Issue
ESAIM: PS
Volume 6, 2002
New directions in Time Series Analysis (Guest Editor: Philippe Soulier)
Page(s) 311 - 329
Section New directions in Time Series Analysis (Guest Editor: Philippe Soulier)
DOI https://doi.org/10.1051/ps:2002017
Published online 15 November 2002
  1. T.W. Anderson, The Statistical Analysis of Time Series. Wiley, New York (1971). [Google Scholar]
  2. R.T. Baillie, Long memory processes and fractional integration in econometrics. J. Econometrics 73 (1996) 5-59. [CrossRef] [MathSciNet] [Google Scholar]
  3. P. Billingsley, Convergence of Probability Measures. Wiley, New York (1968). [Google Scholar]
  4. T. Bollerslev and H.O. Mikkelsen, Modeling and pricing long memory in stock market volatility. J. Econometrics 73 (1996) 151-184. [CrossRef] [Google Scholar]
  5. F.J. Breidt, N. Crato and P. de Lima, On the detection and estimation of long memory in stochastic volatility. J. Econometrics 83 (1998) 325-348. [CrossRef] [MathSciNet] [Google Scholar]
  6. D.R. Brillinger, Time Series. Data Analysis and Theory. Holt, Rinehart and Winston, New York (1975). [Google Scholar]
  7. Yu. Davydov, The invariance principle for stationary processes. Theory Probab. Appl. 15 (1970) 487-489. [CrossRef] [MathSciNet] [Google Scholar]
  8. A. Demos, Moments and dynamic structure of a time-varying-parameter stochastic volatility in mean model. Preprint (2001). [Google Scholar]
  9. Z. Ding and C.W.J. Granger, Modeling volatility persistence of speculative returns: A new approach. J. Econometrics 73 (1996) 185-215. [CrossRef] [MathSciNet] [Google Scholar]
  10. R.F. Engle, Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica 50 (1982) 987-1008. [CrossRef] [MathSciNet] [Google Scholar]
  11. E. Ghysels, A.C. Harvey and E. Renault, Stochastic volatility, edited by G.S. Maddala and C.R. Rao. North Holland, Amsterdam, Handb. Statist. 14 (1993) 119-191. [Google Scholar]
  12. L. Giraitis, P. Kokoszka and R. Leipus, Rescaled variance and related tests for long memory in volatility and levels. Preprint (1999). [Google Scholar]
  13. L. Giraitis, H.L. Koul and D. Surgailis, Asymptotic normality of regression estimators with long memory errors. Statist. Probab. Lett. 29 (1996) 317-335. [CrossRef] [MathSciNet] [Google Scholar]
  14. L. Giraitis, R. Leipus, P.M. Robinson and D. Surgailis, LARCH, leverage and long memory. Preprint (2000). [Google Scholar]
  15. L. Giraitis, P.M. Robinson and D. Surgailis, A model for long memory conditional heteroskedasticity. Ann. Appl. Probab. (2000) (forthcoming). [Google Scholar]
  16. A. Harvey, Long memory in stochastic volatility, edited by J. Knight and S. Satchell, Forecasting Volatility in the Financial Markets. Butterworth & Heineman, Oxford (1998). [Google Scholar]
  17. C. He, T. Teräsvirta and H. Malmsten, Fourth moment structure of a family of first order exponential GARCH models, Preprint. Econometric Theory (to appear). [Google Scholar]
  18. H.-C. Ho and T. Hsing, Limit theorems for functionals of moving averages. Ann. Probab. 25 (1997) 1636-1669. [CrossRef] [MathSciNet] [Google Scholar]
  19. J.R.M. Hosking, Fractional differencing. Biometrika 68 (1981) 165-176. [CrossRef] [MathSciNet] [Google Scholar]
  20. H. Hurst, Long term storage capacity of reservoirs. Trans. Amer. Soc. Civil Engrg. 116 (1951) 770-799. [Google Scholar]
  21. D. Kwiatkowski, P.C. Phillips, P. Schmidt and Y. Shin, Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? J. Econometrics 54 (1992) 159-178. [Google Scholar]
  22. A.C. Lo, Long memory in stock market prices. Econometrica 59 (1991) 1279-1313. [CrossRef] [Google Scholar]
  23. I.N. Lobato and N.E. Savin, Real and spurious long-memory properties of stock market data (with comments). J. Business Econom. Statist. 16 (1998) 261-283. [CrossRef] [Google Scholar]
  24. V.A. Malyshev and R.A. Minlos, Gibbs Random Fields. Kluwer, Dordrecht (1991). [Google Scholar]
  25. B.B. Mandelbrot, Statistical methodology for non-periodic cycles: From the covariance to R/S analysis. Ann. Econom. Social Measurement 1 (1972) 259-290. [Google Scholar]
  26. B.B. Mandelbrot, Limit theorems of the self-normalized range for weakly and strongly dependent processes. Z. Wahrsch. Verw. Geb. 31 (1975) 271-285. [CrossRef] [Google Scholar]
  27. B.B. Mandelbrot and M.S. Taqqu, Robust R/S analysis of long run serial correlation. Bull. Int. Statist. Inst. 48 (1979) 59-104. [Google Scholar]
  28. B.B. Mandelbrot and J.M. Wallis, Robustness of the rescaled range R/S in the measurement of noncyclic long run statistical dependence. Water Resources Research 5 (1969) 967-988. [CrossRef] [Google Scholar]
  29. D.B. Nelson, Conditional heteroskedasticity in asset returns: A new approach. Econometrica 59 (1991) 347-370. [CrossRef] [MathSciNet] [Google Scholar]
  30. P.M. Robinson, Testing for strong serial correlation and dynamic conditional heteroskedasticity in multiple regression. J. Econometrics 47 (1991) 67-84. [CrossRef] [MathSciNet] [Google Scholar]
  31. P.M. Robinson, The memory of stochastic volatility models. J. Econometrics 101 (2001) 195-218. [CrossRef] [MathSciNet] [Google Scholar]
  32. P.M. Robinson and P. Zaffaroni, Nonlinear time series with long memory: A model for stochastic volatility. J. Statist. Plan. Inf. 68 (1998) 359-371. [CrossRef] [Google Scholar]
  33. S. Taylor, Modelling Financial Time Series. Wiley, Chichester (1986). [Google Scholar]

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