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...

Due to the scarcity of reliable data, the existing literature on default risk still displays an imbalance between theoretical and empirical contributions. Consequently, the focus of this book is on empirical work. Within an intensity based modelling framework a broad range of promising...

Due to the scarcity of reliable data, the existing literature on default risk still displays an imbalance between theoretical and empirical contributions. Consequently, the focus of this book is on empirical work. Within an intensity based modelling framework a broad range of promising...

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...

This paper analyzes empirical market utility functions and pricing kernels derived from the DAX and DAX option data for three market regimes. A consistent parametric framework of stochastic volatility is used. All empirical market utility functions show a region of risk proclivity that is...

We revisit the so-called Bergomi-Guyon expansion (Bergomi and Guyon, Stochastic volatility's orderly smiles, Risk, May 2012). The expansion provides the smile of implied volatility at second order in the volatility of volatility for general stochastic volatility models, including variance curve...