Working Paper Series
This paper proposes a new method to empirically validate simulation models that generate artificial time series data comparable with real-world data.
An investigation into Multivariate Variance Ratio Statistics and their application to Stock Market Predictability
We propose several multivariate variance ratio statistics. We derive the asymptotic distribution of the statistics and scalar functions thereof under the null hypothesis that returns are unpredictable after a constant mean adjustment (i.e., under the weak form Efficient Market Hypothesis). We do not impose the no leverage assumption of Lo and MacKinlay (1988) but our asymptotic standard errors are relatively simple and in particular do not require the selection of a bandwidth parameter. We extend the framework to allow for a time varying risk premium through common systematic factors.
We investigate the effects of government spending on U.S. economic activity using a threshold version of a structural vector autoregressive model.