Abstract
This paper presents a preliminary comparison of forecasting performance for alternative non-linear methods using daily returns from the Italian Stock Market. In particular, some non-linear models and non-parametric techniques are considered. The accuracy of the forecast is evaluated using the sign prediction criteria, the mean square error and the mean absolute error.
The paper is partially supported by MURST98, “Modelli Statistici per l’Analisi delle serie temporali”
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Amendola, A., Giordano, F., Perna, C. (2001). Forecasting Non-Linear Time Series: Empirical Evidences on Financial Data(10) . In: Borra, S., Rocci, R., Vichi, M., Schader, M. (eds) Advances in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-59471-7_33
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DOI: https://doi.org/10.1007/978-3-642-59471-7_33
Publisher Name: Springer, Berlin, Heidelberg
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