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The value of multivariate model sophistication: an application to pricing Dow Jones Industrial Average options

ROMBOUTS, Jeroen V. K.; STENTOFT, Lars; VIOLANTE, Francesco - Center for Operations Research and Econometrics (CORE), … - 2012

We assess the predictive accuracy of a large number of multivariate volatility models in terms of pricing options on the Dow Jones Industrial Average. We measure the value of model sophistication in terms of dollar losses by considering a set 248 multivariate models that differ in their...

Persistent link: https://www.econbiz.de/10010610494

Consistent ranking of multivariate volatility models

LAURENT, Sebastien; ROMBOUTS, Jeroen V.K.; VIOLANTE, … - Center for Operations Research and Econometrics (CORE), … - 2009

A large number of parameterizations have been proposed to model conditional variance dynamics in a multivariate framework. This paper examines the ranking of multivariate volatility models in terms of their ability to forecast out-of-sample conditional variance matrices. We investigate how...

Persistent link: https://www.econbiz.de/10008550212

Understanding volatility dynamics in the EU-ETS market: lessons from the future

SANIN, Maria Eugenia; VIOLANTE, Francesco - Center for Operations Research and Econometrics (CORE), … - 2009

In this paper we study the short term price behavior of December 2008 future prices for EU emission allowances. We … model returns and volatility dynamics of this price showing that a standard ARMA-GARCH framework is not adequate and that … fitness of the model, we combine the underlying price process with an additive stochastic jump process. The resulting …

Persistent link: https://www.econbiz.de/10008494369

On the forecasting accuracy of multivariate GARCH models

LAURENT, Sébastien; ROMBOUTS, Jeroen V. K.; VIOLANTE, … - Center for Operations Research and Econometrics (CORE), … - 2010

This paper addresses the question of the selection of multivariate GARCH models in terms of variance matrix forecasting accuracy with a particular focus on relatively large scale problems. We consider 10 assets from NYSE and NASDAQ and compare 125 model based one-step-ahead conditional variance...

Persistent link: https://www.econbiz.de/10008642224

Estimation and empirical performance of non-scalar dynamic conditional correlation models

BAUWENS, Luc; GRIGORYEVA, Lyudmila; ORTEGA, Juan-Pablo - Center for Operations Research and Econometrics (CORE), … - 2014

This paper presents a method capable of estimating richly parametrized versions of the dynamic conditional correlation (DCC) model that go beyond the standard scalar case. The algorithm is based on the maximization of a Gaussian quasi-likelihood using a Bregman-proximal trust-region method to...

Persistent link: https://www.econbiz.de/10011094065

Bayesian inference in dynamic disequilibrium models: an application to the Polish credit market

BAUWENS, Luc; LUBRANO, Michel - Center for Operations Research and Econometrics (CORE), … - 2006

We review Bayesian inference for dynamic latent variable models using the data augmentation principle. We detail the diffculties of simulating dynamic latent variables in a Gibbs sampler. We propose an alternative speciﬁcation of the dynamic disequilibrium model which leads to a simple...

Persistent link: https://www.econbiz.de/10005043443

Clustered panel data models: an efficient approach for nowcasting from poor data

MOUCHART, Michel; ROMBOUTS, Jeroen - Center for Operations Research and Econometrics (CORE), … - 2003

Nowcasting regards the inference on the present realization of random variables, on the basis of information available until a recent past. This paper proposes a modelling strategy aimed at a best use of the data for nowcasting based on panel data with severe deficiencies, namely short times...

Persistent link: https://www.econbiz.de/10005008511

Smooth transition GARCH models: a Bayesian perspective

LUBRANO, Michel - Center for Operations Research and Econometrics (CORE), … - 1998

This paper proposes a new kind of asymmetric GARCH where the conditional variance obeys two different regimes with a smooth transition function. In one formulation, the conditional variance reacts differently to negative and positive shocks while in a second formulation, small and big shocks...

Persistent link: https://www.econbiz.de/10005043445

Smooth Transition Garch Models: A Bayesian Perspective.

Lubrano, M. - Center for Operations Research and Econometrics (CORE), … - 1998

This paper proposes a new kind of asymmetric GARCh where the conditional variance obeys two different regimes with a smooth transition function. In one formulation, the conditional variance reacts differently to negative and positive shocks while in a second formulation, small and big shocks...

Persistent link: https://www.econbiz.de/10005669241

Specification, estimation and evaluation of vector smooth transition autoregressive models with applications

Terasvirta, Timo; Yang, Yukai - Center for Operations Research and Econometrics (CORE), … - 2014

applications. In the first one, the dynamic relationship between the US gasoline price and consumption is studied and possible …

Persistent link: https://www.econbiz.de/10011246311

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