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題名：S&P500選擇權市場投資人交易隱含資訊與日曆價差組合策略之研究
作者：張鼎煥
作者(外文)：Ting-Huan Chang 張鼎煥
校院名稱：淡江大學
系所名稱：財務金融學系博士班
指導教授：邱建良
李命志
學位類別：博士
出版日期：2010
主題關鍵詞：價性；選擇權；風險偏好；波動率指數；日曆價差；Moneyness；Options；Risk Preference；Volatility Index；Calendar Spread
原始連結：連回原系統網址new window
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本篇論文分成三部份探討S&P 500選擇權市場投資人交易隱含資訊與日曆價差組合策略。第一部份主要經由選擇權標的資產波動、成交量、波動率指數、期貨報酬、期貨與現貨關係，研究買權與賣權之價內、價平與價外投資行為所反映投資人風險偏好、市場預期與交易動機。實證資料利用去除平均數方法降低到期效果之干擾，統計發現S&P 500選擇權市場投資人權衡價外較高風險與價內較高持有成本，實際傾向交易價平之選擇權，實證結果亦發現買權與賣權投資人皆具風險趨避偏好與市場反轉預期，賣權投資人交易動機為避險；而買權投資人交易動機則為投機。亦發現價內選擇權可以獲得較價外選擇權高之投資報酬。
第二部份成功利用一階自我迴歸跳躍模型捕捉S&P 500選擇權買權與賣權之間價性距離變動跳躍的強度，藉此探討買權與賣權即多空部位投資人對市場資訊反應是否一致？實證結果發現77.33% 的反應是一致的，但也有22.67%的反應不一致，尤其當市場發生較大事件衝擊期間。後續利用標的資產價格、波動與無風險利率分析發現，當市場低度不確定性即風險較低時，買權與賣權投資人反應較一致；反之當市場高度不確定性即風險較高時，買權與賣權投資人反應較不一致。
日曆價差是利用到期效果創造水平價差交易策略，第三部份計算連續120組S&P 500選擇權近月與次近月之買權與賣權，距到期日前7天至前3天之日曆價差績效，利用最小迴歸虛擬變數模型與數值解方法，估計最適靜態與動態交易策略。靜態交易策略實證結果發現價平的買權與賣權績效較價內與價外高，距到期日越遠日曆價差績效越好，距到期日前7天至前3天價平賣權績效皆較其他組合高；動態交易策略則經由前1日標的資產或波動率指數報酬變動作指標作為買權與賣權、價內與價外交叉組合，實證結果發現，雖然動態交易策略日曆價差績效皆較靜態交易策略佳，但其適用時點常必須當市場發生較大衝擊。
In the first part of this thesis measures option investors’ risk preferences and the motivations underlying option trading behavior by using the adjusted moneyness when initial moneyness has been influenced by the time-to-maturity effect in the contract month. The adjusted moneyness is calculated using the de-mean process by removing the average moneyness of each trading date prior to the expiration date for the minimization of anomalies. The statistics of the adjusted moneyness indicate that both call and put option investors essentially prefer to trade at-the-money options The regression estimation also confirm that both call and put option investors have significant risk-aversion preferences and market-reversion expectations. Furthermore, put option investors’ motivation underlying option trading behavior is hedge, but call option investors’ trading activity is motivated by speculation. In addition, another goal in this part of thesis is to determine the relationship between option returns and its moneyness. The finding confirms the option investors trade in-the-money options that can obtain larger capital gains than trade out-of-the-money options.
For measuring the option’s response against the market information, the second part of this thesis successfully captures the time-varying jump intensity of the range of option moneyness spread by the autoregressive conditional jump intensity (ARJI) model. This study finds the option investors’ response of 77.33% and 22.67% observations is consistent and inconsistent against the market information, respectively. Furthermore, the relationships between option investors’ response and underlying asset’s price variation and volatility, and risk-free interest rate are use to investigate the option investors’ expectation for the market trend. The option investors have similar expectations when the low degrees of market uncertainty, relative to the low degrees of market uncertainty, the high degrees of market uncertainty causes the option investors have dissimilar expectations. Therefore, the option investors are consistent or inconsistent response to the market information which follows their similar or dissimilar expectation for the market trend under conditions of the degrees of market uncertainty.
The calendar spreads often created when the option returns have anomalies that might occur close to the expiration date. In the final part of this thesis uses the least squares dummy variable (LSDV) model and numerical analysis to investigate performance of the optimal static and dynamic trading strategy of call and put option calendar spreads across time-to-maturity and option moneyness in the S&P 500 Index Options (SPX) market. This study finds that both the neutral calls and puts calendar spreads have the highest outperformance than bullish and bearish, and the premium payoffs are obvious declining along the time-to-maturity. By using the static trading strategy, the investors can create the neutral puts calendar spread to maximize their expected profits. Relative to the static trading strategy, the dynamic trading strategy respects to the underlying asset prices and market volatility can maximize the investors’ expected premium payoffs, despite the one-period lagged price variations of the underlying S&P 500 Stock Index and VIX may not be good signals because they merely operate when the high degrees of market shocks.
PART I

