1. NCCU Academic Hub
  2. Publications
  3. College of Commerce
  4. Theses
  5. Item

Publications-Theses

Article View/Open

Publication Export

Google ScholarTM

NCCU Library

Citation Infomation

Related Publications in TAIR

題名 選擇權波動度交易策略之探討-以台指選擇權為例
A study of volatility trading strategies: evidence from Taiwan index options
作者 賴星旅
Lai, Hsing Lu
貢獻者 陳威光<br>江彌修
賴星旅
Lai, Hsing Lu
關鍵詞 波動度交易
選擇權交易策率
隱含波動度指數
時間序列模型
市場情緒指標
volatility trading
option trading strategy
VIX
GARCH(1,1)
Market Sentiment Indices
日期 2009
上傳時間 9-May-2016 11:49:16 (UTC+8)
摘要 本文考量波動度不對稱效果(Volatility Asymmetric Effect)與均數回歸(Mean Reverting)兩個特性，並考量台股市場特性，嘗試建立一個適合台灣市場的波動度交易策略。利用GARCH(1,1)波動度與VIX指標建構第一個交易訊號，並建立當日沖銷部位。以賺取日內行情為出發點，利用時間序列模型捕捉波動度的高估或低估且搭配純跨式(Pure Straddle)策略或根據Delta調整後的跨式(Adjusted Straddle)策略。第二個交易訊號則是利用市場敏感指標，觀察外資與自營商在交易部位與未平倉部位的變化，找出對於波動度的影響。建立由選擇權與期貨組成的Delta-Hedged部位，藉由觀察市場上主力籌碼的變化，動態調整部位契約，尋找波段之間的獲利機會。
實証部分以期交所公布的每日交易資料與VIX日資料，利用2007至2008兩年的歷史資料，估計參數與測試交易訊號。樣本外期間為2009年1月開始至3月結束共55個交易日。考量交易成本後，兩個不同型態的交易訊號，仍然能夠藉由本研究的策略，獲得正的報酬。本文認為台灣為一個淺碟市場，過度反應資訊的特性，讓波動度策略出現獲利的機會。藉由這個波動度交易系統的研究，除了讓資金豐沛的機構投資人使用外，也能夠讓一般投資大眾建立自己的波動度交易策略


關鍵字:波動度交易，選擇權交易策略，GARCH(1,1)，VIX，市場情緒指標
Trying to apply a preliminary study of volatility trading strategies in Taiwan derivative market is the topic of this dissertation. Capturing the market movement or even the dynamic of underlying asset is a Pandora’s Box for academic researchers and industry participants. Mean-reverting and asymmetrical effects are the two special characteristics of volatility for us to design our trading system according to the previous empirical studies.
In our study, we use different type of volatility signal to capture the trading opportunities. Use the new released information form TAIFEX including VIX and Position Structure of Institutional Traders to design our signal. We apply the idea to use pure option position and delta-hedged position as our trading tools in this volatility trading system and look for the opportunities between realized volatility and implied volatility. An over-reaction may rises the uncertainty and also lead the market volatility change coherently. We use history data from 2007 to 2008 test our trading signal and parameters. The out sample period is from 2009 January to 2009 March which has 55 trading days to simulate our strategies. In the end, we see a positive result in both trading signals which earns positive return after considering the trading cost.



