The Impact of Information Signals on Market Prices when Agents have Non-linear Trading Rules

Several methods have been developed for filtering seasonal influences and extreme returns in financial and economic time series. The theoretical support for these approaches is rather questionable since it focuses on the effects of shocks on prices and not on their sources. Removing such effects modifies the true generating system of market dynamics because of the non-proportional character of non-linearity. Thus, taking into account that the underlying process of economic time series is highly non-linear we cannot be certain a priori what the impact of new information will be on the dynamic structure of a system. The main contribution of this paper is to demonstrate using the methodology of simulations the eventual distortions in time series data arising from the arrival of news when agents follow non-linear trading strategies. We argue that if news can really modify the dynamical behaviour of a system, then the methodology of filtering exogenous distortions needs to be re-examined

Can urban coffee consumption help predict US inflation?

Motivated by the importance of coffee to Americans and the significance of the coffee subsector to the US economy, we pursue three notable innovations. First, we augment the traditional Phillips curve model with the coffee price as a predictor, and show that the resulting model outperforms the traditional variant in both in-sample and out-of-sample predictability of US inflation. Second, we demonstrate the need to account for the inherent statistical features of predictors such as persistence, endogeneity, and conditional heteroskedasticity effects when dealing with US inflation. Consequently, we offer robust illustrations to show that the choice of estimator matters for improved US inflation forecasts. Third, the proposed augmented Phillips curve also outperforms time series models such as autoregressive integrated moving average and the fractionally integrated version for both in-sample and out-of-sample forecasts. Our results show that augmenting the traditional Phillips curve with the urban coffee price will produce better forecast results for US inflation only when the statistical effects are captured in the estimation process. Our results are robust to alternative measures of inflation, different data frequencies, higher order moments, multiple data samples and multiple forecast horizons

Evidence for Nonlinear Asymmetric Causality in US Inflation, Metal, and Stock Returns

The purpose of this paper is to propose a version of causality testing that focuses on how the sign of the returns affects the causality results. We replace the traditional VAR specification used in the Granger causality test by a discrete-time bivariate noisy Mackey glass model. Our test reveals interesting and previously unexplored relationships in US economic series, including inflation, metal, and stock returns

The effects of terrorism and war on the oil price-stock index relationship

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 and oil returns is affected by war. A tentative explanation is that the two wars examined here predispose investors and market agents for more profound and longer lasting effects on global markets. On the other hand, terrorist incidents that are one-off unanticipated security shocks, only the co-movement between CAC40, DAX and oil returns is affected and no significant impact is observed in the relationship between the S&P500, FTSE100 and oil returns. This difference in the reaction may tentatively be interpreted as indicating that the latter are more efficient in absorbing the impact of terrorist attacks. (C) 2013 Elsevier B.V. All rights reserved