In this paper we develop a one-factor non-affine stochastic volatility option pricing model where the dynamics of the underlying is endogenously determined from micro-foundations. The interaction and herding of the agents trading the underlying asset induce an amplification of the volatility of the asset over the volatility of the fundamentals. Although the model is non-affine, a closed form option pricing formula can still be derived by using a Gauss-Hermite series expansion methodology. The model is calibrated using S&P 500 index options for the period 1996-2013. When its results are compared to some benchmark models we find that the new non-affine one-factor model outperforms the affine one-factor Heston model and it is competitive, especially out-of-sample, with the affine two-factor double Heston model.
In this a paper a non-linear macro stress testing methodology with focus on early warning is developed.
The methodology builds on a variant of Random Forests and its proximity measures. It is embedded
in a framework, in which naturally defined contagion and feedback effects transfer the impact of stressing a
relatively small part of the observations on the whole dataset, allowing to estimate a stressed future state. It
will be shown that contagion can be directly derived from the proximities while iterating the proximity based
contagion leads to naturally defined feedback effects. Since the methodology is Random Forests based the
framework can be estimated on large numbers of risk indicators up to big data dimensions, fostering the stability
of the results while reducing inaccuracies in estimated stress scenarios by only stressing a small part of
the observations. This procedure allows accurate forecasting of events under stress and the emergence of a
potential macro crisis. The framework also estimates a set of the most influential economic indicators leading
to the potential crisis, which can then be used as indications of remediation or prevention.
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