Flexible multivariate GARCH modeling with an application to international stock markets

This paper offers a new approach to estimating time-varying covariance matrices in the framework of the diagonal-vech version of the multivariate GARCH(1,1) model. Our method is numerically feasible for large-scale problems, produces positive semidefinite conditional covariance matrices, and does not impose unrealistic a priori restrictions. We provide an empirical application in the context of international stock markets, comparing the nev^ estimator with a number of existing ones.

Health systems efficiency after the crisis in the OECD

This work evaluates the efficiency position of the health system of each OECD country. It identifies whether, or not, health systems changed in terms of quality and performance after the financial crisis. The health systems performance was calculated by fixed-effects estimator and by stochastic frontier analysis. The results suggest that many of those countries that the crisis affected the most are more efficient than the OECD average. In addition, some of those countries even managed to reach the top decile in the efficiency ranking. Finally, we analyze the stochastic frontier efficiency scores together with other health indicators to evaluate the health systems’ overall adjustments derived from the crisis.

Financial depth, stock markets and economic growth in the EU-15 countries

The aim of this paper is to assess the impact of financial depth on economic growth in the EU-15 countries from 1970 until 2012, using the two-step System GMM estimator. Even though it might be expected a positive impact, the results show it is negative and sometimes even negative and statistically significant. Among the reasons presented for this, the existence of banking crises seems to better explain these results. In tranquil periods, financial deepening appears to have a positive impact, whereas in banking crises it is persistently negative and statistically significant. Also, after an assessment of the impact of stock markets on economic growth, it appears that more developed countries in the EU-15 have an economy more reliant on this segment of the financial system rather than in bank intermediation.

Orthogonal models, structure crossing, nesting and inference

We intend to study the algebraic structure of the simple orthogonal models to use them, through binary operations as building blocks in the construction of more complex orthogonal models. We start by presenting some matrix results considering Commutative Jordan Algebras of symmetric matrices, CJAs. Next, we use these results to study the algebraic structure of orthogonal models, obtained by crossing and nesting simpler ones. Then, we study the normal models with OBS, which can also be orthogonal models. We intend to study normal models with OBS (Orthogonal Block Structure), NOBS (Normal Orthogonal Block Structure), obtaining condition for having complete and suffcient statistics, having UMVUE, is unbiased estimators with minimal covariance matrices whatever the variance components. Lastly, see ([Pereira et al. (2014)]), we study the algebraic structure of orthogonal models, mixed models whose variance covariance matrices are all positive semi definite, linear combinations of known orthogonal pairwise orthogonal projection matrices, OPOPM, and whose least square estimators, LSE, of estimable vectors are best linear unbiased estimator, BLUE, whatever the variance components, so they are uniformly BLUE, UBLUE. From the results of the algebraic structure we will get explicit expressions for the LSE of these models.