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題名：隱含波動度, 投資人情緒與市場指數之互動關係與策略應用
作者：魏裕珍 引用關係
作者(外文)：Wei, Yu-Chen
校院名稱：國立交通大學
系所名稱：管理科學系所
指導教授：許和鈞
學位類別：博士
出版日期：2010
主題關鍵詞：隱含波動度；投資人情緒；波動度預測；門檻模型；因果；選擇權交易策略；Implied volatility；Investor sentiment；Volatility forecasting；Threshold model；Causality；Options trading strategy
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本文由行為財務學角度剖析隱含波動度、投資人情緒與市場指數之互動關係，並以臺灣證券市場資料構建波動度指數及投資人情緒指標之代理變數進行相關實證分析及策略應用。
研究內容主要分為三個部分。第一部分應用門檻模型(Chan, 1993)檢測投資人情緒過度反應之門檻水準，並剖析不同市場狀況下，投資人情緒與市場報酬間之因果關係，實證結果顯示若未考慮市場狀態，投資人情緒指標與市場報酬之間存在雙向之因果關係，然而，當投資人情緒在極端高或低之區域時，對於市場報酬將具有指引效果。第二部分則應用門檻共整合模型(Hansen and Seo, 2002)探討波動度指數之資訊內涵與標的指數間之關聯性，實證結果顯示，當買權之隱含指數領先加權股價指數時，臺灣證券市場之參與者可應用此資訊做為投資組合調整之參考。第三部分進一步考量投資人情緒指標進行波動度預測，並應用至選擇權交易策略，比較結果顯示，若納入投資人情緒指標，模型之配適與預測績效將優於其他比較模型，特別是納入市場週轉率與市場恐慌指標代理變數-選擇權隱含波動度。
綜而觀之，在探討波動度、投資人情緒與市場指數之互動關係時，應將投資人情緒可能存在的不對稱效果納入考量，未來的研究亦可進一步納入投資人情緒的不對稱效果進行波動度預測，並將研究結果實際應用至交易策略中。
This dissertation investigates the interaction among implied volatility, investor sentiment and market index from the behavioral finance point of view. The volatility measures and proxies of investor sentiment are constructed and the empirical results and strategy application are analyzed in the emerging Taiwan equity market.
There are three main parts in this study. In the first part, we apply a threshold model (Chan, 1993) to detect the extreme level of investors’ sentiment econometrically and investigate the causal relationships between sentiment and returns under different market scenarios. The empirical results show that most of the sentiment measures exhibit a feedback relationship with returns while ignoring different market states. However, sentiment could be a leading indicator if the higher or lower levels of sentiments being distinguished. In the second part, the relationship between the information content implied by the options market-based volatility and the underlying stock index is analyzed through a threshold cointegration model (Hansen and Seo, 2002). Empirical findings show that investors participating in the Taiwan stock market could rebalance their equity portfolios while the implied index derived from the call options takes precedence over the market index. In the last part, an algorithm for effective options trading strategy based on volatility forecasts incorporating investor sentiment is proposed. The forecast evaluation supports the significant incremental explanatory power of investor sentiments in the fitting and forecasting of future volatility in relation to its adversarial multiple-factor model, especially the market turnover and the volatility index which is referred to as the investor fear gauge.
Overall, the asymmetric property of investor sentiment should be incorporated into the interactive analysis between volatility, sentiment and market index. Future research could further investigate the volatility forecasting incorporating the asymmetry of investor sentiment and apply the findings to the actual trading strategies.
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