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    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/30024


    Title: 運用長期記憶模型於估計股票指數期貨之風險值
    Estimating Value-at-Risk for stock index futures using Double Long-memory Models
    Authors: 唐大倫
    Tang,Ta-lun Tang
    Contributors: 謝淑貞
    唐大倫
    Tang,Ta-lun Tang
    Keywords: 長期記憶性
    不對稱
    風險值
    ARFIMA
    FIGARCH
    Long memory
    Asymmetry
    Value-at-Risk
    Date: 2003
    Issue Date: 2009-09-11 17:05:05 (UTC+8)
    Abstract: 在本篇文章中,我們採用長期記憶模型來估計S&P500、Nasdaq100和Dow Jones Industrial Index三個股票指數期貨的日收盤價的風險值。為了更準確地計算風險值,本文採用常態分配、t分配以及偏斜t分配來做模型估計以及風險值之計算。有鑒於大多數探討風險值的文獻只考慮買入部位的風險,本研究除了估計買入部位的風險值,也估計放空部位的風險值,以期更能全面性地估算風險。實證結果顯示,ARFIMA-FIGARCH模型配合偏斜t分配較其他兩種分配更能精確地估算樣本內的風險值。基於ARFIMA-FIGARCH模型配合偏斜t分配在樣本內風險值計算的優異表現,我們利用此模型搭配來實際求算樣本外風險值。結果如同樣本內風險值一般,ARFIMA-FIGARCH模型配合偏斜t分配在樣本外也有相當好的風險預測能力。
    In this thesis, we estimate Value-at-Risk (VaR) for daily closing price of three stock index futures contracts, S&P500, Nasdaq100, and Dow Jones, using the double long memory models. Due to the existence of a long-term persistence characterized in our data, the ARFIMA-FIGARCH models are used to compute the VaR. In order to investigate better, three kinds of density distributions, normal, Student-t, and skewed Student-t distributions, are used for estimating models and computing the VaR. In addition to the VaR for the long trading positions which most researches focus on to date, the VaR for the short trading positions are calculated as well in this study. From the empirical results we show that for the three stock index futures, the ARFIMA-FIGARCH models with skewed Student-t distribution perform better in computing in-sample VaR both in long and short trading positions than symmetric models and has a quite excellent performance in forecasting out-of-sample VaR as well.
    Reference: Alexander, C. O. and Leigh, C. T., 1997, On the covariance metrices used in value-at-risk model, The Journal of Derivatives, 50-62.
    Baillie, R. T., Bollerslev, T., and Mikkelsen, H., 1996, Fractionally integrated generalized autoregressive conditional heteroskedasticity, Journal of Econometrics 74, 3-30.
    Baillie, R. T., Chung, C. -F., and Tieslau, M. A., 1996, Analyzing inflation by the fractionally integrated ARFIMA-GARCH model, Journal of Applied Econometrics 11, 23-40.
    Barkoulas, J. T. and Baum, C. F., Long term dependence in stock returns, Working Paper, Department of Economics, Boston College, USA.
    Beine, M. and Laurent, S., 2003, Central bank interventions and jumps in double long memory models of daily exchange rate, Working Paper.
    Bollerslev, T., 1986, Generalized autoregressive conditional heteroskedasticity, Journal of Econometrics 31, 307-327.
    Bollerslev, T. and Mikkelen, H. O., 1996, Modeling and pricing long memory in stock market volatility, Journal of Econometrics 73, 151-184.
    Brunetti, C. and Gilbert, C. L., 2000, Bivariate FIGARCH and fractional cointegration, Journal of Empirical Finance 7, 509-530.
    Chou, C. H., 2000, The performance of VaR measurements- the empirical studies of currency exchange rates, Graduate Institute of Financa, Fu Jen Catholic University.
    Christoffersen, P. F. and Diebold, F. X., 2000, How relevant is volatility forecasting for financial risk management? Review of Economics and Statistics 82, 1-11.
    Ding, Z., Granger, C. W. J., and Engle, R. F., 1993, A long memory property of stock market returns and a new model, Journal of Empirical Finance 1, 83-106.
    Dueker, M. and Asea, P. K., 1995, Non-monotonic long memory dynamics in black-market exchange rates, Working Paper, Federal Reserve Bank of ST. Louis.
    Engle, R. F., 1982, Autoregressive conditional heteroskedasticity with estimates of the variance of united kingdom inflation, Econometrica 50, 987-1007.
    Giot, P. and Laurent, S., 2003, Value-at-risk for long and short trading positions, Journal of Applied Econometrics 18, 641-664.
    Goorbergh, R. V. D. and Vlaar, P., 1999, Value-at-risk analysis of stock returns historical simulation, variance techniques or tail index estimation, http://www.gloriamundi.org.
    Granger, C. W. J. and Ding, Z. 1996, Varieties of long memory models, Journal of Econometrics 73, 61-77.
    Henry, O. T., 2000, Long memory in stock returns: some international evidence, Working Paper, Department of Economics, The University of Melbourne, Australia.
    Jorion, P., 2000, Value-at-risk: The New Benchmark for Managina Financial Risk, McGraw-Hill.
    Kupiec, P., 1995, Techniques for verifying the accuracy of risk measurement models, Journal of Derivatives 2, 174-184.
    Lambert, P. and Laurent, S., 2000, Modeling skewness dynamics in series of financial data, Discussion Paper, Institute de Statistique, Louvain-la-Neuve.
    Lo, A. W., 1991, Long-term memory in stock market price, Econometrica 59, 1279-1313.
    Liu, S. and Brorsen, B., 1995, Maximun likelihood estimation of a GARCH-stable model, Journal of Applied Econometrics 2, 273-185.
    Shieh, S. –J., 2004, Modeling daily value-at-risk using FIAPARCH model with (skewed) Student-t density, Working Paper, Department of International Trade, National Cheng-chi University, Taiwan.
    Shieh, S. –J., 2003, Mean reversion in stock index futures markets, Working Paper, Department of International Trade, National Cheng-chi University, Taiwan.
    Sriananthakumar, S. and Silvapulle, S., 2003, Estimating value at risks for short and long trading positions, Working Paper, Department of Economics and Business Statistics, Monash University, Australia.
    Tse, Y. K., Anh, V. V., and Tieng, Q., Maximun likelihood estimation of the fractional differencing parameter in an ARFIMA model using wavelets, Working Paper.
    Wung, S. B., 1999, The market risk measurement of the warrants of the issuer, The Department of Economics, Soochow University, Taiwan.
    Description: 碩士
    國立政治大學
    國際經營與貿易研究所
    91351022
    92
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0091351022
    Data Type: thesis
    Appears in Collections:[國際經營與貿易學系 ] 學位論文

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