No abstract

In this paper we provide new evidence on the hypothesis of German leadership and asymmetric performance in the EMS, in the framework of causality tests, using daily data. Given the evidence about non-linearity in financial series, we propose applying non-linear forecasting methods based on the literature on complex dynamic systems. Our analysis covers nine countries, and the sample period runs until 30 April 1998, so including the more recent events in the EMS history. A comparison of our results with those obtained from standard linear econometric techniques leads us to conclude that inference on causality based on our non-linear predictors would be preferable to that based on the standard linear approach.

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a b s t r a c tWe measure the connectedness in EMU sovereign market volatility between April 1999 and January 2014, monitoring stress transmission and identifing episodes of intensive spillovers from one country to the others. We first perform a static and dynamic analysis to measure the total volatility connectedness in the entire period using a framework recently proposed by Diebold and Yilmaz (2014). Second, we use a dynamic analysis to evaluate the net directional connectedness for each country and apply panel model techniques to investigate its determinants. Finally, we examine the time-varying behaviour of net pair-wise directional connectedness at different stages of the recent sovereign debt crisis.

No abstract

AbstractsWe analyse volatility spillovers in EMU sovereign bond markets. First, we examine the unconditional patterns during the full sample (April 1999-January 2014) using a measure recently proposed by Diebold and Yılmaz (2012). Second, we make use of a dynamic analysis to evaluate net directional volatility spillovers for each of the eleven countries under study, and to determine whether core and peripheral markets present differences. Finally, we apply a panel analysis to empirically investigate the determinants of net directional spillovers of this kind.

Growth, Genetic algorithms, Data mining, Regressors selection, C63, O47,

We present a system for combining the different types of predictions given by a wide category of mechanical trading rules through statistical learning methods (boosting, and several model averaging methods like Bayesian or simple averaging methods). Statistical learning methods supply better out-of-sample results than most of the single moving average rules in the NYSE Composite Index from January 1993 to December 2002. Moreover, using a filter to reduce trading frequency, the filtered boosting model produces a technical strategy which, although it is not able to overcome the returns of the buy-and-hold (B&H) strategy during rising periods, it does overcome the B&H during falling periods and is able to absorb a considerable part of falls in the market. Copyright © 2008 John Wiley & Sons, Ltd.