Key words: Volatility Trading, Market Sentiment Indices, Option Strategies, VIX, GARCH(1,1)
參考文獻 5. References
Alexander, C. ”Market Risk Analysis”, Volume Three, John Wiley & Sons, Inc., 2008
Andersen T. G., Bollerslev T., Christoffersen P.F. and Diebold F.X., “Volatility
Forecasting”, Working Paper, National Bureau of Economic Research, Cambridge, 2005
Anil K. Bera, Matthew L. Higgins, “ ARCH Models: Properties, Estimation and Testing”, Journal of Economic Surveys, 1999
Asger L. and P. R. Hansen., “A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?”, Working Papers 04, Brown University, Department of Economics, 2001
Bartels, H.J and Jian, L.,”Volatility Forecasting and Delta-Neutral Volatility Trading for DTB Options on the DAX”, 1998
Bollerslev, T., “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, 1986
Brown, G.W.,” Volatility, sentiment, and noise traders”, Financial Analysts Journal, pp. 82–90, 1999
Carr, P. and D Madan.,” Towards a theory of volatility trading”. Handbooks in Mathematical Finance:Option Pricing, Interest Rates and Risk Management, eds. J. Cvitanic,E. Jouini and M. Musiela, Cambridge University Press, 458–476, 2001
Carr, P., and Wu, L.,” A Tale of Two Indices. Journal of Derivatives”, 13-29. 2004
Clive W.J. Granger, “The past and future of empirical finance: some personal comments”, Journal of Econometrics, Volume 129, Issues 1-2, 2005
Corrado, C. J. T., and W. Miller,” The forecast quality of CBOE implied volatility indexes”, Journal of Futures Markets 25, 2005
De Long, J.B., Shleifer, A., Summers, L. and Waldmann, R.,” Noise trader risk in financial markets”. Journal of Political Economy 98, pp. 703–738, 1990
Dumas, B., J. Fleming and R. E. Whaley, “Implied Volatility Functions: Empirical Tests,” Journal of Finance, 53, 1998
Engle, R.F., “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation,” Econometrica, 1982
Franses PH, van Dijk D.,” Forecasting stock market volatility using (non-linear) GARCH models”. Journal of Forecasting 15: 229-235, 1996
Giot P., “Relationships between Implied Volatility Indexes and Stock Index Returns. Are implied volatility indexes leading indicators?”, Journal of Portfolio Management, pp. 92-100, 2005.
Lee,W.Y C.X. Jiang and D.C. Indro, “Stock market volatility, excess returns, and the role of investor sentiment”, Journal of Banking and Finance, pp. 2277–2299, 2002
Mark,B.,J., and Anthony,N.,” Option Prices, Implied Price Processes, and Stochastic Volatility”, The Journal of Finance, Vol. 55, No. 2, pp. 839-866, 2000
Poon S.H. and Granger C.W.L., “Forecasting Volatility in Financial Markets: A
Review.”, Journal of Economic Literature, Vol. XLI , pp.478-539, 2003
Sato,A.-H., and H. Takayasu. “Derivation of ARCH(1) Process from Market Price Changes Based on Deterministic Microscopic Multi-Agent”, Empirical Science of Financial Fluctuations, Springer, 2002
Simon D, and Wiggins R,”S&P Futures returns and contrary sentiment indicators”, Journal of Futures Mark, 21:447–462, 2001
Sinclar E., “Volatility Trading”, John Wiley & Sons, Inc., 2008
Solt, M.E. and M. Statman, “How useful is the sentiment index”, Financial Analysts Journal, pp. 45–55, 1988
Whaley, R.E., ”The investor fear gauge”, Journal of Portfolio Management 26, 2000
Yaw-Huei Wang, Y.H. and A. Keswani, Stephen J. Taylor,” The relationships between sentiment, returns and volatility”, International Journal of Forecasting, Volume 22, Pages 109-123, 2005
描述 碩士
國立政治大學
金融研究所
94352014
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0094352014
資料類型 thesis
dc.contributor.advisor 陳威光<br>江彌修zh_TW
dc.contributor.author (Authors) 賴星旅zh_TW
dc.contributor.author (Authors) Lai, Hsing Luen_US
dc.creator (作者) 賴星旅zh_TW
dc.creator (作者) Lai, Hsing Luen_US
dc.date (日期) 2009en_US
dc.date.accessioned 9-May-2016 11:49:16 (UTC+8)-
dc.date.available 9-May-2016 11:49:16 (UTC+8)-
dc.date.issued (上傳時間) 9-May-2016 11:49:16 (UTC+8)-
dc.identifier (Other Identifiers) G0094352014en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/94758-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 金融研究所zh_TW
dc.description (描述) 94352014zh_TW
dc.description.abstract (摘要) 本文考量波動度不對稱效果(Volatility Asymmetric Effect)與均數回歸(Mean Reverting)兩個特性，並考量台股市場特性，嘗試建立一個適合台灣市場的波動度交易策略。利用GARCH(1,1)波動度與VIX指標建構第一個交易訊號，並建立當日沖銷部位。以賺取日內行情為出發點，利用時間序列模型捕捉波動度的高估或低估且搭配純跨式(Pure Straddle)策略或根據Delta調整後的跨式(Adjusted Straddle)策略。第二個交易訊號則是利用市場敏感指標，觀察外資與自營商在交易部位與未平倉部位的變化，找出對於波動度的影響。建立由選擇權與期貨組成的Delta-Hedged部位，藉由觀察市場上主力籌碼的變化，動態調整部位契約，尋找波段之間的獲利機會。
實証部分以期交所公布的每日交易資料與VIX日資料，利用2007至2008兩年的歷史資料，估計參數與測試交易訊號。樣本外期間為2009年1月開始至3月結束共55個交易日。考量交易成本後，兩個不同型態的交易訊號，仍然能夠藉由本研究的策略，獲得正的報酬。本文認為台灣為一個淺碟市場，過度反應資訊的特性，讓波動度策略出現獲利的機會。藉由這個波動度交易系統的研究，除了讓資金豐沛的機構投資人使用外，也能夠讓一般投資大眾建立自己的波動度交易策略


