We propose a parsimonious semiparametric method for macroeconomic forecasting during episodes of sudden changes. Based on the notion of clustering and similarity, we partition the time series into blocks, search for the closest blocks to the most recent block of observations, and with the...

We propose a new methodology to estimate the empirical pricing kernel implied from option data. In contrast to most of the studies in the literature that use an indirect approach, i.e. first estimating the physical and risk-neutral densities and obtaining the pricing kernel in a second step, we...

In this thesis, a new methodology using convex risk measures is developed to incorporate parameter risk into prices of financial derivatives, provided that a distribution on the parameter space is given. A technique to induce a parameter distribution in case of calibration to market prices is...