Please use this identifier to cite or link to this item:
標題: The Study on The Long Memory of The Return Rate of The Main Foreign Currencies in Terms of New Taiwan Dollar in Taiwan Exchange Market
作者: 孫承君
Sun, Chen-Jun
關鍵字: long memory;緩長記憶;ARFIMA model;FIGARCH model;ARFIMA模型;FIGARCH模型
出版社: 企業管理學系所
引用: 中文部分 1. 李命志、洪瑞成、劉洪鈞(1997), 厚尾GARCH 模型之波動性預測能力比較., 輔仁管理評論,第十四卷第二期 2. 楊踐為、李家豪、類惠貞(2007),應用時間序列分析法建構台灣證券市場之預測交易模型,中華管理評論,第十卷三期 英文部份 1. Abdol, S. S. (2004). Long Memory in the Gulf States Foreign Currency Markets. Scientific Journal of Administrative, Vol.2. 2. Agiakloglou, C., Newbold, P., and Wohar, M. (1992). Bias in an estimator of the fractional difference parameter. Journal of Time Series Analysis, 14, 235- 246. 3. Andrew, D. (1991). Heteroskedasticity and autocorrelation consistent covariancematrix estimation. Econometrica, 59, 817-858. 4. Anis, A. A., and Lloyd, E. H. (1976). The expected value of the adjusted rescaled Hurst range of independent normal summands. Biometrika, 63, 111-116. 5. Baillie, R. T. (1996). Long memory processes and fractional integration in econometrics. Journal of Econometrics, 73(1), 5-59. 6. Baillie, R. T., Bollerslev, T., and Mikkelsen, H. O. (1996). Fractionally integrated generalised autoregressive conditional heteroskedasticity. Journal of Econometrics, 74(1), 3-30. 7. Bekaert, G. (1995). Market integration and investment barriers in emerging equity markets. World Bank Economic Review, 9, 75-107. 8. Blattberg, R. C., and Gonedes, N. J. (1974). A comparison of the stable and student distribution as statistical models for stock prices. Journal of Business, 47(2), 224-280. 9. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31,307-327. 10. Bollerslev, T., Chou, R.Y., and Kroner, K. F. (1992). ARCH modeling in finance. Journal of Econometrics, 52, 5-59. 11. Box, G. E. P., and Jenkins, G. M. (1970). Time series analysis: Forecasting and control. San Francisco, CA: Holden-Day. 12. Cootner, P. H. (1964). Stock market price: random vs. system change. Industrial Management Review, l3, 24-45. 13. Christian,C.(2007). Non-negativity conditions for the Hyperbolic GARCH Model. KOF Swiss Economic Institute WEH D4, Weinbergstrasse 35, 8092 Zurich, Switzerland. 14. Chung, C.-F. (1999): “Estimating the Fractionnally Intergrated GARCH Model,” National Ta¨iwan University working paper. 15. Davies, R. B., and Harte, D. S. (1987). Tests for Hurst effect. Biometrika, 74, 95- 102. 16. David,G.M.,andAlan,E.H.S.(2006). Long-memory and heterogeneous components in high frequency Pacific-Basin exchange rate volatility. Asia-Pacific Finan Markets, 12,199–226. 17. Dickey, D., and Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association, 74, 427-431. 18. Dickey, D., and Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, 1057-1072. 19. Domowitz, I., Glen, J., and Madhavan, A. (1998). International cross-listing and order flow migration: Evidence from an emerging market. Journal of Finance, 53, 2001-2027. 20. Enders, W. (2004). Applied Econometric Time Series. USA: John Wiley and Son, Inc. 21. Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of UK inflation. Econometrica, 50, 987-1008. 22. Engle, R. F., and Bollerslev, T. (1986). Modeling the persistence of conditional variances. Econometric Reviews, 5, 1-50. 23. Engle, R. F., and Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation and testing. Econometrica, 55, 251-276. 24. Ewing, B. T., and Malik, F. (2005). Re-examining the asymmetric predictability of conditional variances: The role of sudden changes in variance. Journal of Banking and Finance, 29, 2655-2673. 25. Fama, E. F. (1970). Efficient capital market: A review of theory and empirical work. Journal of Finance, 25, 383-417. 26. Fang, W. (2007). Analysis on long memory of the volatilities of international dry bulk freight index using fractal theory. Paper presented at the international conference on Wireless Communications, Networking and Mobile Computing in Shanghai, China. 27. Gab,J.O.,Seungwan,K.,and Cheol,J.E.(2006). Long-term Memory and Volatility Clustering in Daily and High-frequency Price Changes. 28. Geweke, J., and Porter-Hudak, S. (1983). The estimation and application of long memory time series models. Journal of Time Series Analysis, 4, 221-237. 29. Giraitis, L., Kokoszka, P. S., Leipus, R., and Teyssiere, G. (2003). Rescaled variance and related tests for long memory in volatility and levels. Journal of Econometrics, 112, 265-294. 30. Granger, C. W. J. (1986). Developments in the study of cointegrated economic variables. Oxford Bulletin of Economics and Statistics, 48, 213-228. 31. Granger, C. W. J., and Joyeux, R. (1980). An introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis, 1, 15-39. 