In this paper, we shed light on the debate about the financial performance of socially responsible investment (SRI) mutual funds by separately analyzing the contributions of before-fee performance and fees to SRI funds' performance and by investigating the role played by fund management companies in the determination of those variables. We apply the matching estimator methodology to obtain our results and find that in the period 1997-2005, US SRI funds had significantly higher fees and better before-and after-fee performance than conventional funds with similar characteristics. Differences, however, were driven exclusively by SRI funds run by management companies specialized in socially responsible investment.

This article compares multivariate and univariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models to forecast portfolio value-at-risk (VaR). We provide a comprehensive look at the problem by considering realistic models and diversified portfolios containing a large number of assets, using both simulated and real data. Moreover, we rank the models by implementing statistical tests of comparative predictive ability. We conclude that multivariate models outperform their univariate counterparts on an out-of-sample basis. In particular, among the models considered in this article, the dynamic conditional correlation model with Student's t errors seems to be the most appropriate specification when implemented to estimate the VaR of the real portfolios analyzed. ( JEL: C22, C53, G17) KEYWORDS: backtesting, Basel Accords, market risk, composite likelihood, risk managementMarket risk management has been receiving increased attention in the past few years due to the importance devoted by the Basel II and Basel III Accords to the regulation of the financial system. These Accords explicitly recognize the role of value-at-risk (VaR) that financial institutions must implement and report in order A. A. P. S.

We assemble a massive sample of 180,000 CVs of Brazilian academic researchers of all disciplines from the Lattes platform. From the CVs we gather information on key variables related to the researchers and their publications. We find males are more productive in terms of quantity of publications, but the effect of gender in terms of research impact is mixed for individual groups of subject areas. Holding a PhD from abroad increases the chance for a researcher to publish in journals of higher impact, whereas domestic PhDs publish more articles, but in journals of less impact. Thus, there is a trade-off between quantity and research impact. We also find that the more years a researcher takes to finish his or her doctorate, the more likely he or she will publish less thereafter, although in outlets of higher impact. The data also support the existence of an inverted U-shaped function relating research age and productivity.Keywords: Lattes platform, scholarly publishing, scientific productivity, Brazilian researchers Acknowledgements. We thank the developers of the R (R Core Team 2015) packages that helped us to build the script used in this research: RSQLite, doParallel, DataTable, ggplot2, texreg, dplyr and xtable (Wickham et al

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We look into the interaction of Google's search queries and several aspects of international equity markets. Using a novel methodology for selecting words and a vector autoregressive modeling approach, we study whether the search queries of finance‐related words can have an impact on returns, volatility of returns and traded volume in four different English‐speaking countries. We identify several words whose search frequency is associated with changes in the dependent variables. In particular, we find that increases in search queries including the word stock predict increased volatility and decreased index returns over the next week. On top of that, we investigate the performance of a market‐timing strategy based on the search frequency of this word and benchmark it against random words from the Word‐Net database and a naive buy‐and‐hold strategy. The results of this empirical application are positive and particularly stronger during the global crisis of 2009. Copyright © 2016 John Wiley & Sons, Ltd.

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JEL classification: G11 G32Keywords: Convex optimization Multivariate GARCH Out-of-sample evaluation Stress testing a b s t r a c tWe propose a novel approach to active risk management based on the recent Basel II regulations to obtain optimal portfolios with minimum capital requirements. In order to avoid regulatory penalties due to an excessive number of Value at Risk (VaR) violations, capital requirements are minimized subject to a given number of violations over the previous trading year. Capital requirements are based on the recent Basel II amendments to account for the 'stressed' VaR, that is, the downside risk of the portfolio under extreme adverse market conditions. An empirical application for two portfolios involving different types of assets and alternative stress scenarios demonstrates that the proposed approach delivers an improved balance between capital requirement levels and the number of VaR exceedances. Furthermore, the risk adjusted performance of the proposed approach is superior to that of minimum VaR and minimum stressed VaR portfolios.

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