Motivated by economic-theory concepts - the Fisher hypothesis and the theory of the term structure - we consider a small set of simple bivariate closed-loop time-series models for the prediction of price inflation and of long- and short-term interest rates. The set includes vector...

The paper considers an elementary New-Keynesian three-equations model and contrasts its Bayesian estimation with the results from the method of moments (MM), which seeks to match the model-generated second moments of inflation, output and the interest rate to their empirical counterparts....

This paper analyses Czech and Hungarian index options that are traded on the Austrian Futures and Options Exchange. We find that the Poisson jump-diffusion and not the GARCH (1,1) process lends statistical support for the data description. We estimate that approximately four-fifth of 4 percent...

In most of the empirical research on capital markets, stock market indexes are used as proxies for the aggregate market development. In previous work we found that a particular market segment might be less efficient than the whole market and hence easier to forecast. In this paper we extend the...

The paper considers an elementary New-Keynesian three equation model and compares its Bayesian estimation to the results from the method of moments (MM), which seeks to match finite set of the model-generated second moments of inflation, output and the interest rate to their empirical...

Using data from Germany, Japan, UK, and the U.S., we explore possible threshold cointegration in nominal short- and long-run interest rates with corresponding inflation rates. Traditional cointegration implies perfect mean reversion in real rates and hence confirms the Fisher hypothesis....

Many macroeconometric models depict situations where the shares of the major demand aggregates in output are stable over time. The joint dynamic behavior of the considered demand aggregate and output may thus be approximated by a cointegrated vector autoregression. However, the shares of many...