A Fractionally Integrated Wishart Stochastic Volatility Model
There has recently been growing interest in modeling and estimating alternative continuous time multivariate stochastic volatility models. We propose a continuous time fractionally integrated Wishart stochastic volatility (FIWSV) process. We derive the conditional Laplace transform of the FIWSV mode...
| Autores: | , |
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| Formato: | informe técnico |
| Fecha de publicación: | 2013 |
| País: | España |
| Recursos: | Universidad Complutense de Madrid (UCM) |
| Repositorio: | Docta Complutense |
| Idioma: | inglés |
| OAI Identifier: | oai:docta.ucm.es:20.500.14352/41453 |
| Acesso em linha: | https://hdl.handle.net/20.500.14352/41453 |
| Access Level: | acceso abierto |
| Palavra-chave: | C32 C51 G13 Diusion process Multivariate stochastic volatility Long memory Fractional Brownian motion Generalized Method of Moments. Econometría (Economía) 5302 Econometría |
| Resumo: | There has recently been growing interest in modeling and estimating alternative continuous time multivariate stochastic volatility models. We propose a continuous time fractionally integrated Wishart stochastic volatility (FIWSV) process. We derive the conditional Laplace transform of the FIWSV model in order to obtain a closed form expression of moments. We conduct a two-step procedure, namely estimating the parameter of fractional integration via log-periodgram regression in the rst step, and estimating the remaining parameters via the generalized method of moments in the second step. Monte Carlo results for the procedure shows reasonable performances in nite samples. The empirical results for the bivariate data of the S&P 500 and FTSE 100 indexes show that the data favor the new FIWSV processes rather than one-factor and two-factor models of Wishart autoregressive processes for the covariance structure. |
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