Multivariate Gram-Charlier Densities

This paper introduces a new family of multivariate distributions based on Gram-Charlier and Edgeworth expansions. This family encompasses many of the univariate seminonparametric densities proposed in the financial econometrics as marginal distributions of the different formulations. Within this family, we focus on the specifications that guarantee positivity so obtaining a well-defined multivariate density. We compare different "positive" multivariate distributions of the family with the multivariate Edgeworth-Sargan, Normal and Student’s t in an in- and out-sample framework for financial returns data. Our results show that the proposed specifications provide a quite reasonably good performance being so of interest for applications involving the modelling and forecasting of heavy-tailed distributions.Multivariate distributions; Gram-Charlier and Edgeworth-Sargan densities; MGARCH models; financial data

Legal and Cultural Factors as Catalysts for Promoting Women in the Boardroom

12 p.This study focuses on whether regulation as well as national cultures play significant roles in defining women’s role in society. We are contributing to the existing debate by providing the first empirical analysis to calibrate which legal mechanisms and cultural dimensions are more efficient in achieving boardroom gender equality. We have highlighted the impact of regulation by distinguishing between those countries that have passed positive laws imposing gender quotas in the boardroom and those applying the ‘comply or explain’ recommendation in their good governance codes. We have monitored enforcement levels among countries and tested the validity of Hofstede’s cultural factors in impacting on gender quotas. The emerging picture is that of gender diversity being triggered by the adoption of positive laws rather than by soft recommendations. Moreover, gender diversity policies are more commonly promoted in countries where governments, corporations and institutions are characterized by less masculinity and lower power distance.S

Multivariate Gram-Charlier Densities

This paper introduces a new family of multivariate distributions based on Gram-Charlier and Edgeworth expansions. This family encompasses many of the univariate seminonparametric densities proposed in the financial econometrics as marginal distributions of the different formulations. Within this family, we focus on the specifications that guarantee positivity so obtaining a well-defined multivariate density. We compare different "positive" multivariate distributions of the family with the multivariate Edgeworth-Sargan, Normal and Student’s t in an in- and out-sample framework for financial returns data. Our results show that the proposed specifications provide a quite reasonably good performance being so of interest for applications involving the modelling and forecasting of heavy-tailed distributions

Measuring the Impact of Corporate Investment Announcements on Share Prices: The Spanish Experience

We bring together three disparate strands of literature to develop a comprehensive empirical framework to examine the efficiency of security analysts' earnings forecasts in Singapore. We focus specifically on how the increased uncertainty and the negative market sentiment during the period of the Asian crisis affected the quality of earnings forecasts. While we find no evidence of inefficiencies in the pre-crisis period, our results suggest that after the onset of the crisis, analysts (1) issued forecasts that were systematically upward biased; (2) did not fully incorporate the (negative) earnings-related news; and (3) predicted earnings changes which proved too extreme. Copyright Blackwell Publishers Ltd, 2003.

VaR performance during the subprime and sovereign debt crises: An application to emerging markets

Highly volatile scenarios, such as those provoked by the recent subprime and sovereign debt crises, have questioned the accuracy of current risk forecasting methods. This paper adds fuel to this debate by comparing the performance of alternative specifications for modeling the returns filtered by an ARMA-GARCH: Parametric distributions (Student's t and skewed-t), the extreme value theory (EVT), semi-nonparametric methods based on the Gram–Charlier (GC) expansion and the normal (benchmark). We implement backtesting techniques for the pre-crisis and crisis periods for stock index returns and a hedge fund of emerging markets. Our results show that the Student's t fails to forecast VaR during the crisis, while the EVT and GC accurately capture market risk, the latter representing important savings in terms of efficient regulatory capital provisions