Journal
ANNALS OF STATISTICS
Volume 32, Issue 5, Pages 2223-2253Publisher
INST MATHEMATICAL STATISTICS
DOI: 10.1214/009053604000000797
Keywords
diffusion processes; nonparametric estimation; discrete sampling; low frequency data; spectral approximation; ill-posed problems
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We study the problem of estimating the coefficients of a diffusion (X-t, t greater than or equal to 0); the estimation is based on discrete data X-nDelta, n = 0, 1,..., N. The sampling frequency Delta(-1) is constant, and asymptotics are taken as the number N of observations tends to infinity. We prove that the problem of estimating both the diffusion coefficient (the volatility) and the drift in a nonparametric setting is ill-posed: the minimax rates of convergence for Sobolev constraints and squared-error loss coincide with that of a, respectively, first- and second-order linear inverse problem. To ensure ergodicity and limit technical difficulties we restrict ourselves to scalar diffusions living on a compact interval with reflecting boundary conditions. Our approach is based on the spectral analysis of the associated Markov semigroup. A rate-optimal estimation of the coefficients is obtained via the nonparametric estimation of an eigenvalue-eigenfunction pair of the transition operator of the discrete time Markov chain (X-nDelta, n = 0, 1,..., N) in a suitable Sobolev norm, together with an estimation of its invariant density.
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