32. Granger, C. W. J. (1981). Some properties of time series data and their use in econometric model specification. Journal of Econometrics, 16, 121-130. 33. Granger, C. W. J., and Newbold. P. (1974). Spurious regression in econometric. Journal of Econometrics, 12, 111-120. 34. Hansen, P. R., and Lunde, A. (2005). A forecast comparison of volatility models: Does anything beat a GARCH (1, 1)?. Journal of Applied Econometrics, 20(7), 873-889. 35. Harvey, A. C. (1993). Time Series Models (2nd Ed.). Harvester Wheatsheaf. 36. Hosking, J. R. M. (1981). Fractional differencing. Biometrika, 68, 165-76. 37. Hurst, H. E. (1951). Long-term storage capacity of reservoirs. Transactions of the American Society of Civil Engineers, 116, 770-799. 38. Hurvich, C. M., and Beltrao, K. I. (1994). Automatic semiparametric estimation of the long memory parameter of a long memory time series. Journal of Time Series Analysis, 15, 285-302. 39. Hurvich, C. M., and Ray, B. K. (1995). Estimation of the memory parameter for nonstationary or noninvertible fractionally integrated processes. Journal of Time Series Analysis, 16(1), 17-41. 40. Ioannis,A. V., Agustín, D., and Iván,P.(2005).The long memory story of real interest rates can it be supported.(IVIE working papers),Instituto Valenciano de Investigaciones Económicas,S.A., V-442-2005. 41. Janacek, G. J. (1982). Determining the degree of differencing for time series via the log spectrum. Journal of Time Series Analysis, 3, 177-183. 42. John, T. B., Christopher, F. B., Mustafa, C.,and Atreya, C.(1995).Persistent dependence in foreign exchange rates? A reexamination. Department of Economics, 02467-3806. 43. Jon,F.,John,H.R.,Shing-Yi,B.W.,andJonathan,H.W.(2007).Thehigh-frequency response of exchange rates and interest rates to macroeconomic announcements. Journal of Monetary Economics, 54 ,1051,1068. 44. Jong, D. N., Nankervis, J. C., Savin, N. E., Whiteman, C. H. (1992). The power problems of unit root tests in time series with autoregressive errors. Journal of Econometrics, 53, 323-343. 45. Kendall, M. (1953). The analysis of economic time series. Journal of the Royal Statistical Society, 96(1), 11-25. 46. Kennedy, D. (1976). The distribution of the maximum Brownian excursion. Journal of Applied Probability, 13, 371-376. 47. Kunsch, H. (1986). Discrimination between monotonic trends and long-range dependence. Journal of Applied Probability, 23, 1025-1030. 48. Kwiatkowski, D., Phillips, P. C. B., Schmidt, P., and Shin, Y. (1992). Testing the null hypothesis of stationary against the alternatives of a unit root: How sure are we that economic time series have a unit root?. Journal of Econometrics, 54, 159-178. 49. Laurent, S. (2004). Analytical derivates of the APARCH model. Computational Economics, 24, 51-57. 50. Leonardo, R. S.(2004). Spectral Properties of Temporally Aggregated Long Memory Processes,Journal of Assessoria Econômica. 51. LEILA ,N., IBR,AHAMADA.,JAMEL,J., ALAIN,N.(2004) Long-memory and shifts in theunconditional variance in the exchange rate euro/US dollar returns. Applied Economics Letters,11, 591–594 52. Ljung, G. M., and Box, G. E. P. (1978). On a measure of lack of fit in time-series model. Biometrka, 65, 297-303. 53. Lloyd, D. G. (1976). The transmission of genes via pollen and ovules in gynodioecious angiosperms. Theor. Pop. Biol., 9, 299-316. 54. Mandelbrot, B. B. (1963). The variation of certain speculative prices. Journal of Business, 36, 394-419. 55. Mandelbrot, B. B. (1967). How long is the coast of Britain? Statistical self similarity and fractal dimension. Science, 156, 636-638. 56. Mandelbrot, B. B. (1971). When can price be arbitraged efficiently? A limit to the validity of the random walk and martingale models. Review of Economics and Statistics, 53, 225-236. 57. Mandelbrot, B. B. (1972). Statistical methodology for non-periodic cycles: From the covariance to R/S analysis. Annals of Economics and Measurement, 1, 259-290. 58. Mandelbrot, B. B. (1975). Stochastic models for the earth''s relief, the shape and the fractal dimension of the coastlines, and the number-area rule for islands. Proceedings of the National Academy of Sciences of the United States of America, 72(10), 3825-3828. 59. Mandelbrot, B. B. (1977). Fractals: Form, chance, and dimension. San Francisco: Freeman. 60. Mandelbrot, B. B., and Wallis, J. R. (1969). Robustness of the rescaled range R/S in the measurement of noncyclic long-run statistical dependence. Water Resource Research, 967-988. 61. Mantegna, R. M., and Stanley, H. E. (2000). Introduction to Econophysics: Correlations and Complexity in Finance. Cambridge University Press. 62. Márcio, P.L.,andMarcelo,S.P(2003). Long memory in the R$/US$ exchange rate: A robust analysis. (Financelab working paper), flwp,03 , 2003 63. Michael ,D.,andPatrick,K.A.