This study analyzes whether investors take risks related to environmental, social and governance (ESG) factors into account when making portfolio decisions. We exploit the new Morningstar's ESG risk indicators-introduced at the end of 2019-to estimate the effect of ESG risk perception on investment fund flows. Our exercise, related to the early phase of the Covid-19 crisis when uncertainty skyrocketed, shows that investors have preferred low-ESG-risk funds, with environmental risks remaining a top concern.

The rising number of foreign workers in Italy during the last 15 years has led to a conspicuous increase in the amount of remittances sent abroad. In this paper, we examine the determinants of remittance outflows originated in Italy and transferred abroad through registered financial intermediaries. After controlling for a wide set of socioeconomic regressors, we document a strong positive relation between remittances and the cost of travel between Italy and the migrants' respective home countries. We interpret this result as indirect evidence of unrecorded flows, since the relation between remittances and travel cost should be non‐significant unless geographical proximity permits remitters to switch to informal (non‐observable) transmission mechanisms. Moreover, using data on temporal and monetary costs for a subset of bilateral corridors, we also find remittances to be negatively correlated with high transaction costs and low speed of transfer. We rely on this empirical evidence and on a model of migrants' remitting behavior to present new strategies for estimating the size of the informal outflow.

Summary  In this paper, we consider joint estimation of objective and risk‐neutral parameters for stochastic volatility option pricing models using both stock and option prices. A common strategy simplifies the task by limiting the analysis to just one option per date. We first discuss its drawbacks on the basis of model interpretation, estimation results and pricing exercises. We then turn the attention to a more flexible approach, that successfully exploits the wealth of information contained in large heterogeneous panels of options, and we apply it to actual S&P 500 index and index call options data. Our approach breaks the stochastic singularity between contemporaneous option prices by assuming that every observation is affected by measurement error, essentially recasting the problem as a non‐linear filtering one. The resulting likelihood function is evaluated using a Monte Carlo Importance Sampling (MC‐IS) strategy, combined with a Particle Filter algorithm. The results provide useful intuitions on the directions that should be followed to extend the model, in particular by allowing jumps or regime switching in the volatility process.

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