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SUMMARY:Scalable inference for a full multivariate stochastic volatility m
 odel - Prof. Petros Dellaportas\, Dept. of Statistical Science\, UCL
DTSTART:20160421T130000Z
DTEND:20160421T140000Z
UID:TALK65760@talks.cam.ac.uk
CONTACT:Prof. Ramji Venkataramanan
DESCRIPTION:We introduce a multivariate stochastic volatility model for as
 set returns that imposes no restrictions to the structure of the volatilit
 y matrix and treats all its elements as functions of latent stochastic pro
 cesses. When the number of assets is prohibitively large\, we propose a fa
 ctor multivariate stochastic volatility model in which the variances and c
 orrelations of the factors evolve stochastically over time. Inference is a
 chieved via a carefully designed feasible andscalable Markov chain Monte C
 arlo algorithm that combines two computationally important ingredients: it
  utilizes invariant to the prior Metropolis proposal densities for simulta
 neously updating all latent paths and has quadratic\, rather than cubic\, 
 computational complexity when evaluating the multivariate normal densities
  required. We apply our modelling and computational methodology to 571 sto
 ck daily returns of Euro STOXX index for data over a period of 10 years.
LOCATION:LR11\, Department of Engineering
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