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研究生: 楊舒媛
Shu-Yuang Yang
論文名稱: Modelling the VIX index and hedging the S&P 500 futures using VIX opions
指導教授: 鄧惠文
Huei-Wen Teng
口試委員:
學位類別: 碩士
Master
系所名稱: 理學院 - 統計研究所
Graduate Institute of Statistics
論文出版年: 2015
畢業學年度: 103
語文別: 英文
論文頁數: 55
中文關鍵詞: VIXS&P 500隱含波動度GARCH避險選擇權
外文關鍵詞: VIX, S&P 500, implied volatility, GARCH, hedge, option
相關次數: 點閱:15下載:0
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  • VIX 是美國 S&P 500 指數的隱含波動度測度的指數,由美國芝加哥證券交易所發行,
    該指數量測未來 30 天的市場波動。此篇論文針對 VIX 指數歷史資料的變異數波動現象,
    比較數個 GARCH 型模型的配適結果。另外,利用 VIX 和 S&P 500 指數之間的負相關,
    比較 VIX 選擇權和 S&P 500 選擇權規避 S&P 500 期貨的下行風險成果。


    VIX is a popular measure of the implied volatility of Standard and Poor 500 (S&P 500)
    index options, it is a trademarked ticker symbol for the Chicago Board Options Exchange
    Market Volatility Index, and it represents one measure of the market's expectation of
    stock market volatility over the next 30 day period. This thesis investigates the volatility
    clustering phenomenon and compares the tting performance of several GARCH-typed
    models. In addition, because there is a negative relationship between VIX index and S&P
    500 index, hedging performances for S&P 500 index futures using VIX options and S&P
    500 options are also compared. It is interesting to nd that, to hedge the downward risk
    of S&P 500 index future using VIX call options outperforms than using S&P 500 option.

    摘要 i Abstract ii 致謝 iii List of Figures vi List of Tables viii 1 Introduction 1 2 Modelling the VIX 5 2.1 Explorotary data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Model considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.1 GARCH (1,1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.2 EGARCH(1,1) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.3 GARCH(1,1)-Jump . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3 Parameter estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.3.1 GARCH(1,1) with Gaussian innovations . . . . . . . . . . . . . . . 13 iv 2.3.2 GARCH(1,1)-Jump with Gaussian innovations . . . . . . . . . . . . 13 2.4 Estimation results and residual tests . . . . . . . . . . . . . . . . . . . . . 19 3 Hedging 25 3.1 Motivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2 Explorotary data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.3 Hedging procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.4 Execution and hedging results . . . . . . . . . . . . . . . . . . . . . . . . . 35 4 Conclusion 42 Reference 44

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