博碩士論文 994408005 詳細資訊




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姓名 葉俞昀(Yu-Yun Yeh)  查詢紙本館藏   畢業系所 財務金融學系
論文名稱 VIX金融衍生性市場的價格發現和跳躍行為之研究
(Price Discovery and Jump Behaviors on VIX Derivatives Markets)
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摘要(中) 本文是由兩篇關於VIX金融衍生性市場的價格發現和跳躍行為的研究所構成。第一篇研究主要探討S&P 500 index、S&P 500 index選擇權和VIX 選擇權市場之間的價格發現關係;第二篇研究提出了一般化的多因子選擇權定價模型,此模型包含了多重波動度、跳躍、共同跳躍和槓桿等因子。

第一篇研究:
此研究主要探討S&P 500指數、S&P 500指數選擇權和VIX 選擇權市場之間的價格發現關係。本研究採用Hascrouck (1995)和Yan and Zivot’s (2010)及Putni??’s (2013)提出的方法來對價格發現能力做計算。接下來,我們使用時間序列迴歸模型來更進一步探討價格發現和市場特徵之間關係。在價格發現能力的分析上,我們將資料拆分成三個部分:全資料期間、金融風暴期間、排除金融風暴期間。除此之外,本研究也提供新的視角來探討買權和賣權的資訊含量,以及價內外程度對價格發現的影響。

第二篇研究:
此研究提出了一般化的多因子選擇權定價模型,此模型包含了多重波動度、跳躍、共同跳躍和槓桿等因子。本研究使用高頻的S&P 500指數選擇權和VIX選擇權來建立共同跳躍因子,進而使用此模型來探討選擇權定價的結果。
摘要(英) This study contains two essays on the price discovery and jump behaviors on VIX derivatives markets. The first essay investigates on price discovery across the S&P 500 index, S&P 500 index options, and VIX options markets; whereas the second essay provides a general form of multi-components option pricing model which includes multiple volatility, jump, co-jump, and leverage components, namely, Heterogeneous AutoRegerssive Gamma model for Realized Volatility with Leverage, Jumps, and Co-jumps (CoJJLHARG).

First Essay:
This paper investigates on price discovery across the S&P 500 index (SPX), SPX options, and VIX options markets by applying Hasbrouck’s (1995) information share and Yan and Zivot’s (2010) and Putni??’s (2013) information leadership share methods. We estimated a time series regression model to integrate the price discovery into market characteristics. We also separated the data into two subsamples - one in the presence of crisis and the second in its absence - and examined the relationship between price discovery and market characteristics. In addition, this study provides a new angle to analyze whether the information is identical in the call and put options by market characteristics. Finally, this study contributes to literature since it indicates how informed traders in the option market are distributed across strike prices.

Second Essay:
This paper provides a general form of multi-components option pricing model which includes multiple volatility, jump, co-jump, and leverage components, namely, Heterogeneous AutoRegerssive Gamma model for Realized Volatility with Leverage, Jumps, and Co-jumps (CoJJLHARG). The model employs the high-frequency SPX and VIX data to filter the co-jump component. Moreover, we use this model to analyze the options pricing’s performance.
關鍵字(中) ★ 價格發現
★ S&P 500指數
★ S&P 500指數選擇權
★ VIX選擇權
★ 避險
★ 選擇權定價
★ 共同跳躍模型
關鍵字(英) ★ price discovery
★ S&P 500 index
★ S&P 500 index options
★ VIX options
★ information share
★ information leadership share
★ hedging
★ option pricing
★ co-jump model
論文目次 Content
摘要 I
Abstract II
Content IV
List of Figure VI
List of Table VII
Essay 1: Price Discovery across the S&P 500 Index, S&P 500 Options, and VIX Options 8
1. Introduction 8
2. Methodology 13
2.1. Option-Implied Underlying Security Prices 13
2.2. Measures for the Lead–lag relationships 14
2.3. Hypotheses 17
3. Empirical Results 21
3.1. Data Description and Statistics for the SPX and VIX Options 21
3.2. Price Discovery Shares in SPX, SPX Options, and VIX Options Markets 26
3.3. Relationship between Price Discovery Shares and Market Characteristics 28
4. Price Discovery Shares and Strike Price 36
5. Robustness 40
6. Conclusion 41
References 45
Essay 2: Option Pricing with Multiple Volatility, Jump, Co-Jump, and Leverage Components 50
1. Introduction 50
2. The CoJJLHARG Model 55
2.1. Log-Return Dynamics 55
2.2. Model Setting 56
2.3. The Family of RV Option Pricing Models 58
2.4. Risk-neutral dynamics for the CoJJLHARG model 60
3. Data Description 63
4. Model Estimation 67
5. Option Pricing: Performance Assessment 72
5.1. Option Pricing Method 72
5.2. Option Pricing Results 73
6. Conclusion 79
References 81
Appendix A. Proofs 85
A.1. Proof of Proposition 1 85
A.2. Proof of Proposition 2 86
A.3. Proof of Proposition 3 88
Appendix B. Numerical Values of RMSEIV 91
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指導教授 張傳章 楊曉文(Chuang-Chang Chang Sharon S. Yang) 審核日期 2018-7-25
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