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Nonparametric analysis of financial time series by the Kernel methodology

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  • Mohamed Chikhi

    ()

  • Claude Diebolt

    ()

Abstract

This paper aims to study, in the most recent historical time period, the efficiency of the Paris Stock Exchange market. We test its weak form while analysing the stock exchange returns series by nonparametric methods, using kernel methodology in particular. In doing so, our approach extends the traditional view treating the observed cyclical fluctuations on this market.

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File URL: http://hdl.handle.net/10.1007/s11135-009-9239-6
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Bibliographic Info

Article provided by Springer in its journal Quality & Quantity.

Volume (Year): 44 (2010)
Issue (Month): 5 (August)
Pages: 865-880

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Handle: RePEc:spr:qualqt:v:44:y:2010:i:5:p:865-880

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Web page: http://www.springer.com/economics/journal/11135

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Related research

Keywords: Efficiency; Random walk process; Kernel methodology; Functional autoregressive process; Forecasting; Cliometrics;

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References

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  1. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-72, June.
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  4. Fama, Eugene F, 1991. " Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-617, December.
  5. L. YANG & Wolfgang HÄRDLE, 1996. "Nonparametric Autoregression with Multiplicative Volatility and Additive Mean," SFB 373 Discussion Papers 1996,62, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  6. L. Yang & R. Tschernig, 1999. "Multivariate bandwidth selection for local linear regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(4), pages 793-815.
  7. Wolfgang HÄRDLE & H. LÜTKEPOHL & R. CHEN, 1996. "A Review of Nonparametric Time Series Analysis," SFB 373 Discussion Papers 1996,48, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  8. repec:wop:humbsf:1995-14 is not listed on IDEAS
  9. Tschernig, Rolf & Yang, Lijian, 1997. "Nonparametric lag selection for time series," SFB 373 Discussion Papers 1997,59, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  10. Wolfgang HÄRDLE & A. TSYBAKOV & L. YANG, 1996. "Nonparametric Vector Autoregression," SFB 373 Discussion Papers 1996,61, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  11. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
  12. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
  13. repec:wop:humbsf:1996-61 is not listed on IDEAS
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  15. Bruce Mizrach, 1995. "A Simple Nonparametric Test for Independence," Departmental Working Papers 199523, Rutgers University, Department of Economics.
  16. Wolfgang HÄRDLE & R. CHEN, 1995. "Nonparametric Time Series Analysis, a selectiv review with examples," SFB 373 Discussion Papers 1995,14, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  17. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-94, September.
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Cited by:
  1. I. Sánchez-Borrego & M. Rueda & J. Muñoz, 2012. "Nonparametric methods in sample surveys. Application to the estimation of cancer prevalence," Quality & Quantity: International Journal of Methodology, Springer, vol. 46(2), pages 405-414, February.

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