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...

We study both theoretically and empirically option prices on firms undergoing a cash merger offer. To estimate the merger's success probability, we use a Markov Chain Monte Carlo (MCMC) method using a state space representation of our model. Our estimated probability measure has significant...

We study an extension of the classical B1ack-Scholes model which accounts for feedback effects from trading in an imperfectly elastic market. The proposed semi-martingale model may be viewed as a compromise between the diffusion approach in, e.g., (Cuoco and Cvitanic 1998), (Cvitanic and Ma...