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研究生: 詹知諭
Chan, Chih-Yu
論文名稱: 隱含波動度曲面校準與日內對沖策略研究:以台指選擇權為例
Calibration of Implied Volatility Surface and Research on Intraday Trading Strategy: Evidence from Taiwan Market
指導教授: 林士貴
Lin, Shih-Kuei
口試委員: 許順吉
Sheu, Shuenn-Jyi
姜祖恕
Chiang, Tzuu-Shuh
江彌修
Chiang, Mi-Hsiu
林士貴
Lin, Shih-Kuei
陳亭甫
Chen, Ting-Fu
學位類別: 碩士
Master
系所名稱: 商學院 - 金融學系
Department of Money and Banking
論文出版年: 2022
畢業學年度: 110
語文別: 英文
論文頁數: 27
中文關鍵詞: 日內隱含波動度曲面模型校準波動度預測對沖策略高頻資料
外文關鍵詞: Intraday implied volatility surface, Model calibration, Volatility prediction, Long-short strategy, High-frequency data
DOI URL: http://doi.org/10.6814/NCCU202200996
相關次數: 點閱:65下載:0
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  • 隱含波動度曲面對於衍生性商品的定價、造市、交易甚或是風險管理都扮演著至關重要的角色,過去有許多研究提出各式各樣的模型來描繪隱含波動度曲面的笑狀波幅與期間結構。本研究基於過去所提出的相關模型,嘗試以台指選擇權日內高頻資料做模型校準,並進而建構預測之日內隱含波動度曲面。實證結果表明,在給定日內部分資訊下,外生給定價平隱含波動度的模型相較於其他的模型在樣本外更加適應於市場的動態,並且我們的結果顯示日內隱含波動度是可預測的。最後,比較市場實際隱含波動度與我們所校準出的預測曲面,我們透過買入/賣出最被市場低估/高估的契約,建構一個delta中立的對沖策略。回測結果顯示,不考慮交易成本下,策略可獲利,佐證模型具有一定預測能力。但在考慮交易成本後,由於賺取的vega不足以支付交易成本,以致本波動度套利策略於實務上並不可行。


    Implied volatility surface is very important for derivatives pricing, market making, trading or even risk management. Various models are proposed by previous studies to portray skew and term structure observed in implied volatility surfaces. In this study, we try to calibrate these models by using TAIEX options’ quotes at 1-min frequency filtered from trade book and construct predictive intraday implied volatility surfaces. Our results show that models referring to at-the-money volatility are more adaptive to the market dynamics, and it is possible for us to predict intraday volatility.
    Moreover, by building a long-short delta-neutral strategy, we found that it is profitable assuming no transaction costs, which shows that our model is predictive. But after considering transaction costs, this strategy can’t work in practice since earned vega is not enough to pay transaction costs.

    摘要 i
    Abstract ii
    Contents iii
    List of Figures v
    1 Introduction 1
    2 Literature review 4
    2.1 Stochastic volatility models 4
    2.2 Models including jump-diffusion 5
    2.3 Modeling dynamics of implied volatility 5
    2.4 Parametric representations 6
    3 Methodology 7
    3.1 Implied volatility calculation 7
    3.2 SABR model 8
    3.3 VGVV model 10
    3.4 SSVI model 12
    3.5 Optimization algorithm 14
    3.6 Evaluation of model performance 15
    4 Empirical results 17
    4.1 Data description 17
    4.2 Model comparison 17
    4.3 Intraday dynamic calibration 19
    4.4 Construction of long-short strategy 22
    5 Conclusion and future works 24
    5.1 Conclusion 24
    5.2 Future works 25
    References 26

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