Information about risk preferences from investors is essential for modelling a wide range of quantitative finance applications. Valuable information related to preferences can be extracted from option prices through pricing kernels. In this paper, pricing kernels and their term structure are estimated in a time varying approach from DAX and ODAX data using dynamic semiparametric factor model (DSFM). DSFM smooths in time and space simultaneously, approximating complex dynamic structures by basis functions and a time series of loading coefficients. Contradicting standard risk aversion assumptions, the estimated pricing kernels indicate risk proclivity in certain levels of return. The analysis of the time series of loading coefficients allows a better understanding of the dynamic behaviour from investors preferences towards risk.