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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/49552


    Title: 資產配置,波動率與交易密集度
    Asset allocation, Volatility and Trading Intensity
    Authors: 張炳善
    Chang, Ping Shan
    Contributors: 郭炳伸
    林信助
    張炳善
    Chang, Ping Shan
    Keywords: 實現波動率
    時間轉換過程
    交易次數時間抽樣法
    風險趨避
    realized volatility
    time-deformation
    transaction time sampling
    risk-aversion
    Date: 2009
    Issue Date: 2010-12-08 13:42:40 (UTC+8)
    Abstract: 本文旨在探討具有捕捉交易密集度特性的波動率測度模型是否能幫助投資者改
    善其資產配置的決策。因此，本文分別考量了利用兩種不同價格抽樣方式所計算
    出來的實現波動率 (realized volatility) 模型： (1) 日曆時間抽樣法 (calendar time sampling scheme) 與 (2) 交易次數時間抽樣法 (transaction time sampling scheme)。相較於另一廣為應用的一般化自我迴歸條件異質變異 (Generalized Autoregressive
    Conditional Heteroskedasticity) 模型而言，這兩種實現波動率模型的優點除了在於它們可以捕捉日內資產報酬率的動態變化之外，交易次數時間抽樣法更可以另外捕捉市場的交易密集度。因此利用交易次數間抽樣法所計算出的實現波動率相對提供給投資者較多的訊息。本文利用了West, Edison and Cho (1993) 所提出的資產組合期望效用模型衡量三種波動率測度的預測績效：(1) 實現波動率 - 日曆時間抽樣法 (2) 實現波動率 - 交易次數時間抽樣法 (3) 指數型一般化自我迴歸條件異質變異 (Exponential Generalized Autoregressive Conditional Heteroskedasticity)。我們的實證結果發現，只有在投資者風險趨避係數越小的條件下，此三種波動率測度模型兩兩之間才有較大的期望效用差距；另外，有趣的是，當市場存在異常的交易波動現象時，交易次數時間抽樣法下的實現波動率所產生的期望效用值總是不輸給另外兩種波動率測度模型的結果。
    This paper examines whether volatility measures that account for trading intensity would help investors make better decisions in their asset allocation. Specifically, we consider two versions of realized volatility (RV), namely, one (RV-C) constructed by regular calendar time sampling, and the other one (RV-T) constructed by transaction time sampling. Comparing to models in the GARCH family, both of these two RVs can capture intraday variations of asset return dynamics. In particular, the RV-T incorporates intraday trading intensity, and hence provides even more valuable information for investors. With the utility-based approach developed by West, Edison, and Cho (1993), we compare the predictive performance of RV-C, RV-T, and the EGARCH model in terms of utility generated with each of these three volatility measures. Our empirical results show that the three measures differ from each other mostly when investors are less risk-averse. Most interestingly, the time-deformed RV-T weakly dominates the RV-C and the EGARCH model when the markets are extremely volatile.
    Reference: 1. Ait-Sahalia, Y., Mykland, P., Zhang, L. (2005a). A Tale of Two Scales: Determining Integrated Volatility With Noisy High-Frequency Data. Journal of the American Statistical Association 100: 1394-1411
    2. Ait-Sahalia, Y., Mykland, P., Zhang, L. (2005b). Ultra-high frequency volatility estimation with dependent microstructure noise. NBER working paper 11380.
    3. Andersen, T. G., Bollerslev, T., (1997). Heterogeneous information arrivals and return volatility dynamics: Uncovering the long-run in high frequency returns. Journal of Finance 52 (3): 975-1005.
    4. Andersen, T. G., Bollerslev, T. (1998a). Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. International Economic Review 39(4):885-905.
    5. Andersen, T. G., Bollerslev, T. (1998b). Deutsche Mark-Dollar Volatility: Intraday Activity Patterns,Macroeconomic Announcements, and Longer Run Dependencies. Journal of Finance 53(1): 219-265.
    6. Andersen, T. G., Bollerslev, T., Diebold, F. X., Labys, P. (1999), (Understanding, Optimizing, Using and Forecasting) Realized Volatility and Correlation, New York University, Leonard N. Stern School Finance Department Working Paper Seires with number 99-061.
    7. Andersen, T. G., Bollerslev, T., Diebold, F. X., Labys, P. (2000), Exchange Rate Returns Standardized by Realized Volatility are (Nearly) Gaussian. Multinational Finance Journal 4: 159-169
    8. Andersen, T. G., Bollerslev, T., Diebold, F. X., Ebens, H. (2001). The distribution of stock return volatility. Journal of Financial Economics 61:43-76.
