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研究生： 王立民
Wang, Li-Min
論文名稱： 價格全距的資訊內涵
The Information Content of Price Range
指導教授： 江明憲
Chiang, Min-Hsien
學位類別： 博士
Doctor
系所名稱： 管理學院 - 國際企業研究所
Institute of International Business
論文出版年： 2011
畢業學年度： 99
語文別： 英文
論文頁數： 73
中文關鍵詞： 偏誤調整 、(C)DCC-RGARCH 、關聯性結構 、價格全距 、Range-augmented GARCH 、RGARCH 、波動性預測
外文關鍵詞： Bias correction, (C)DCC-RGARCH, Copula, High-low range, Range-augmented GARCH, RGARCH, Volatility forecasting
相關次數： 點閱：201  下載：2 
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    • 本篇論文的動機來自於range-based volatility 模型在波動性的估計及預測上具有準確的結果。然而，不同於過去的文獻，我將重點放在利用價格全距所提供的資訊來強化單變量與多變量return-based GARCH model的估計準確度，而非直接是由價格全距的分配著手。這篇論文涵蓋兩個主題。

    在第一章中，我提出一個共同利用報酬率與價格全距兩者的資訊來估計波動性的方法，稱為range-augmented GARCH (RGARCH) 模型。其中所提出的兩種方法其估計的結果均優於傳統的GARCH，GJR-GARCH，與EGARCH模型，且其估計的準確性與range-based模型中的REGARCH模型相當。此外，本方法也可以估計出對於價格全距的偏誤調整量。

    在第二章中，報酬率共變異數的估計更進一步加入了價格全距的資訊。本文提出的模型稱為copula-based dynamic conditional correlation range-augmented GARCH (CDCC-RGARCH) 模型。此模型可以同時討論單變量序列波動性的估計準確性與誤差項分配對於共變異數估計的影響。實證的結果顯示價格全距的資訊會增加報酬率共變異數估計的準確性。

    The work presented in this dissertation was motivated by the good performance of the range-based volatility estimators in volatility estimation and forecasting. Instead of directly modeling the price range distribution, I investigate how to improve volatility estimation and forecasting through incorporating the information content of price range to the return-based univariate and multivariate GARCH models. This dissertation is written in two Chapters.
    In Chapter One, volatility estimators incorporating the information provided by price return and high-low range are proposed. Two specifications of the conditional variance of the proposed range-augmented GARCH (RGARCH) class models are demonstrated to have better volatility estimation than three conventional GARCH-type models, the return-based GARCH, EGARCH, and GJR-GARCH models as well as have equivalent performance compared to the range-based REGARCH model. In addition, a dynamic bias correction component for the biased volatility estimator of high-low range also can be extracted through the proposed approach.
    In Chapter Two, the information content of the price range is further investigated with regard to the return covariance estimation. A copula-based dynamic conditional correlation range-augmented GARCH (CDCC-RGARCH) model is proposed to investigate the accuracy of covariance estimation with respect to the volatility estimator and error distribution. The empirical results show that price range is crucial to multivariate volatility modeling with regard to estimation accuracy.

    摘要 I ABSTRACT II 誌謝 III TABLE OF CONTENTS IV LIST OF TABLES VI LIST OF FIGURES VII Chapter 1. Volatility Forecasting Based on Price Return and High-low Range 1 1. Introduction 2 2. The range-augmented GARCH model 6 2.1. The role of high-low range 6 2.2. The relation between price return and high-low range 8 2.3. Specifications for the conditional variance 9 3. Measuring estimation and forecasting performance 13 4. Empirical study 15 4.1. Preliminary analysis 16 4.2. Parameter estimation and in-sample performance 17 4.3. The performance of volatility forecasting 18 5. Conclusions 20 Chapter 2. Covariance Estimation with Price Range Information 29 1. Introduction 30 2. The model 34 2.1. The range-augmented GARCH model 34 2.2. The D-vine copula 36 2.3. The copula-based DCC-RGARCH model 38 3. Parameter estimation and model comparisons 41 3.1. Data 41 3.2. Preliminary analysis 41 3.3. Parameter estimates of the CDCC-RGARCH models 42 3.4. The performance of covariance estimation 44 4. Evaluating the estimated covariance by the pricing error 45 4.1. Measuring pricing error 47 4.2. Empirical evidence 48 5. Conclusions 48 Appendix A 50 Appendix B 51 References 53

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