This paper develops an econometric framework for (i)
estimating excess returns of the security process from
high frequency derivative prices, (ii) testing for risk
neutral pricing, and (iii) measuring premiums outside the
no-arbitrage pricing model. The estimator is constructed
by applying quasi-likelihood and Feynman–Kac theory
to the risk neutral contingent claims pricing model to
generate the optimal orthogonality restriction. The strong
consistency and asymptotic normality of the estimator are
established in the context of a nonstationary underlying
state process. These results further imply that the estimator
is robust to distributional assumptions on the underlying
asset process. The proposed approach is applicable to any
arbitrary derivative security, does not require estimation
of the risk neutral probability measure, and has application
to spot rate bond pricing models. A controlled diagnostic
study based on generating the S&P500 index and calls
verifies the ability of the estimators to correctly estimate
security excess returns and test for risk neutral pricing.
The estimator is invariant to call strikes, and larger
samples constructed by cycling over shorter maturity options
can be used to reduce its variance.