    9. Andersen, T. G., Bollerslev, T., Diebold, F. X., Labys, P. (2003). Modeling and forecasting realized volatility. Econometrica 71(2):579-625
    10. Bachelier, L., (1900). Theory of Speculation. Paris: Gauthier-Villars
    11. Barndorff-Nielsen, O. E., Shephard, N. (2004). Econometric analysis of realised covariation: High frequency based covariance, regression and correlation in financial economics. Econometrica 72(3):885-925.
    12. Bollerslev, T., Chou, R., Kroner, K. (1992), ARCH modeling in finance – A review of the theory and empirical evidence. Journal of Econometrics 52: 5-59
    13. Bodie, Z., Kane, A., Marcus, A. J. (1999). Investments, Fourth edition. New York: McGraw-Hill
    14. Clark, P. K. (1973). A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices. Econometrica 41: 135-155.
    15. Easley, D., O’ Hara, M. (1992). Time and the Process of Security Price Adjustment. Journal of Finance XLVII：577-605.
    16. Epps, T. W., Epps, R. E. (1976). The stochastic dependence of security price changes and transaction volume: Implication for the mixture-of-distribution hypothesis. Econometrica 44: 305-321.
    17. Fukasawa, M. (2009). Central limit theorem for the realized volatility based on tick time sampling. Finance and Stochastics：Online First
    18. Giot, P., Laurent, S. (2004). Modelling daily Value-at-Risk using realized volatility and ARCH type models. Journal of Empirical Finance 11: 379-398.
    19. Gonzalez-Rivera, G., Lee, T. H., Mishra, S. (2004). Forecasting volatility: A reality check based on option pricing, utility function, value-at-risk, and predictive likelihood. International Journal of Forecasting 20: 629-645.
    20. Griffin, J., Oomen, R. C. (2008). Sampling returns for realized variance calculations: tick time or transaction time? Econometric Review 27:230-253
    21. Grossman, S. J., Shiller, R., J. (1981). The Determinants of the Variability of Stock Market Prices. American Economic Review 71: 222-227.
    22. Hansen, P. R., Lunde, A. (2005). A forecast comparison of volatility models: does anything beat a GARCH(1,1)?. Journal of Applied Econometrics 20(7):873-889.
    23. Hansen, P. R., Lunde, A. (2006). Realized variance and market microstructure noise. Journal of Business & Economic Statistics 24(2):127-161.
    24. Mandelbrot, B., Taylor, H., (1967). On the distribution of stock price differences. Operation Research 15: 1057-1062.
    25. McAleer, M., Mederios, M. C., (2008). Realized Volatility：A Review. Econometric Review 27: 10-45.
    26. Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: a New Approach. Econometrica 59(2) :347-370
    27. O’ Hara, M. (1995). Market Microstructure Theory, Blackwell.
    28. Oomen, R. C. (2005). Properties of bias-corrected realized variance under alternative sampling schemes. Journal of Financial Econometrics 3(4):555-577.
    29. Oomen, R. C. (2006). Properties of realized variance under alternative sampling schemes. Journal of Business & Economic Statistics 24(2):219-237.
    30. Sowell, F. (1992). Maximum Likelihood Estimation of Stationary Univariate Fractionally Integrated Time Series Models. Journal of Econometrics, 53, 165-188.
    31. Upton, D. E., Shannon, D. S. (1979). The stable Paretian distribution, subordinated stochastic processes, and asymptotic log-normaility: A empirical investigation. Journal of Finance 34: 1031-1039.
    32. Wasserfallen, W., Zimmermann, H. (1985). The behavior of intraday exchange rates. Journal of Banking and Finance 9:55–72.
    33. West, K. D., Edison, H. J., Cho, D. (1993). A Utility Based Comparison of Some Models of Exchange Rate Volatility. Journal of International Economics 35, 23-45.
    34. Zhang, L. (2006). Efficient Estimation of Stochastic Volatility Using Noisy Observations: A Multi-Scale Approach. Bernoulli, 12 (6): 1019-1043
    35. Zhou, B. (1996). High frequency data and volatility in foreign-exchange rates. Journal of Business & Economic Statistics 14(1):45-52.
    Description: 碩士
    國立政治大學
    國際經營與貿易研究所
    97351002
    98
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0097351002
    Data Type: thesis
    Appears in Collections:[國際經營與貿易學系 ] 學位論文

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