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An Out-of-Sample Test for Nonlinearity in Financial Time Series: An Empirical Application

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  • Theodore Panagiotidis

    ()
    ( Department Of Economics, University Of Macedonia, Thessaloniki, Greece; The Rimini Centre for Economic Analysis (RCEA), Italy)

Abstract

This paper employs a local information, nearest neighbour forecasting methodology to test for evidence of nonlinearity in financial time series. Evidence from well-known data generating process are provided and compared with returns from the Athens stock exchange given the in-sample evidence of nonlinear dynamics that has appeared in the literature. Nearest neighbour forecasts fail to produce more accurate forecasts from a simple AR model. This does not substantiate the presence of in-sample nonlinearity in the series.

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File URL: http://www.rcfea.org/RePEc/pdf/wp20_10.pdf
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Bibliographic Info

Paper provided by The Rimini Centre for Economic Analysis in its series Working Paper Series with number 20_10.

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Date of creation: Jan 2010
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Handle: RePEc:rim:rimwps:20_10

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Keywords: nearest neighbour; nonlinearity;

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References

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  1. Theodore Panagiotidis, 2010. "Market efficiency and the Euro: the case of the Athens stock exchange," Empirica, Springer, vol. 37(3), pages 237-251, July.
  2. Jaditz, Ted & Sayers, Chera L, 1998. "Out-of-Sample Forecast Performance as a Test for Nonlinearity in Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 110-17, January.
  3. Agnon, Yehuda & Golan, Amos & Shearer, Matthew, 1999. "Nonparametric, nonlinear, short-term forecasting: theory and evidence for nonlinearities in the commodity markets," Economics Letters, Elsevier, vol. 65(3), pages 293-299, December.
  4. repec:att:wimass:9105 is not listed on IDEAS
  5. Golan, Amos & Perloff, Jeffrey M, 2002. "Superior forecasts of the U.S. unemployment rate using a nonparametric method," CUDARE Working Paper Series 956, University of California at Berkeley, Department of Agricultural and Resource Economics and Policy.
  6. Jaditz Ted & Riddick Leigh A., 2000. "Time-Series Near-Neighbor Regression," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 4(1), pages 1-11, April.
  7. 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.
  8. Fernandez-Rodriguez, Fernando & Sosvilla-Rivero, Simon, 1998. "Testing nonlinear forecastability in time series: Theory and evidence from the EMS," Economics Letters, Elsevier, vol. 59(1), pages 49-63, April.
  9. LeBaron, Blake, 1992. "Forecast Improvements Using a Volatility Index," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S137-49, Suppl. De.
  10. Mizrach, B, 1992. "Multivariate Nearest-Neighbor Forecasts of EMS Exchange Rates," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S151-63, Suppl. De.
  11. David Chappell & Theodore Panagiotidis, 2005. "Using the correlation dimension to detect non-linear dynamics: Evidence from the Athens Stock Exchange," Econometrics 0504005, EconWPA.
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Cited by:
  1. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2013. "Some thoughts on accurate characterization of stock market indexes trends in conditions of nonlinear capital flows during electronic trading at stock exchanges in global capital markets," MPRA Paper 49921, University Library of Munich, Germany.

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