(1995). Non-Monotonic Long Memory Dynamics in Black-Market Exchange Rates. (Federal Reserve Bank of St. Louis Working Papers.), Retrieved from 64. Nabeya, S., and Tanaka, K. (1990). Limiting power of unit-root tests in time-series regression. Journal of Econometrics, 46, 247-271. 65. Nagayasu, J. (2003). The efficiency of the Japanese equity market. (IMF Working Paper 03/142). Retrieved from 66. Nelson, C. R., and Plosser, C. I. (1982). Trends and random walks in macroeconomic time series: Some evidence and implications. Journal of Monetary Economics, 10, 139-162. 67. Nesrin,A.(2006). Long Memory Analysis of USD/TRL Exchange Rate. Journal of Social Sciences, v1-2-16. 68. Panas, E. (2001). Estimating fractal dimension using stable distributions and exploring long memory through ARFIMA models in Athens stock exchange. Applied Financial Economics, 11(4), 395-402. 69. Peters, E. E. (1991). Chaos and order in the capital market. New York: John Willey andSons. 70. Phillips, P. C. B., and Perron, P. (1988). Testing for a unit root in time series regression. Biometrica, 75, 335-346. 71. Richard,T. B.,Aydin, A. C.,Young,W.H.,(2000). High Frequency Deutsche Mark-US Dollar Returns: FIGARCH Representations and Non Linearities. Multinational Finance Journal, vol. 4, no. 3and4, 247–267. 72. Robinson.P.M.(1994).Semiparametric analysis of long-memory time series.Journal of Statistics, Vol. 22, No. 1,515-539. 73. Robinson.P.M. (1995). Log-Periodogram Regression of Time Series with Long Range Dependence. Journal of Statistics, Vol. 23, No. 3 , pp. 1048-1072. 74. Rolf, T.(1994).Long memory in foreign exchange rates revisited. Journal of Statistics and Econometrics. 75. Said, E. S., and Dickey, D. A. (1984). Testing for unit roots in autoregressive- moving average models of unknown order. Biometrika, 71, 599-607. 76. Sang, H. K.,and Seong, M. Y(2008). Intraday Periodicity and Long Memory Property in High Frequency Data. Department of Economics, 609-735. 77. Shiuyan,P.,Mark,B.S.,andStephen,J.T.(2008).Distinguishing short and long memory volatility specifications. Department of Accounting and Finance,Lancaster University. 78. Siddiqui, M. (1976). The asymptotic distribution of the range and other functions of partial sums of stationary processes. Water Resources Research, 12, 1271-1276. 79. Simkowitz, M. A., and Beedies, W. L. (1980). Asymmetric stable distributed security returns. Journal of the American Statistical Association, 75, 306-312. 80. Yaffee, R. A., and McGee, M. (2000). Introduction to time series analysis and forecasting with applications of SAS and SPSS. Academic Press, Inc. 81. Yajima, Y. (1985). On estimation of long memory time series models. Australian Journal of Statistics, 27, 303-320. 82. Yin,W.C.,(2009). Long Memory in Foreign-Exchange Rates. Journal of Business and Economic Statistics.Vol. 11, No. 1 (Jan., 1993), pp. 93-101. 83. Young ,W.H.,(2005). Long memory volatility dependency, temporal aggregation and the Korean currency crisis: the role of a high frequency Korean won (KRW)–US dollar ($) exchange rate. Journal of Japan and the World Economy,17, 97–109.
The understanding of the volatility and movement of exchange rate will help investors and government to hedge and arbitrage. So the issue of the effectiveness of exchange rate market has gained considerable attention for long time. The present paper, therefore, empirically investigate whether long memory exists in the first moment and second moment time series of the main foreign currencies in terms of New Taiwan Dollar in Taiwan exchange market. In testing long memory, the present paper adopts the methodologies of fractal theory: R/S analysis, modified R/S analysis, GPH test, Robinson test, ARFIMA model and FIGARCH model. Firstly, the long memory testing is applied to the first moment of the return rate of the main foreign currencies in terms of New Taiwan Dollar in Taiwan exchange market. The results of the testing would then be used to construct ARFIMA-FIGARCH model and ARMA-FIGARCH model in order to explore the long memory effect in the volatility of the the main foreign currencies in terms of New Taiwan Dollar in Taiwan exchange market ofreturn time series. The research results has indicated that long memory only exists in the first moment of NTD/CNY exchange rate of returns exchange rate. However, the return rate of the six main foreign currencies in terms of New Taiwan Dollar in Taiwan exchange market of the second moment possesses significant long memory effect. Therefore, investors can hedge or speculate by forecasting future volatilities from the historical data. Specially NTD/CNY exists more significant long memory property, so it may more easily be arbitraged.

其他識別: U0005-2306200920551500
Appears in Collections:企業管理學系所

Show full item record

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.