Time scale and fractionality in financial time series

Machine learning algorithms for time series in financial markets

This research is related to the usefulness of different machine learning methods in forecasting time series on financial markets. The main issue in this field is that economic managers and scientific society are still longing for more accurate forecasting algorithms. Fulfilling this request leads to an increase in forecasting quality and, therefore, more profitability and efficiency. In this pa...

Determinism in Financial Time Series

The attractive possibility that financial indices may be chaotic has been the subject of much study. In this paper we address two specific questions: “Masked by stochasticity, do financial data exhibit deterministic nonlinearity?”, and “If so, so what?”. We examine daily returns from three financial indicators: the Dow Jones Industrial Average, the London gold fixings, and the USD-JPY exchange ...

Characterization of Financial Time Series

This paper provides an exhaustive review of the literature on the characterization of financial time series. A stylized fact is a term in economics used to refer to empirical findings that are so consistent across markets that they are accepted as truth. Financial time series may be characterized by the following stylized facts. The autocorrelation of returns is largely insignificant. The distr...

Modelling and Prediction of Financial Time Series

We consider statistical aspects of the modelling and prediction theory of time series in one and many dimensions. We discuss Lévy-based and general models, and the stationary and non-stationary cases. Our starting point is the recent pair of surveys, Szegö’s theorem and its probabilistic descendants and Multivariate prediction and matrix Szegö theory, by this author.

Financial Time Series and Their Characterictics