In this paper I describe a wavelet filtering approach to separate a time series, the signal, into its main components. With this approach I can separate stochastic from structural components. The statistical predictive analysis will be performed on the filtered signal while the stochastic term could be a-posteriori reintroduced through statistical simulation approaches (such as Markov Chain Monte Carlo). The proposed metodology has been applied to financial time series to predict both returns and risk.

Wavelet filtering for prediction in Time Series Analysis / Minerva, Tommaso. - STAMPA. - (2010), pp. 89-95.

Wavelet filtering for prediction in Time Series Analysis

MINERVA, Tommaso
2010

Abstract

In this paper I describe a wavelet filtering approach to separate a time series, the signal, into its main components. With this approach I can separate stochastic from structural components. The statistical predictive analysis will be performed on the filtered signal while the stochastic term could be a-posteriori reintroduced through statistical simulation approaches (such as Markov Chain Monte Carlo). The proposed metodology has been applied to financial time series to predict both returns and risk.
2010
89
95
Minerva, Tommaso
Wavelet filtering for prediction in Time Series Analysis / Minerva, Tommaso. - STAMPA. - (2010), pp. 89-95.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/642048
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