Psychological barriers are prevalent among various asset classes, and it is important to consider their impact on the prices of derivative securities. This paper demonstrates the potential existence of such barriers on the S&P 500 Index and examines their impact on this index's rate of return...

We apply a two-step strategy to forecast the dynamics of the volatility surface implicit in option prices to all American-style options written on the stocks that have entered the Dow Jones Industrial Average Index between 2004 and 2016. We explore whether the implied volatilities extracted...

Over-allotment arrangements are nowadays part of almost any initial public offering. The underwriting banks borrow stocks from the previous shareholders to issue more than the initially announced number of shares. This is combined with the option to cover this short position at the issue price....

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...

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...

The valuation of options and many other derivative instruments requires an estimation of exante or forward looking volatility. This paper adopts a Bayesian approach to estimate stock price volatility. We find evidence that overall Bayesian volatility estimates more closely approximate the...