In the public debate, it is argued that Poland avoided a massive drop in output during the 2008/2009 economic crisis in part thanks to substantial nominal zloty’s depreciation against the euro. The Polish case is often contrasted with Slovakia that adopted the euro in January 2009 and, since the Ecofin Council decision in summer 2008, exhibited virtually no nominal exchange rate volatility while facing deep losses in output. In this paper we attempt to validate this contrast by reversing the roles, i.e. checking if Poland really would have faced the same drop – and Slovakia would have remained relatively resilient – if it had been Poland, not Slovakia, that adopted the euro at that point. Our counterfactual simulations based on a New Keynesian DSGE model indicate that, indeed, the Polish tradable output could have been 10‒15 percent lower than actually observed in 2009, while the Slovak one – approximately 20 percent higher. This asymmetry results mainly from structural differences between the two economies, such as size, openness, share of nontradable sector and foreign trade elasticities. The difference of this size would have been short-lived (3‒4 quarters), and the difference of the nontradable output would have been of much lower magnitude.
This paper describes a forecasting exercise of close-to-open returns on major global stock indices, based on high-frequency price patterns that have become available in foreign markets overnight. Generally speaking, out-of-sample forecast performance depends on the forecast method as well as the information that the forecasts are based on. In this paper both aspects are considered. The fact that the close-to-open gap is a scalar response variable to a functional variable, namely an overnight foreign price pattern, brings the prediction exercise in the realm of functional data analysis. Both parametric and non-parametric functional data analysis are considered, and compared with a simple linear benchmark model. The information set is varied by dividing global markets into three clusters, Asia-Pacific, Europe and North-America, and including or excluding price patterns on a per-cluster basis. The overall best performing forecast is nonparametric using all available information, suggesting the presence of nonlinear relations between the overnight price patterns and the opening gaps.
High movements of asset prices constitute intrinsic elements of financial crises. There is a common agreement that extreme events are responsible for that. Making inference about the risk spillover and its effect on markets one should use such methods and tools that can fit properly for catastrophic events. In the paper Extreme Value Theory (EVT) invented particularly for modelling extreme events was used. The purpose of the paper is to model risky assets using EVT and to analyse the transfer of risk across the financial markets all over the world using the Granger causality in risk test. The concept of testing in causality in risk was extended to Spectral Risk Measure i.e., respective hypotheses were constructed and checked by simulation. The attention is concentrated on the Chinese financial processes and their relations with those in the rest of the globe. The original idea of the Granger causality in risk assumes usage of Value at Risk as a risk measure. We extended the scope of application of the test to Expected Shortfall and Spectral Risk Measure. The empirical results exhibit very interesting dependencies.
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