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Bayesian Estimation of Volatility with Moment-Based Nonlinear Stochastic Filters

Grothe, Oliver; Singer, Hermann

Abstract:
This article adresses parameter estimation with moment-based stochastic filters that only heed the first two moments of the state densities. This approximation provides good results in numerous cases. However, due to missing linear correlation between diffusion parameters and expected states, Bayesian estimation of diffusion parameters such as volatility is not possible. While other filters overcome this problem by simulations, we present a deterministic algorithm for Bayesian estimation of the diffusion coefficient based on sigma points which can be applied to all moment-based filters. To show the validity of the algorithm we use the continuous-discrete unscented Kalman filter proposed by Singer [18].


Zugehörige Institution(en) am KIT Institut für Operations Research (IOR)
Publikationstyp Buch
Jahr 2006
Sprache Englisch
Identifikator KITopen ID: 1000050468
Verlag Hagen
Serie Diskussionsbeiträge / Fakultät für Wirtschaftswissenschaft FernUniversität in Hagen ; 401
URLs Link
Schlagworte Bayesian parameter estimation, nonlinear systems, unscented Kalman filter, maximum likelihood estimation, stochastic volatility
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