Direction-of-Change Forecasting using a Volatility- Based Recurrent Neural Network

This paper investigates the profitability of a trading strategy, based on recurrent neural networks, that attempts to predict the direction-of-change of the market in the case of the NASDAQ composite index. The sample extends over the period 2/8/1971 \u2013 4/7/1998, while the sub-period 4/8/1998 - 2/5/2002 has been reserved for out-of-sample testing purposes. We demonstrate that the incorporation in the trading rule of estimates of the conditional volatility changes strongly enhances its profitability during `bear' market periods. This improvement is being measured with respect to a nested model that does not include the volatility variable as well as to a buy & hold strategy. We suggest that our findings can be justified by invoking either the `volatility feedback' theory or the existence of portfolio insurance schemes in the equity markets. Our results are also consistent with the view that volatility dependence produces sign dependence.

Business Cycle Synchronization of the Visegrad Four and the European Union

In this paper, we map the process of synchronization of the Visegrad Four within the framework of the European Union using the wavelet techniques. In addition, we show that the relationship of output and key macroeconomic indicators is dynamic and varies over time and across frequencies. We study the synchronization applying the wavelet cohesion measure with time-varying weights. This novel approach allows for studying the dynamic relationship among countries from a different perspective than usual timedomain models. Analysing monthly data from 1990 to 2014, the results for the Visegrad region show an increasing co-movement with the European Union after the countries began with preparation for the accession to the European union. The participation in a currency union possibly increases the co-movement. Further, analysing the Visegrad and South European countries' synchronization with the European Union core countries, we find a high degree of synchronization in long-term horizons

Estimating the correlation of international equity markets with multivariate extreme and GARCH, CeNDEF Working paper 06-17

Abstract In this paper we study the dependence structure of extreme realization of returns between seven Asian Pasific stock markets and the USA. Methodologically, we apply the Multivariate Extreme Value theory that best suits to the problem under investigation. The main advantage of this approach is that it generates dependence measures even if the multivariate Gaussian distribution does not apply, as the case is for the tails of the high frequency stock index returns distributions. The empirical evidence suggests that conventional Constant Conditional Correlation GARCH models (Bollerslev, 1990) produce very similar results not just quantitatively but qualitatively so, as a clustering analysis showed. Dynamic Conditional Correlation GARCH models JEL Classification: G15; C10; F30