Evaluating asset-pricing models using the hansen-jagannathan bound: a Monte Carlo investigation

We use recent statistical tests, based on a 'distance' between the model and the Hansen-Jagannathan bound, to compute the rejection rates of true models. For asset-pricing models with time-separable preferences, the finite-sample distribution of the test statistic associated with the risk-neutral case is extreme, in the sense that critical values based on this distribution deliver type I errors no larger than intended-regardless of risk aversion or the rate of time preference. We also show that these maximal-type-I-error critical values are appropriate for both time and state non-separable preferences and that they yield acceptably small type II error rates.