Conditional Gauss-Hermite filtering with application to volatility estimation

Singer, Hermann GND

The conditional Gauss{Hermite lter (CGHF) utilizes a decompo- sition of the lter density by conditioning on an appropriate part of the state vector. In contrast to the usual Gauss{Hermite lter (GHF) it is only assumed that the terms in the decomposition can be ap- proximated by Gaussians. Due to the nonlinear dependence on the condition, quite complicated densities can be modeled, but the ad- vantages of the normal distribution are preserved. For example, in stochastic volatility models, the joint density of return and volatility strongly deviates from a bivariate Gaussian, whereas the conditional density can be well approximated by a normal distribution. As in the GHF, integrals in the time and measurement updates can be com- puted by Gauss-Hermite quadrature.

Vorschau

Zitieren

Zitierform:

Singer, Hermann: Conditional Gauss-Hermite filtering with application to volatility estimation. Hagen 2008. FernUniversität in Hagen.

Zugriffsstatistik

Gesamt

Volltextzugriffe:
Metadatenansicht:

12 Monate

Volltextzugriffe:
Metadatenansicht:

Rechte

Nutzung und Vervielfältigung:
Alle Rechte vorbehalten

Export

powered by MyCoRe