In this thesis I present the multivariate Autoregressive Gamma process introduced by Le, Singleton and Dai (2010), a model founded on the univariate ARG first introduced in Gourieroux and Jasiak (2006). I discuss its mathematical properties and provide a MCMC algorithm for the Bayesian estimation of the parameters. The gamma process has been used due to its desirable properties in modelling realized volatility, for this reason I evaluate its performance on a panel of realized volatilities for multiple assets.
Bayesian Multivariate Autoregressive Gamma Processes: An Application to Realized Volatility
Bianchin, Daniele
2017/2018
Abstract
In this thesis I present the multivariate Autoregressive Gamma process introduced by Le, Singleton and Dai (2010), a model founded on the univariate ARG first introduced in Gourieroux and Jasiak (2006). I discuss its mathematical properties and provide a MCMC algorithm for the Bayesian estimation of the parameters. The gamma process has been used due to its desirable properties in modelling realized volatility, for this reason I evaluate its performance on a panel of realized volatilities for multiple assets.File in questo prodotto:
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Utilizza questo identificativo per citare o creare un link a questo documento:
https://hdl.handle.net/20.500.14247/17688