New methods to forecast volatility are usually compared to simple methods like weighted moving averages or GARCH (1, 1) models. In this paper, we provide new benchmark methods which are more accurate but still very simple. In an empirical study of daily returns on major world indices, our new...

Orthogonalised impulses are the standard way to isolate shocks to variables in a vector error correction model. While using the Cholesky decomposition to adjust interdependencies of the shocks, the ordering of the variables on the stage of estimation has a great impact on the resulting impulse...

This paper investigates the effect of spillover (i.e., causality in variance) on the Johansen tests for cointegration by conducting a Monte Carlo experiment where 16 different data generating processes (DGP) are used and a number of factors that might affect the properties of the Johansen...

This paper presents a rigorous test of statistical independence (as opposed to lack of correlation) between two random variables.

This paper examines the problem of order selection in connection to the forecasting performance for vector autoregressive (VAR) processes. For this purpose we present a generalisation of the modified divergence information criterion (MDIC) for VAR models and compare it with traditional...

A new method is introduced for panel-data models. Asymptotic robustness is used for a multivariate model with latent variables for a family of estimators. It is shown numerically that in comparison to standard methods we obtain: 1) better predictions in out-of-sample occasions; 2) smaller...

The deterministic extended-path (EP) method for solving dynamic stochastic optimisation problems approximates conditional expectations instead of approximating a model's complex non-linear dynamics. For a benchmark real business cycle model we show that this straightforward approach provides...