動態變幅波動性與相關性之馬可夫轉換模型的建立以及其在風險值上的實證應用

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研究生：	蘇義凱 Yi-Kai Su
論文名稱：	動態變幅波動性與相關性之馬可夫轉換模型的建立以及其在風險值上的實證應用 Establishing the Dynamical Range Volatility and Correlation Models with Markov-switching Structure and its Empirical Application in VaR
指導教授：	繆維中 Daniel Wei-Chung Miao
口試委員:	劉代洋 Day-Yang Liu 黃瑞卿 Rachel Juiching Huang 巫春洲 Chun-Chou Wu 莊文議 Wen-I Chuang 周恆志 Heng-Chih Chou 涂登才 Teng-Tsai Tu
學位類別：	博士 Doctor
系所名稱：	管理學院 - 財務金融研究所 Graduate Institute of Finance
論文出版年：	2013
畢業學年度：	101
語文別：	英文
論文頁數：	83
中文關鍵詞：	馬可夫轉換、變幅波動性模型、變幅DCC模型、波動性、相關性、狀態轉換、波動性調整後的歷史風險值估計
外文關鍵詞：	Markov-switching method, Range-based volatility model, Range-based DCC model, Volatility, Correlation, Regime-switching, Volatility-adjusted historical VaR estimation
相關次數：	點閱：375 下載：10
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本文的主要目的在於將馬可夫轉換方法引入變幅波動性和變幅相關性模型中以及該模型的實證分析。考慮非線性的結構調整來處理那些隱藏在波動性與相關性變數資料產生過程的結構轉變現象是必要的。而且，使用馬可夫轉換方法的優點包含估計狀態轉換行程不會使用到額外的觀察訊息。除了建構的模型外，本文包含兩個實證研究。第一部分使用兩狀態的馬可夫轉換變幅波動性和相關性模型來檢視美國股票和公債資料的波動性和相關性是否存在潛在的狀態轉換現象。在此我們不僅證實在波動性和相關性行程中確實存在狀態轉變的現象，也進一步說明這些狀態轉變的現象是由財務危機事件所引發的。此外，我們可以藉由所建構的模型來標示波動性和相關性變數資料產生過程的結構轉變時間點。第二部分我們使用美國和英國的股價指數資料來估計馬可夫轉換變幅波動性模型。估計的結果呈現出我們所建構的波動性模型確實可以刻劃出波動性行程中未預期的結構轉變現象。即便如此，我們想進一步確認該模型的估計優勢對其預測能力的改善程度。因此，我們將波動性的估計值應用在估算波動性調整後的歷史風險值上。根據應用的結果顯示在估計波動性調整後歷史風險值的議題中，我們所建構的馬可夫轉換變幅波動性模型表現優於其他的波動性模型其中包括CARR、GARCH和馬可夫轉換GARCH模型。

The main purpose of this dissertation is to introduce the Markov-switching method into the range-based volatility and correlation models, and use these two proposed models to empirical analysis. It is necessary to consider the nonlinear adjustment approach to address the structural change embedded in the data generating process for volatility and correlation variables. Furthermore, the advantage of using Markov-switching approach is that there is no feedback from the observed information to the switching-process. There are two empirical studies in this article except the proposed models. In the first part, we examines latent shifts in the conditional volatility and correlation for the U.S. stock and T-bond data using the two-state Markov-switching range-based volatility and correlation models. We not only find evidence of regime changes in both volatility and correlation processes but demonstrate that the phenomena of regime changes are triggered by several financial crises. In addition, we also present the true date of structure changes in the data generating process for volatility and correlation variables by our proposed models. In the second part, we collect the U.S. and the U.K. stock indices to estimate the flexible Markov-switching range-based volatility model. The estimation results show that our proposed model could indeed characterize unexpected switching in the volatility process. However, we would like to further examine the effects of the advantages of the estimate on its forecasting ability. Therefore, we apply the statistic volatility to evaluate the volatility-adjusted historical VaR estimates. According to the application results, we illustrate that the Markov-switching range-based volatility model outperforms the other alternatives including CARR, GARCH and Markov-switching GARCH models on the estimation of volatility-adjusted historical VaR.

Abstract                                                                  i
Acknowledgements                                                        iii
List of Figures                                                          vi
List of Tables                                                          vii
Introduction	                                           1
Literature Review and Methodology	                      8
1  Introduction	                                                     8
2  Range-based Models	                                          10
3  Markov-switching Method	                                          11
4  Markov-switching Range-based Volatility Model	                     13
5  Markov-switching Range-based Dynamic Conditional Correlation Model	14
Regime-switching in Volatility and Correlation Structure Using Range-based Models with Markov-switching	                                19
1  Motivation	                                                    19
2  Introduction	                                                    22
3  Empirical Analysis of Regime-switching in Volatility Structure	25
4  Empirical Analysis of Regime-switching in Correlation Structure	34
5  Concluding Remarks	                                          41
A Markov-switching Range-based Volatility Model with Applications in Volatility-adjusted VaR Estimation	                                43
1  Introduction	                                                     43
2  Monte Carlo Simulation with MS-CARR Model	                      46
3  Empirical Analysis	                                          49
4  Forecasting Comparison	                                          61
4.1 The estimation of the volatility-adjusted historical VaR	           61
4.2 Assessment Criteria for the VaR estimation	                     62
4.3 Forecasting Results	                                          65
5  Concluding Remarks	                                          69
Conclusions	                                          71
1  Summaries of the Two Essays	                                71
2  Potential Future Work	                                          72
Appendix	                                                    74
Appendix A: Construction of log-likelihood function for the Markov-switching range-based volatility model	                                          74
Appendix B: The estimation procedure for the Markov-switching range-based DCC model	                                                               75
Bibliography	                                                     77
國立臺灣科技大學博碩士論文授權書	                                83

                                

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