關鍵字:波動度交易，選擇權交易策略，GARCH(1,1)，VIX，市場情緒指標		zh_TW
dc.description.abstract (摘要) Trying to apply a preliminary study of volatility trading strategies in Taiwan derivative market is the topic of this dissertation. Capturing the market movement or even the dynamic of underlying asset is a Pandora’s Box for academic researchers and industry participants. Mean-reverting and asymmetrical effects are the two special characteristics of volatility for us to design our trading system according to the previous empirical studies.
In our study, we use different type of volatility signal to capture the trading opportunities. Use the new released information form TAIFEX including VIX and Position Structure of Institutional Traders to design our signal. We apply the idea to use pure option position and delta-hedged position as our trading tools in this volatility trading system and look for the opportunities between realized volatility and implied volatility. An over-reaction may rises the uncertainty and also lead the market volatility change coherently. We use history data from 2007 to 2008 test our trading signal and parameters. The out sample period is from 2009 January to 2009 March which has 55 trading days to simulate our strategies. In the end, we see a positive result in both trading signals which earns positive return after considering the trading cost.



Key words: Volatility Trading, Market Sentiment Indices, Option Strategies, VIX, GARCH(1,1)		en_US
dc.description.tableofcontents 1. Introduction……………………………………………………………….1
1.1 Motivation
1.2 References Review
1.3 Volatility Trading Types

2. Methodology…………………………………………………………………5
2.1 Trading Signal: GARCH Volatility vs. VIX
2.2 Trading Signal: Market Sentiment Indices vs. Realized Volatility
2.3 Volatility Strategy: Pure Option Position
2.4 Volatility Strategy: Delta-Hedged Position

3. Data and Empirical Result…………………………………………21
3.1 Spread of GARCH(1,1) Volatility and VIX
3.2 Market Sentiment Indices Selection
3.3 Trading Performance Analysis of Pure Option Position
3.4 Trading Performance Analysis of Delta-Hedged Position

