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研究生: 陳鈺雯
Yu-wen Chen
論文名稱:
Firm Attributes and Long Memory in Volatility
指導教授: 周賓凰
Pin-huang Chou
口試委員:
學位類別: 碩士
Master
系所名稱: 管理學院 - 財務金融學系
Department of Finance
畢業學年度: 99
語文別: 英文
論文頁數: 48
中文關鍵詞: 波動持續性緩長記憶異質信念GPH 模型馬可夫轉換模型資訊不確定性套利風險
外文關鍵詞: Arbitrage r, Long memory, Volatility persistence
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  • 本研究探討何種公司特性與投資人行為會產生股票報酬變異數的緩長記憶。從行為財務學的觀點來看,如果市場上雜訊交易者的數量大於理性投資者,股價將明顯遠離基本價值,並導致股價報酬變異數有持續性的現象。投資人傾向低估公開資訊、高估私有訊息,甚至是在樂觀時交易過多的股票,這些行為都將產生報酬變異數有緩長記憶的現象。實證結果顯示大公司、高機構投資人持股比例、低營業活動現金流量分散性、歷史較久的公司、較多分析師研究的公司擁有較高程度的緩長記憶。公司擁有較低的套利風險也會有較高程度的緩長記憶。本研究也檢驗何種公司特性與投資人行為會產生制度上的轉換。結果顯示公司擁有較低的套利風險、高交易量的公司、大型公司、歷史較久的公司、高數量的機構投資人、與高機構投資人持股比例的公司,其兩種狀態的轉換機率較大。由結果可知,導致股價報酬變異數緩長記憶的公司特性、投資人行為,與導致轉換機率較高的公司特性、投資人行為一致。本研究與Diebold和Inoue在2001年的結果一致。


    In this paper, we examine the cross-sectional determinants of firm characteristics that explain the long memory in stock volatility. From the view of behavioral finance, the number of noise traders is larger than rational arbitrageurs; the price diverges significantly from fundamental values then drives the volatility persistence. Investors tend to underreact to public information and overreact to private information and trade more when they are optimistic. All of those behaviors drive the phenomena of long memory in return volatility. We find that firms with larger market capitalization, higher percentage of institutional ownership, lower dispersion in cash flow from operations, older history, and wider analyst coverage, have higher degrees of long memory in return volatility. Firms with lower arbitrage risk also have higher degrees of long memory. We also examine regime switching model to see what firm characteristics and investor behavior could force the long memory in volatility. And we find that lower arbitrage risk, lower bid-ask spread, lower stock price, higher trading volume, larger firm, higher number of institution owners, higher percentage of institutional ownership, and older history drive higher transition probabilities in both states. This result is consistent with that of Diebold and Inoue (2001) where when both p00 and p11 are large, it appears that the parameter d of GPH model is away from zero, in other words, drive the volatility persistence.

    中文摘要 i Abstract ii 致 謝……………………………………………...………………………………….iii Contents iv List of Tables v 1. Introduction 1 2. Explanations of the market mispricing phenomenon 5 2-1Arbitrage risk 5 2-2Information uncertainty 7 2-3Heterogeneous beliefs 8 2-4Comprehensive proxies 10 3. The method to estimate long memory and transition probability 12 3-1 GPH model 12 3-2 Markov-switching models 14 4. Data and empirical results 16 4-1Definitions and data sources 16 4-2Empirical results 19 4-2-1 Empirical results for the parameter d of GPH model………………............19 4-2-2 Empirical results for the transition probabilities of Markov switching models ………...…………………………………………………...………...…….…….23 5. Conclusion 25 References 28

    Ajinkya, Bipin B. and Michael J. Gift, 1985, “Dispersion of Financial Analysts’ Earnings Forecasts and the (Option Model) Implied Standard Deviations of Stock Returns,” Journal of Finance 40, 1353-1365.
    Ali, Ashiq, Lee-Seok Hwang, and Mark A. Trombley, 2003, “Arbitrage Risk and the Book-to-market Anomaly,” Journal of Financial Economics, 69, 355-373.
