Inference for Systems of Stochastic Differential Equations from Discretely Sampled Data

A Numerical Maximum Likelihood Approach

Maximum likelihood estimation of discretely observed diffusion processes is mostly hampered by the lack of a closed form solution of the transient density. In this paper the author expands extant work on univariate diffusions to higher dimensions. After providing evidence for the efficiency of a numerical approach, the authors illustrate its application for the estimation of a joint system of short-run and medium run investor sentiment and asset price dynamics using German stock market data.

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