| 摘要: | 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Putniņš’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。
;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 Putniņš’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. |