    Anderson, Evan W., Eric Ghysels, and Jennifer L. Juergens, 2005, “Do Heterogeneous Beliefs Matter for Asset Pricing?” Review of Financial Studies, 18, 875-924.
    Baillie, Richard T., Tim Bollerslev, and Hans O. Mikkelsen, 1996, “Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity,” Journal of Econometrics 74, 3-30.
    Baker, Malcolm and Jeffrey Wurgler, 2007, “Investor Sentiment in the Stock Market,” Journal of Economic Perspectives, 21, 129-151.
    Banerjee, Anindya and Giovanni Urga, 2005, “Modeling Structural Breaks, Long Memory and Stock Market Volatility: An Overview,” Journal of Econometrics 129, 1-34.
    Barkoulas, John T., Christopher F. Baum, 1996, “Long-term Dependence in Stock Returns,” Economics Letters 53, 253-259.
    Barkoulas, John T., Christopher F. Baum, and Nickolaos Travols, 2000, “Long Memory in the Greek Stock Market,” Applied Financial Economics 10, 177-184.
    Barron, Orie E., Oliver Kim, Steve C. Lim, and Douglas E. Stevens, 1998, “Using Analysts’ Forecasts to Measure Properties of Analysts’ Information Environment,” Accounting Review 73, 421-433.
    Barry, Christopher B. and Stephen J. Brown, 1985, “Differential Information and Security Market Equilibrium,” Journal of Financial and Quantitative Analysis 20, 407-422.
    Bartov, Eli, Suresh Radhakrishnan, and Itzhak Krinsky, 2000, “Investor Sophistication and Patterns in Stock Returns after Earnings Announcements,” Accounting Review 75, 43-63.
    Bollerslev, Tim and Hans O. Mikkelsen, 1996, “Modeling and Pricing Long Memory in Stock Market Volatility,” Journal of Econometrics 73, 151-184.
    Brennan, Michael J, Narasimhan Jegadeesh, and Bhaskaran Swaminathan, 1993, “Investment Analysis and the Adjustment of Stock Prices to Common Information,” Review of Financial Studies 6, 799-824.
    Chan, Louis K., Narasimhan Jegadeesh, and Josef Lakonishok, 1995, “Evaluating the Performance of Value Versus Glamour Stocks: The Impact of selection bias,” Journal of Financial Economics 38, 269-296.
    Cheung, Yin-Wong, 1993, “Long Memory in Foreign-Exchange Rates,” Journal of Business and Economic Statistics 11, 93-101.
    Cheung, Yin-Wong and Kon Lai, 1995, “A Search for Long Memory in International Stock Market Returns,” Journal of International Money and Finance 14, 597-615.
    Diebold, Francis X. and Atsushi Inoue, 2001, “Long Memory and Regime Switching,” Journal of Econometrics 105, 131-159.
    Diebold, Francis X. and Peter Pauly, 1987, “Structural Change and the Combination of Forecasts,” Journal of Forecasting 6, 21-40.
    Ding, Zhuanxin, Clive W. J. Granger, and Robert F. Engle, 1993, “A Long Memory Property of Stock Market Returns and A New Model,” Journal of Empirical Finance 1, 83-106.
    DiSario, Robert, Hakan Saraoglu, Joseph McCarthy, and His Li, 2008, “Long Memory in the Volatility of An Emerging Equity Market: The Case of Turkey,” Journal of International Financial Markets, Institutions and Money 18, 305-312.
    Fama, Eugene F., 1976, “Foundations of Finance: Portfolio Decision and Security Prices,” New York: Basic Books Inc.
    Glosten, Lawrence R. and Lawrence E. Harris, 1988, “Estimating the Components of the Bid/Ask Spread,” Journal of Financial Economics 21, 123-142.
    Gil-Alana, L. A., 2006, “Fractional Integration in Daily Stock Market Indexes,” Review of Financial Economics 15, 28-48.
