Working papers results

We evaluate linear stochastic discount factor models using an ex-post portfolio metric: the realized out-of-sample Sharpe ratio of mean-variance portfolios backed by alternative linear factor models. Using a sample of monthly US portfolio returns spanning the period 1968-2016, we find evidence that multifactor linear models have better empirical properties than the CAPM, not only when the cross-section of expected returns is evaluated in-sample, but also when they are used to inform one-month ahead portfolio selection. When we compare portfolios associated to multifactor models with mean-variance decisions implied by the single-factor CAPM, we document statistically significant differences in Sharpe ratios of up to 10 percent. Linear multifactor models that provide the best in-sample fit also yield the highest realized Sharpe ratios.

We propose a Markov Switching Graphical Seemingly Unrelated Regression (MS-GSUR) model to investigate time-varying systemic risk based on a range of multi-factor asset pricing models. Methodologically, we develop a Markov Chain Monte Carlo (MCMC) scheme in which latent states are identified on the basis of a novel weighted eigenvector centrality measure. An empirical application to the constituents of the S&P100 index shows that cross-firm connectivity significantly increased over the period 1999-2003 and during the financial crisis in 2008-2009. Finally, we provide evidence that firm-level centrality does not correlate with market values and it is instead positively linked to realized financial losses.

What are the effects of a housing bubble on the rest of the economy? We show that if firms and banks face collateral constraints, a housing bubble initially raises credit demand by housing firms while leaving credit supply unaffected. It therefore crowds out credit to non-housing firms. If time passes and the bubble lasts, however, housing firms eventually pay back their higher loans. This leads to an increase in banks' net worth and thus to an expansion in their supply of credit to all firms: crowding-out gives way to crowding-in. These predictions are confirmed by empirical evidence from the recent Spanish housing bubble. In the early years of the bubble, non-housing firms reduced their credit from banks that were more exposed to the bubble, and firms that were more exposed to these banks had lower credit and output growth. In its last years, these effects were reversed.

Since the middle of the 1990s, productivity growth in Southern Europe has been substantially lower than in other developed countries. In this paper, we argue that this divergence was partly caused by inefficient management practices, which limited Southern Europe's gains from the IT Revolution. To quantify this effect, we build a multi-country general equilibrium model with heterogeneous firms and workers. In our model, the IT Revolution generates divergence for three reasons. First, inefficient management limits Southern firms' productivity gains from IT adoption. Second, IT increases the aggregate importance of management, making its inefficiencies more salient. Third, IT-driven wage increases in other countries stimulate Southern high-skill emigration. We calibrate our model using firm-level evidence, and show that it can account for 28% of Italy's, 39% of Spain's and 67% of Portugal's productivity divergence with respect to Germany between 1995 to 2008.

We experimentally explore decision-making under uncertainty using a framework that decomposes uncertainty into three distinct layers: (1) physical uncertainty, entailing inherent randomness within a given probability model, (2) model uncertainty, entailing subjective uncertainty about the probability model to be used and (3) model misspecification, entailing uncertainty about the presence of the true probability model among the set of models considered. Using a new experimental design, we measure individual attitudes towards these different layers of uncertainty and study the distinct role of each of them in characterizing ambiguity attitudes. In addition to providing new insights into the underlying processes behind ambiguity aversion -failure to reduce compound probabilities or distinct attitudes towards unknown probabilities- our study provides the first empirical evidence for the intermediate role of model misspecification between model uncertainty and Ellsberg in decision-making under uncertainty.

We study in a theoretical and experimental setting the interaction between belief-dependent preferences and reputation building in a finitely repeated trust game. We focus mainly on the effect of guilt aversion. In a simple two-types model, we analyze the effect of reputation building in presence of guilt-averse players and derive behavioral predictions. In the experiment, we elicit information on trustees' belief-dependent preferences and disclose it to the paired trustor before the repeated game. Our experimental results show that disclosing information on the trustee's belief-dependent preferences and thus letting players play the repeated trust game in presence of almost complete information leads to higher trust and cooperation than in the corresponding incomplete information game setting. In particular, disclosure of information on preferences of guilt-averse trustees also enhances the trustors'cooperation. Disclosure of information on belief-dependent preferences of reciprocity-concerned trustees, instead, does not lead to higher trust and cooperation. We show that this is theoretically consistent with subjects featuring low reciprocity concerns.

In social dilemmas, choices may depend on belief-dependent motivations enhancing the credibility of promises or threats at odds with personal gain maximization. We address this issue theoretically and experimentally in the context of the Ultimatum Minigame, assuming that the choice of accepting or rejecting an unfair proposal is affected by a combination of frustration, due to unfulfilled expectations, and inequity aversion. We increase the responder's payoff from the default allocation (the proposer's outside option) with the purpose of increasing the responder's frustration due to the unfair proposal, and thus his willingness to reject it. In addition, we manipulate the method of play, with the purpose of switching on (direct response method) and off (strategy method) the responder's experience of anger. Our behavioral predictions across and within treatments are derived from the theoretical model complemented by explicit auxiliary assumptions, without relying on equilibrium analysis.

We show that a probability measure on a metric space X has full support if and only if the set of all probability measures that are absolutely continuous with respect to it is dense in P (X). We illustrate the result through a general version of Laplace's method, which in turn leads to a general stochastic convergence result to global maxima.

Firm-level productivity shocks can help understand sector- and macroeconomic-level outcomes. Capturing the market power of these firms is important: it determines how productivity gains translate into prices and markups. In existing models, firms do not internalize the impact of their systemic size. This paper explores the alternative oligopolistic market structure. To this end, I build a tractablemulti-sector heterogeneous-firmgeneral equilibriummodel featuring oligopolistic competition and an input-output (I-O) network. By affecting price and markup, firm-level productivity shocks propagate both to the downstream and upstream sectors. Sector-level competition intensity affects the strength of these new propagation mechanisms. The structural importance of a firm is determined by the interaction of (i) the sector-level competition intensity, (ii) the firm's sector position in the I-O network, and (iii) the firm size. In a calibration exercise, the aggregate volatility arising from independent firm-level shock is 34% of the one observed in the data.