Density Approximations for Multivariate Affine Jump-Diffusion Processes
By Damir Filipovi'c, Eberhard Mayerhofer, Paul Schneider. 2011
125 citations recorded by the source corpus.
We introduce closed-form transition density expansions for multivariate affine jump-diffusion processes. The expansions rely on a general approximation theory which we develop in weighted Hilbert spaces for random variables which possess all polynomial moments. We establish parametric conditions which guarantee existence and differentiability of transition densities of affine models and show how they naturally fit into the approximation framework. Empirical applications in option pricing, credit risk, and likelihood inference highlight the usefulness of our expansions. The approximations are extremely fast to evaluate, and they perform very accurately and numerically stable.
Subfields: Finance, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/17fdd6f8f72c9d4108e3bc4df6923fccb8eb4ded
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In Finance
In Statistics and Probability
Citations in the corpus
- Maximum-likelihood estimation for diffusion processes via closed-form density expansions (2013, background): “and Mykland [7, 8], Aı̈t-Sahalia and Kimmel [5, 6], Li [45], Egorov, Li and Xu [26], Schaumburg [58], Aı̈t-Sahalia and Yu [9], Yu [72], Filipović, Mayerhofer and Schneider [31], Tang and Chen [61], Xiu [66] and Chang and Chen [22].”