Modelling and Forecasting financial time series of "tick data" by functional analysis and neural networks
(2007) Forecasting Financial Markets 2007 — Location: Aix-en-Provence (France) (30.May.2007)
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- Abstract
- The analysis of financial time series is of primary importance in the economic world. This paper deals with a data-driven empirical analysis of financial time series. The goal is to obtain insights into the dynamics of series and out-of-sample forecasting. In this paper we present a forecasting method based on an empirical functional analysis of the past of series. An originality of this method is that it does not make the assumption that a single model is able to capture the dynamics of the whole series. On the contrary, it splits the past of the series into clusters, and generates a specific local neural model for each of them. The local models are then combined in a probabilistic way, according to the distribution of the series in the past. This forecasting method can be applied to any time series forecasting problem, but is particularly suited for data showing nonlinear dependencies, cluster effects and observed at irregularly and randomly spaced times like high-frequency financial time series do. One way to overcome the irregular and random sampling of "tick-data" is to resample them at low-frequency, as it is done with "Intraday". However, even with optimal resampling using say five minute returns when transactions are recorded every second, a vast amount of data is discarded, in contradiction to basic statistical principles. Thus modelling the noise and using all the data is a better solution, even if one misspecifies the noise distribution. The method is applied to the forecasting of financial time series of «tick data» of assets on a short horizon in order to be useful for speculators.
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Dablemont, S., Van Bellegem, S., & Verleysen, M. (2007). Modelling and Forecasting financial time series of “tick data” by functional analysis and neural networks. Proceedings of Forecasting Financial Markets 2007, p. 1-18. https://hdl.handle.net/2078.5/254136