Runs tests for assessing volatility forecastability in financial time series
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References listed on IDEAS
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- Hua, Zhongsheng & Zhang, Bin, 2008. "Improving density forecast by modeling asymmetric features: An application to S&P500 returns," European Journal of Operational Research, Elsevier, vol. 185(2), pages 716-725, March.
- Sévi, Benoît, 2014.
"Forecasting the volatility of crude oil futures using intraday data,"
European Journal of Operational Research, Elsevier, vol. 235(3), pages 643-659.
- Benoît Sévi, 2014. "Forecasting the volatility of crude oil futures using intraday data," Post-Print hal-01463921, HAL.
- Benoît Sévi, 2014. "Forecasting the volatility of crude oil futures using intraday data," Working Papers 2014-53, Department of Research, Ipag Business School.
- Ma, T. & Fraser-Mackenzie, P.A.F. & Sung, M. & Kansara, A.P. & Johnson, J.E.V., 2022. "Are the least successful traders those most likely to exit the market? A survival analysis contribution to the efficient market debate," European Journal of Operational Research, Elsevier, vol. 299(1), pages 330-345.
- Doyle, John R. & Chen, Catherine H., 2013. "Patterns in stock market movements tested as random number generators," European Journal of Operational Research, Elsevier, vol. 227(1), pages 122-132.
- repec:ipg:wpaper:2014-053 is not listed on IDEAS
- Leandro Maciel & Fernando Gomide & Rosangela Ballini, 2014. "An Evolving Fuzzy-Garch Approach Forfinancial Volatility Modeling And Forecasting," Anais do XL Encontro Nacional de Economia [Proceedings of the 40th Brazilian Economics Meeting] 138, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
- Lin, Yao-San & Li, Der-Chiang, 2010. "The Generalized-Trend-Diffusion modeling algorithm for small data sets in the early stages of manufacturing systems," European Journal of Operational Research, Elsevier, vol. 207(1), pages 121-130, November.
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