Financial time series modelling: return on assets

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.

Modelling Financial Time Series with S-PLUS

Modelling Financial Time series using Grammatical Evolution

The traditional models of price, and its statistical signatures are often based on limiting assumptions, such as linearity. Moreover, the model developer is faced with the model selection problem, and model uncertainty. In this paper we introduce a method based on Grammatical Evolution (GE) to evolve models for predicting financial returns, and we examine the profitability of these models. Our ...

The Econometric Modelling of Financial Time Series Second edition

Modelling financial time series with switching state space models

The deeciencies of stationary models applied to nancial time series are well documented. A special form of non-stationarity, where the underlying generator switches between (approximately) stationary regimes, seems particularly appropriate for nancial markets. We use a dynamic switching (modelled by a hidden Markov model) combined with a linear dynamical system in a hybrid switching state space...