We investigate the pererformance of socially responsible funds (SRFs) and conventional funds (CFs) in different market (geographical area and class\ud size) segments during the period 1992–2012. From an unbalanced sample of more than 22 000 funds, we define a matched sample using a betadistance measure to match any SRF with the ‘nearest neighbour’ CF in terms of sensitivity to risk factors. Using this matching approach and a recursive analysis, we identify several switch points in the lead/lag relationship between the two investment styles over time in different market segments. A relevant finding of our analysis is that SRFs played an ‘insurance role’ outperforming CFs during the 2007 global financial crisis

Using the Laplace transform approach, we compute the expected value and the variance of the error of a hedging strategy for a contingent claim when trading in discrete time. The method applies to a fairly general class of models, including Black-Scholes, Merton's jump-diffusion and Normal Inverse Gaussian, and to several interesting strategies, as the Black-Scholes delta, the Wilmott's improveddelta and the local optimal one. With this approach, also transaction costs may be treated. The results obtained are not asymptotical approximations but exact and efficient formulas, valid for any number of trading dates. They can also be employed under model mispecification, to measure the influence of model risk on a hedging strategy.

A simple non-stationary paradigm for the dynamics of multivariate returns is discussed.Unlike most of the multivariate econometric models for financial returns, our approach supposes the volatility to be exogenous and non-stationary. The vectors of returns are assumed to be animated by a slowly changing unconditional covariance structure. The methodological frame is that of non-parametric regression with fixed, equidistant design points. The regression function is the time evolving unconditional covariance. Special attention is payed to the accurate description of the extremal dependence of the vector of returns. The non-stationary paradigm is first applied to describe the changing dynamics of a multivariate data set of returns on three financial risk factors: a foreign exchange rate, an index and an interest rate. Then, its one-day-ahead multivariate distributional forecast performance is evaluated. We show through an out-of sample simulation experiment that our methodology is superior to the plain-vanilla specification of the industry standard RiskMetrics in forecasting the distribution of returns on portfolios of the three risk factors over horizons of one day, ten days and twenty days.JEL classification: C14, C16, C32.

We investigate the performance of Socially Responsible Funds (SRFs) and Conventional Funds (CFs) in different market segments during the 1992-2012 period. From an unbalanced sample of more that 22,000 funds, we define a matched sample using a beta-distance measure to match any SRF with the "nearest neighbor" CF in terms of risk factors. Using this novel matching approach and a recursive analysis, we identify several switch points in the lead/lag relationship between the two investment styles over time in different market segments (geographical area and size). A relevant finding of our analysis is that SRFs played an "insurance role" outperforming CFs during the 2007 global financial crisis.

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