4. Conclusion…………………………………………………………………35

5. References……………………………………………………………38

Appendix A: Tables………………………………………………………….1
Appendix B: Figures……………………………………………………….20
Appendix C: Margin and Transaction Cost Calculation……29		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0094352014en_US
dc.subject (關鍵詞) 波動度交易zh_TW
dc.subject (關鍵詞) 選擇權交易策率zh_TW
dc.subject (關鍵詞) 隱含波動度指數zh_TW
dc.subject (關鍵詞) 時間序列模型zh_TW
dc.subject (關鍵詞) 市場情緒指標zh_TW
dc.subject (關鍵詞) volatility tradingen_US
dc.subject (關鍵詞) option trading strategyen_US
dc.subject (關鍵詞) VIXen_US
dc.subject (關鍵詞) GARCH(1,1)en_US
dc.subject (關鍵詞) Market Sentiment Indicesen_US
dc.title (題名) 選擇權波動度交易策略之探討-以台指選擇權為例zh_TW
dc.title (題名) A study of volatility trading strategies: evidence from Taiwan index optionsen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 5. References
Alexander, C. ”Market Risk Analysis”, Volume Three, John Wiley & Sons, Inc., 2008
Andersen T. G., Bollerslev T., Christoffersen P.F. and Diebold F.X., “Volatility
Forecasting”, Working Paper, National Bureau of Economic Research, Cambridge, 2005
Anil K. Bera, Matthew L. Higgins, “ ARCH Models: Properties, Estimation and Testing”, Journal of Economic Surveys, 1999
Asger L. and P. R. Hansen., “A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?”, Working Papers 04, Brown University, Department of Economics, 2001
Bartels, H.J and Jian, L.,”Volatility Forecasting and Delta-Neutral Volatility Trading for DTB Options on the DAX”, 1998
Bollerslev, T., “Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics, 1986
Brown, G.W.,” Volatility, sentiment, and noise traders”, Financial Analysts Journal, pp. 82–90, 1999
Carr, P. and D Madan.,” Towards a theory of volatility trading”. Handbooks in Mathematical Finance:Option Pricing, Interest Rates and Risk Management, eds. J. Cvitanic,E. Jouini and M. Musiela, Cambridge University Press, 458–476, 2001
Carr, P., and Wu, L.,” A Tale of Two Indices. Journal of Derivatives”, 13-29. 2004
Clive W.J. Granger, “The past and future of empirical finance: some personal comments”, Journal of Econometrics, Volume 129, Issues 1-2, 2005
Corrado, C. J. T., and W. Miller,” The forecast quality of CBOE implied volatility indexes”, Journal of Futures Markets 25, 2005
De Long, J.B., Shleifer, A., Summers, L. and Waldmann, R.,” Noise trader risk in financial markets”. Journal of Political Economy 98, pp. 703–738, 1990
Dumas, B., J. Fleming and R. E. Whaley, “Implied Volatility Functions: Empirical Tests,” Journal of Finance, 53, 1998
Engle, R.F., “Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation,” Econometrica, 1982
Franses PH, van Dijk D.,” Forecasting stock market volatility using (non-linear) GARCH models”. Journal of Forecasting 15: 229-235, 1996
Giot P., “Relationships between Implied Volatility Indexes and Stock Index Returns. Are implied volatility indexes leading indicators?”, Journal of Portfolio Management, pp. 92-100, 2005.
Lee,W.Y C.X. Jiang and D.C. Indro, “Stock market volatility, excess returns, and the role of investor sentiment”, Journal of Banking and Finance, pp. 2277–2299, 2002
Mark,B.,J., and Anthony,N.,” Option Prices, Implied Price Processes, and Stochastic Volatility”, The Journal of Finance, Vol. 55, No. 2, pp. 839-866, 2000
Poon S.H. and Granger C.W.L., “Forecasting Volatility in Financial Markets: A
Review.”, Journal of Economic Literature, Vol. XLI , pp.478-539, 2003
Sato,A.-H., and H. Takayasu. “Derivation of ARCH(1) Process from Market Price Changes Based on Deterministic Microscopic Multi-Agent”, Empirical Science of Financial Fluctuations, Springer, 2002
Simon D, and Wiggins R,”S&P Futures returns and contrary sentiment indicators”, Journal of Futures Mark, 21:447–462, 2001
Sinclar E., “Volatility Trading”, John Wiley & Sons, Inc., 2008
Solt, M.E. and M. Statman, “How useful is the sentiment index”, Financial Analysts Journal, pp. 45–55, 1988
Whaley, R.E., ”The investor fear gauge”, Journal of Portfolio Management 26, 2000
Yaw-Huei Wang, Y.H. and A. Keswani, Stephen J. Taylor,” The relationships between sentiment, returns and volatility”, International Journal of Forecasting, Volume 22, Pages 109-123, 2005		zh_TW