    Geweke, John and Susan Porter-Hudak, 1983, “The Estimation and Application of Long Memory Time Series Models,” Journal of Time Series Analysis 4, 221-238.
    Grau-Carles, Pilar, 2000, “Empirical Evidence of Long-range Correlations in Stock Returns,” Physica A 287, 396-404.
    Granger, C. W. J. and R. Joyeux, 1980, “An Introduction to Long Memory Time Series Models and Fractional Differencing,” Journal of Time Series Analysis 1, 15-29.
    Harris, Milton and Artur Raviv, 1993, “Differences of Opinion Make a Horse Race,” Review of Financial Studies 6, 473-506.
    Hamao, Yasushi, Ronald W. Masulis, and Victor Ng, 1990, “Correlation in Price Changes and Volatility Across International Stock Markets,” Review of Financial Studies 3, 281-307.
    Hamilton, James D., 1989, “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle,” Econometrica 57, 357-384.
    Hamilton, James D. and Raul Susmel, 1994, “Autoregressive Conditional Heteroskedasticity and Changes in Regime,” Journal of Econometrics 64, 307-333.
    Hirshleifer, David, 2001, “Investor Psychology and Asset Pricing,” Journal of Finance 56, 1533-1596.
    Hosking, J. R. M., 1981, “Fractional Differencing,” Biometrika 68, 165-176.
    Kang, Sang Hoon, Chongcheul Cheong, and Seong-Min Yoon, 2009, “Long Memory Volatility in Chinese Stock Markets,” Physica A 389, 1425-1433.
    Kang, Sang Hoon, Chongcheul Cheong, and Seong-Min Yoon, 2010, “Contemporaneous Aggregation and Long-memory Property of Returns and Volatility in the Korean Stock Market,” Physica A 389, 4844-4854.
    Kyle, Albert S., 1985, “Continuous Auctions and Insider Trading,” Econometrica 53, 1315-1335.
    Lakonishok, Josef, Robert W. Vishny, Andrei Shleifer, 1994, “Contrarian Investment, Extrapolation, and Risk,” Journal of Finance 49, 1541-1578.
    Lamoureux, Christopher G. and William D. Lastrapes, 1990. “Persistence in Variance, Structural Change, and the GARCH Model,” Journal of Business & Economic Statistics 8, 225-234.
    Lesmond, David A., Joseph P. Ogden, and Charles A. Trzcinka, 1999, “A New Estimate of Transaction Costs,” Review of Finance Studies 12, 1113-1141.
    Liu, Ming, 2000, “Modeling Long Memory in Stock Market Volatility,” Journal of Econometrics 99, 139-171.
    Lo, Andrew W., 1991, “Long-term Memory in Stock Market Prices,” Econometrica 59, 1279-1313.
    Mendenhall, Richard R., 2004, “Arbitrage Risk and Post-Earnings-Announcement Drift,” Journal of Business 77, 875-894.
    Sadique, Shibley and Param Silvapulle, 2001, “Long-term Memory in Stock Market Returns: International Evidence,” International Journal of Finance and Economics 6, 59-67.
    Shalen, Catherine T., 1993, “Volume, Volatility and the Dispersion of Beliefs,” Review of Financial Studies 6, 405-434.
    Shleifer, Andrei and Robert W. Vishny, 1997, “The Limits of Arbitrage,” Journal of Finance 52, 35-55.
    Taylor, Stephen J., 1986, “Modeling Financial Time Series,” New York, John Wiley &Sons.
    Utama, Siddharta and William M. Cready, 1997, “Institutional Ownership, Differential Predisclosure Precision and Trading Volume at Announcement Dates,” Journal of Accounting and Economics 24, 129-150.
    Walther, Beverly R., 1997, “Investor Sophistication and Market Earnings Expectations,” Journal of Accounting Research 35, 157-192.
    Zhang, X. Frank, 2006, “Information Uncertainty and Stock Returns,” Journal of Finance 61, 105-137.
    Zumbach, Gilles, 2004, “Volatility Processes and Volatility Forecast with Long Memory,” Quantitative Finance 4, 70-86.

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