Band 21, Heft 3

The paper aims at developing new Bayesian Vector Error Correction – Stochastic Volatility (VEC-SV) models, which combine the VEC representation of a VAR structure with stochastic volatility, represented by either the multiplicative stochastic factor (MSF) process or the MSF-SBEKK specification. Appropriate numerical methods (MCMC-based algorithms) are adapted for estimation and comparison of these type of models. Based on data coming from the Polish economy (time series of unemployment, inflation, interest rates, and of PLN/EUR, PLN/USD and EUR/USD exchange rates) it is shown that the models and numerical methods proposed in our study work well in simultaneous modelling of volatility and long-run relationships.

In this study, we propose a new method to find convergence clubs that combine pairwise method of testing convergence with maximal clique algorithm. Unlike many of those already developed in the literature, this new method aims to find convergence clubs endogenously without depending on priori classifications. We use our method to study convergence among different capital markets as captured by their respective indices. Stock market convergence would indicate the absence of arbitrage opportunities in moving between the different markets as they would all present investors with similar risks. Furthermore, stock market convergence would be a precursor to GDP convergence as these economies would be bound by similar (possibly unobservable) common factors that affect long run macroeconomic performance.

Declining inflation persistence has been documented in numerous studies. We show that when time series with changes in persistence are analyzed in a regression framework with other persistent time series like interest rates, spurious regressions are likely to occur. We propose the coefficient of determination R 2 as a simple test statistic to distinguish between spurious and genuine regressions in situations where time series possibly exhibit changes in persistence. We extend the analysis towards fractional (co-)integration as well. To this end, we establish the limit theory for the R 2 statistic and conduct a Monte Carlo study where we investigate its finite-sample properties. The test performs remarkably well in terms of size and power and is robust to level shifts and multiple changes in persistence. Finally, we apply the test to the Fisher equation for the United States. The newly proposed R 2 -based test offers robust evidence favourable to the Fisher hypothesis.

Abstract: We examine the relationship between money supply growth and inflation in 3 Asian Economies which are India, Malaysia and Japan using a time-frequency approach. The application of a unified multi-scale analysis allows us to provide a continuous assessment of the link between money supply growth and inflation, unlike most of the existing literature studying this relationship. We also employ a bivariate frequency-domain causality test to determine the nature and direction of interdependence between money supply growth and inflation dynamics. Our findings provide a better understanding of their lead-lag linkages and causal relationship in the selected countries of the Asia-Pacific region.

This work studies wavelet-based Whittle estimator of the fractionally integrated exponential generalized autoregressive conditional heteroscedasticity (FIEGARCH) model often used for modeling long memory in volatility of financial assets. The newly proposed estimator approximates the spectral density using wavelet transform, which makes it more robust to certain types of irregularities in data. Based on an extensive Monte Carlo study, both behavior of the proposed estimator and its relative performance with respect to traditional estimators are assessed. In addition, we study properties of the estimators in presence of jumps, which brings interesting discussion. We find that wavelet-based estimator may become an attractive robust and fast alternative to the traditional methods of estimation. In particular, a localized version of our estimator becomes attractive in small samples.