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In this paper, we investigate the relationship between common risk factors and average returns for Italian stocks. Our research has identi®ed the Italian stock market's economic variables by using the results from factor analyses and time series regressions.We study several multi-factor models combining the relevant macroeconomic variables with the mimicking equity portfolios SMB (small minus big) and HML (high minus low) proposed by Fama and French (1993). The key question we want to ask ourselves, is whether the in¯uential role of the size and book-to-market equity factors in explaining average stock returns can stand up well when competing with some macroeconomic factors. In other words, do stock returns carry some risk premium that is independent of either the market return or the economic forces that underlie the common variation in returns?Our empirical work estimates risk premiums using both traditional two-pass procedures and one-pass (full information) methodologies. We show that only the market index and variables linked to interest rate shifts are consistently priced in the Italian stock returns. The role of other factors, and in particular both the size and the price-to book ratio, are crucially dependent on the estimation procedure.(J.E.L.: G11, G12).

We address the problem of modelling and verifying contract-oriented systems, wherein distributed agents may advertise and stipulate contracts, but — differently from most other approaches to distributed agents — are not assumed to always respect them. A key issue is that the honesty property, which characterises those agents which respect their contracts in all possible execution contexts, is undecidable in general. The main contribution of this paper is a sound verification technique for honesty, targeted at agents modelled in a value-passing version of the calculus CO2. To do that, we safely over-approximate the honesty property by abstracting from the actual values and from the contexts a process may be engaged with. Then, we develop a model-checking technique for this abstraction, we describe its implementation in Maude, and we discuss some experiments with it