In this paper, we consider the Bickel-Rosenblatt test for continuous time stochastic volatility models. The test is constructed based on discretely observed samples by measuring integrated squared deviations between the nonparametric kernel density estimate from the observations and a parametric fit of the density. It is shown that under the null, the proposed test is asymptotically normal. To evaluate the proposed test, a simulation study is performed for illustration.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- Statistics, Probability and Uncertainty