Option prices provide a great deal of information regarding the market’s expectations of future asset price dynamics. But, the implied dynamics are under the risk-neutral measure rather than the physical measure under which the price of the underlying asset itself evolves. This article demonstrates new techniques for joint analysis of the physical and risk-neutral models using data from both the underlying asset and options. While much of the prior work in this area has focused on affine and affine-jump models because of their analytical tractability, the techniques used in this article are straightforward to apply to a broad class of models of potential interest. The techniques are based on evaluating various integrals of interest using Monte Carlo sums over simulated volatility paths. In an application using S&P 500 index data, we find that log volatility models perform dramatically better than affine models, but that some evidence of misspecification remains.



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