models, and we provide a feasible way to price options in this framework. Our framework can be used irrespective of the … options on the minimum of two indices. Our results show that not only is correlation important for these options but so is …

, mispricings remain. This paper uses mixed normal heteroskedasticity models to price options. Our model allows for significant … negative skewness and time varying higher order moments of the risk neutral distribution. Parameter inference using Gibbs …. When forecasting out-of-sample options on the S&P 500 index, substantial improvements are found compared to a benchmark …

This paper uses asymmetric heteroskedastic normal mixture models to fit return data and to price options. The models …, and allow for substantial negative skewness and time varying higher order moments of the risk neutral distribution. When … forecasting out-of-sample a large set of index options between 1996 and 2009, substantial improvements are found compared to …

, mispricings remain. This paper uses mixed normal heteroskedasticity models to price options. Our model allows for significant … negative skewness and time varying higher order moments of the risk neutral distribution. Parameter inference using Gibbs …. When forecasting out-of-sample options on the S&P 500 index, substantial improvements are found compared to a benchmark …

, mispricings remain. This paper uses mixed normalheteroskedasticity models to price options. Our model allows for significant …. When forecasting out-of-sample options on the S&P 500index, substantial improvements are found compared to a benchmark …

It contains an introduction to how simulation methods can be used to price American options and a discussion of various …

pricing options in this framework. Our application confirms the importance of allowing for dynamic correlation, and it shows …