Numerical Integration-Based Gaussian Mixture Filters for Maximum Likelihood Estimation of Asymmetric Stochastic Volatility Models  

Oleh:

Kawakatsu, Hiroyuki

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: The Econometrics Journal vol. 10 no. 2 (2007), page 342–358.
Topik: Stochastic; sthocastic votality; non linear filtering; leverage effect; numerical integration; mixture gaussian
Fulltext: 342.pdf (509.13KB)

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: EE39.3 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

I consider Gaussian filters based on numerical integration for maximum likelihood estimation of stochastic volatility models with leverage. I show that for this class of models, the prediction step of the Gaussian filter can be evaluated analytically without linearizing the state – space model. Monte Carlo simulations show that the mixture Gaussian filter performs remarkably well in terms of both accuracy and computation time compared to the quasi - maximum likelihood and importance sampler filters. The result that the prediction step of the Gaussian filter can be evaluated analytically is shown to apply more generally to a number of commonly used specifications of the stochastic volatility model.

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