We propose a nonparametric Bayesian approach for the estimation of the pricing kernel. Historical stock returns and option market data are combined through the Dirichlet Process (DP) to construct an option-adjusted physical measure. The precision parameter of the DP process is calibrated to the...

This paper proposes a theoretical analysis on the impacts of using a suboptimal information set on the three main components used in asset pricing, namely the risk physical and neutral measures and the relative pricing kernel.The analysis is carried out by means of a portfolio optimization...

Supported by empirical examples, this paper provides a theoretical analysis on the impacts of using a suboptimal information set for the estimation of the empirical pricing kernel and, more in general, for the validity of the fundamental theorems of asset pricing. While inferring the...

The forward-looking nature of option market data allows one to derive economically-based and model-free risk measures. This article proposes an extensive analysis of the performances of option-implied VaR and CVaR, and compare them with classical risk measures for the S&P500 Index. Delivering...

Empirical indicators of sentiment are commonly employed in the economic literature while a precise understanding of what is sentiment is still missing. Exploring the links among the most popular proxies of sentiment, fear and uncertainty this paper aims to fi ll this gap. We show how fear and...

Using option market data we derive naturally forward-looking, nonparametric and model-free risk estimates, three desired characteristics hardly obtainable using historical returns. The option-implied measures are only based on the first derivative of the option price with respect to the strike...