This paper investigates how the ordering of variables affects properties of the time-varying covariance matrix in the Cholesky multivariate stochastic volatility model.It establishes that systematically different dynamic restrictions are imposed whenthe ratio of volatilities is time-varying....

This empirical analysis deals with the determinants of growth and the explanation of variations in the growth between innovative and non-innovative start-ups. Based on theoretical models explaining the growth of firms, hypotheses on potential determinants are formulated. The regression results...

We derive multivariate risk-neutral asset distributions for major US financial institutions (FIs) using option implied marginal risk-neutral asset distributions (RNDs) and probabilities of default (PoDs). The multivariate densities are estimated by combining the entropy approach, dynamic copulas...

This paper describes the inference procedures required to perform Bayesian inference to some multivariate econometric models. These models have a spatial component built into commonly used multivariate models. In particular, the seemingly unrelated regression and vector autoregressive models are...

We develop a new Bayesian estimator that is able to deal with multivariate panel data structure in the presence of spatial correlation. The analysis of panel data introduced here allows us to analyze not only the fixed effect but also the random effect model. This work extends the previous study...

This paper investigates the ability of several generalized Bayesian vector autoregressions to cope with the extreme COVID-19 observations and discusses their impact on prior calibration for inference and forecasting purposes. It shows that the preferred model interprets the pandemic episode as a...