The aim of this article is the study of complex structures which are behind the short-term predictability of stock returns series. In this regard, we employ a seasonal version of the Mackey-Glass-GARCH(p,q) model, initially proposed by Kyrtsou and Terraza (2003) and generalized by Kyrtsou (2005,...

The nonlinear testing and modeling of economic and financial time series has increased substantially in recent years, enabling us to better understand market and price behavior, risk and the formation of expectations. Such tests have also been applied to commodity market behavior, providing...

Most recent empirical works that apply sophisticated statistical proceduressuch as a correlation-dimension method have shown that stock returns arehighly complex. The estimated correlation dimension is high and there islittle evidence of low-dimensional deterministic chaos. Taking the...

An increasing number of recent articles applying powerful tests for non-linear causality have fuelled interest in discovering complex dynamics in macroeconomic and financial data. This paper is an attempt to add to the set of available tools by proposing a test for determining the source of...

The main objective of this paper is to employ a new dynamic model that combines the bivariate noisy Mackey–Glass recently proposed by Kyrtsou and Labys [Kyrtsou, C., Labys, W., 2006. Evi- dence for chaotic dependence between US inflation and commodity prices. Journal of Macroeco- nomics...

The effects war and terrorism have on the covariance between oil prices and the indices of four major stock markets – the American S&P500, the European DAX, CAC40 and FTSE100 – using non-linear BEKK–GARCH type models are investigated. The findings indicate that the covariance between stock...

In this paper, we analyze the rich dynamic properties of the noisy chaotic model developed by Kyrtsou [C. Kyrtsou, Evidence for neglected linearity in noisy chaotic models, International Journal of Bifurcation and Chaos 15 (10) (2005)] considering homoskedastic errors, with the aim of deriving...