This article introduces and applies two refinements to the algorithm of solving rational expectations models of a currency union. Firstly, building upon Klein (2000), it generalizes the standard methods of solving rational expectations models to the case of time-varying nonstochastic parameters, recurring in a finite cycle. Such a specification occurs in a simple stylized New Keynesian model of the euro area after a joint introduction of (i) rotation in the ECB Governing Council (as constituted by the Treaty of Nice) and (ii) home bias in the interest rate decisions preferred by its members. Secondly, we apply the method of Christiano (2002) to solve the model with heterogenous information sets. This is justified if we argue that the information set of domestic economic agents in a currency union is home-biased (i.e. foreign shocks enter only with a lag). Both methods of solution are illustrated with simulation results.
Volatility persistence is a stylized statistical property of financial time-series data such as exchange rates and stock returns. The purpose of this letter is to investigate the relationship between volatility persistence and predictability of squared returns.
This paper studies flows on the labour market in Poland in 1995‒2008. We show that the main driving force behind the unemployment rate is the behaviour of outflow to employment. In addition, this flow is found to be procyclical, while the separation rate is acyclical.
This paper develops a new model of market abuse detection in real time. Market abuse is detected, as Minenna (2003) proposed, on the basis of prediction intervals. The model structure is based on the discrete-time, extended market model introduced by Monteiro, Zaman, Leitterstorf (2007) to analyze the market cleanliness. Parameters of the expected return equation are assumed, however, to be time-varying and estimated under the state-space framework using the extended Kalman filter postulated by Chou, Engle, Kane (1992) to capture the GARCH effect in returns. QML estimation is performed on intraday data; its utilization is proposed as an alternative to the continuous time modeling by Minenna (2003). This framework is generalized to the bivariate case which enables the analysis of daily open/close data. The paper also extends procedures of the statistical verification of the estimated state-space model to include the uncertainty arising from time-invariant parameters.