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Black, F., Scholes, M., 1973. The pricing of options and corporate liabilities. Journal of Political Economy 81, 637-659.
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Brookfield, D., Ormrod, P., 2000. Executive stock options: volatility, managerial decisions and agency costs. Journal of Multinational Financial Management 10, 275-295.
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Kamara, A., Miller, T., 1995. Daily and intradaily tests of European put-call parity. Journal of Financial and Quantitative Analysis 30, 519-539.
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Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., Shin, Y., 1992. Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root?. Journal of Econometrics 54, 159-178.
Lakonishok, J., Lee, I., Pearson, N., Poteshman, A., 2007. Option market activity. Review of Financial Studies 20, 813-857.
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Simon, D.P., Wiggins III, R.A., 2001. S&P futures returns and contrary sentiment indicators. Journal of Futures Markets 21, 447-462.
Whaley, R.E., 1993. Derivatives on market volatility: hedging tools long overdue. Journal of Derivatives 1, 71-84.new window
Whaley, R.E., 2000. The investor fear gauge. Journal of Portfolio Management 26, 12-17.
White, H., 1980. A heteroscedasticity-consistent covariance matrix estimator and a direct test for heteroscedasticity. Econometrica 48, 817-838.
Ziegler, A., 2007. Why does implied risk aversion smile?. Review of Financial Studies 20, 859-904.

PART II

Admati, A.R., Pfleiderer, P., 1988. A theory of intraday trading patterns: volume and price variability. Review of Financial Studies 1, 3-40.new window
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Black, F., Scholes, M., 1973. The pricing of options and corporate liabilities. Journal of Political Economy 81, 637-659.
Bollen, N.P.B., Whaley, R., 2004. Does net buying pressure affect the shape of implied volatility functions?. Journal of Finance 59, 711-753.
Brookfield, D., Ormrod, P., 2000. Executive stock options: volatility, managerial decisions and agency costs. Journal of Multinational Financial Management 10, 275-295.
Cao, C., Chen, Z., Griffin, J.M., 2005. Information content of option volume prior to takeovers. Journal of Business 78, 1073-1109.
Chakravarty, S., Gulen, H., Mayhew, S., 2004. Informed trading in stock and option markets. Journal of Finance 59, 1235-1257.
Chakravarty, S., Gulen, H., Mayhew, S., 2004. Informed trading in stock and option markets. Journal of Finance 58, 1235-1257.
Chan, K.C., Cheng, L.T.W., Lung, P.P., 2005. Asymmetric volatility and trading activity in index futures options. Financial Review 40, 381-407.
Chan, W.H., Maheu J.M., 2002. Conditional jump dynamics in stock market returns. Journal of Business and Economic Statistics 20, 377-389.
Chan, K., Chung, Y.P., Fong, W.M., 2002. The informational role of stock and option volume. Review of Financial Studies 15, 1049-1075.
Chang, C.C., Hsieh, P.F., Lai, H.N., 2009. Do informed option investors predict stockspread? Evidence from the Taiwan stock exchange. Journal of Banking and Finance 33, 757-764.
Chang, C.C., Hsieh, P.F, Wang, Y.H., 2010. Information content of options trading volume for future volatility: Evidence from the Taiwan options market. Journal of Banking and Finance 34, 174-183.
Chern, K.Y., Tandon, K., Yu, S., Webb, G., 2008. The information content of stock split announcements: do options matter? Journal of Banking and Finance 32, 930-946.
Day, T., Lewis, C., 1998. The behavior of the volatility implicit in the prices of stock index options. Journal of Financial Economics 22, 103–122.
Dickey, D.A., Fuller, W.A., 1981. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49, 1057-1072.
Etling, C., Miller, T., 2000. The relationship between index option moneyness and relative liquidity. Journal of Futures Markets 20, 971–987.
Garay, U.A., Justiniano, R., López, M., 2003. The relationship between options moneyness and liquidity: evidence from options on futures on S&P 500 Index. Derivatives Use, Trading and Regulation 8, 305-323.
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Kamara, A., Miller, T., 1995. Daily and intradaily tests of European put-call parity. Journal of Financial and Quantitative Analysis 30, 519-539.
Kwiatkowski, D., Phillips, P.C.B., Schmidt, P., Shin, Y., 1992. Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root?. Journal of Econometrics 54, 159-178.
Lakonishok, J., Lee, I., Pearson, N.D., Poteshman, A.M., 2007. Option market activity. Review of Financial Studies 20, 813-857.
Lee, M, Chen, C.D., 2005. The intraday behaviors and relationships with its underlying assets: evidence on option market in Taiwan. International Review of Financial Analysis 14, 587-603.
Merton, R.C., 1976. Option pricing when underlying stock spread are discontinuous. Journal of Financial Economics 3, 125-144.
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Pan, J., Poteshman, A.M., 2006. The information in option volume for future stock price. Review of Financial Studies 19, 871-908.
Phillips, P., Perron, P., 1988. Testing for a unit root in time series regression. Biometrika 75, 335-346.
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PART III

Banerjee, P.S., Doran, J.S., Peterson D.R., 2007. Implied volatility and future portfolio returns. Journal of Banking and Finance 31, 3183-3199.
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Simon, D.P., Wiggins III, R.A., 2001. S&P futures returns and contrary sentiment indicators. Journal of Futures Markets 21, 447-462.
Whaley, R.E., 1993. Derivatives on market volatility: hedging tools long overdue. Journal of Derivatives 1, 71-84.new window
Whaley, R.E., 2000. The investor fear gauge. Journal of Portfolio Management 26, 12-17.
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