溯、討論各種處理財經變數結構問題之實證模型差異，分析馬可夫轉換模型相對優、劣點。
Abstract
This paper serves as one of the first studies that adopts a Markov-switching (MS) model to estimate the value of risk (VaR). Specifically, we use a two-regime MS specification, a MS setting with two sets of regime mean and regime variance, on Taiwan Stock Exchange (hereafter TAIEX) market returns as well as major industrial group stock index returns. There is a long tradition of using linear models in measuring VaR. Nevertheless, possibly due to structural changes during the estimation period, many prior studies document significant tail-fatness, Lepto kurtosis, and skewness in returns. Interestingly, our empirical results lend supports to the notion that TAIEX, TAIEX-Construction, TAIEX-Finance, TAIEX-Electronic, and TAIEX-Electric&Machinery returns are significantly skewed with fat tails. Also consistently, we demonstrate that the more generalized MS models effectively correct kurtosis and skewness problems and outshine both GARCH and the mixing normal models, with the former (latter) alternative being subject to over-estimating (under-estimating) the persistence of security index return volatility (hereafter volatility). As for estimating the 5% VaR, MS appears to be equally effective as the competing Bayesian mixing normal and GARCH models. In contrast, MS significantly outperforms the two non-linear alternatives for estimating VaR with 1% or 2.5% tail probabilities. Furthermore, as for the window of learning period on rare events, we find that one need to go much farther back to effectively depict the left as opposed to the right tail.
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