Empirical Ross recovery without discretization
提出一种连续状态的经验方法，基于双变量正交埃尔米特表示恢复罗斯恢复理论中的关键对象，避免离散化，并用标普500指数期权价格展示了良好效果。
Abstract We propose a new continuous‐state method for empirically recovering the key objects in the Ross recovery theory which avoids discretization. The new method is based on a key bivariate orthogonal Hermite representation of the state price transition kernel, which leads to an elegant correspondence of the eigenvalue–eigenfunction system of the transition kernel and the eigenvalue–eigenvector system of the expansion coefficient matrix. Using S&P 500 index option prices, we demonstrate how our method can generate well‐behaved state price transition kernels and physical densities. Our method can also be used to compute the key objects in the Hansen and Scheinkman's factorization theory.