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SEMIFAR Forecasts, with Applications to Foreign Exchange Rates

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    Abstract

    SEMIFAR models introduced in Beran (1999) provide a semiparametric modelling framework that enables the data analyst to separate deterministic and stochastic trends as well as short- and long-memory components in an observed time series. A correct distinction between these components, and in particular, the decision which of the components may be present in the data have an important impact on forecasts. in this paper, forecasts and forecast intervals for SEMIFAR models are obtained. The forecasts are based on an extrapolation of the stochastic component. In the data analystical part of the paper, the proposed method is applied to foreign exchange rates from Europe and Asia.

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    Bibliographic Info

    Paper provided by Center of Finance and Econometrics, University of Konstanz in its series CoFE Discussion Paper with number 99-13.

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    Length: 25 pages
    Date of creation: 02 Jun 1999
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    Handle: RePEc:knz:cofedp:9913

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    1. Peter C.B. Phillips, 1995. "Unit Root Tests," Cowles Foundation Discussion Papers 1104, Cowles Foundation for Research in Economics, Yale University.
    2. Liu, Christina Y & He, Jia, 1991. " A Variance-Ratio Test of Random Walks in Foreign Exchange Rates," Journal of Finance, American Finance Association, vol. 46(2), pages 773-85, June.
    3. Baillie, Richard T & Bollerslev, Tim, 1989. "The Message in Daily Exchange Rates: A Conditional-Variance Tale," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(3), pages 297-305, July.
    4. Jeffrey A. Frankel & Andrew K. Rose, 1994. "A Survey of Empirical Research on Nominal Exchange Rates," NBER Working Papers 4865, National Bureau of Economic Research, Inc.
    5. Meese, Richard A & Rose, Andrew K, 1991. "An Empirical Assessment of Non-linearities in Models of Exchange Rate Determination," Review of Economic Studies, Wiley Blackwell, vol. 58(3), pages 603-19, May.
    6. Hall, Peter & Hart, Jeffrey D., 1990. "Nonparametric regression with long-range dependence," Stochastic Processes and their Applications, Elsevier, vol. 36(2), pages 339-351, December.
    7. Chiu, Shean-Tsong, 1989. "Bandwidth selection for kernel estimate with correlated noise," Statistics & Probability Letters, Elsevier, vol. 8(4), pages 347-354, September.
    8. Meese, Richard A & Singleton, Kenneth J, 1982. " On Unit Roots and the Empirical Modeling of Exchange Rates," Journal of Finance, American Finance Association, vol. 37(4), pages 1029-35, September.
    9. Francis X. Diebold & James M. Nason, 1989. "Nonparametric exchange rate prediction?," Finance and Economics Discussion Series 81, Board of Governors of the Federal Reserve System (U.S.).
    10. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
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    Cited by:
    1. Fernandez, Viviana, 2008. "The war on terror and its impact on the long-term volatility of financial markets," International Review of Financial Analysis, Elsevier, vol. 17(1), pages 1-26.
    2. Mohamed Chikhi & Anne Péguin-Feissolle & Michel Terraza, 2013. "SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence," Computational Economics, Society for Computational Economics, vol. 41(2), pages 249-265, February.
    3. Jan Beran & Yuanhua.Feng, 2001. "Iterative plug-in algorithms for SEMIFAR models - definition, convergence and asymptotic properties," CoFE Discussion Paper 01-11, Center of Finance and Econometrics, University of Konstanz.
    4. Ana Pérez & Esther Ruiz, 2001. "Modelos De Memoria Larga Para Series Económicas Y Financieras," Documentos de Trabajo de Estadística y Econometría ds010101, Universidad Carlos III, Departamento de Estadística y Econometría.
    5. Beran, Jan & Feng, Yuanhua & Ghosh, Sucharita & Sibbertsen, Philipp, 2000. "On robust local polynominal estimation with long-memory errors," Technical Reports 2000,35, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    6. Chun-Hung Chen & Wei-Choun Yu & Eric Zivot, 2009. "Predicting Stock Volatility Using After-Hours Information," Working Papers UWEC-2009-01, University of Washington, Department of Economics.
    7. Dominique Guegan, 2003. "A prospective study of the k-factor Gegenbauer processes with heteroscedastic errors and an application to inflation rates," Post-Print halshs-00201314, HAL.
    8. Beran, Jan & Heiler, Mark A., 2008. "A nonparametric regression cross spectrum for multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 99(4), pages 684-714, April.
    9. Ghosh, Sucharita & Draghicescu, Dana, 2002. "Predicting the distribution function for long-memory processes," International Journal of Forecasting, Elsevier, vol. 18(2), pages 283-290.

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