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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 - School of Economics and Management, University of Aarhus - 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/10009492823

The tyranny of uncertainty : a new framework to predict, remediate and monitor risk

Abu El Ata, Nabil; Schmandt, Rudolf - 2016

Persistent link: https://www.econbiz.de/10011387014

Multivariate Variance Targeting in the BEKK-GARCH Model

Pedersen, Rasmus Søndergaard; Rahbek, Anders - School of Economics and Management, University of Aarhus - 2012

This paper considers asymptotic inference in the multivariate BEKK model based on (co-)variance targeting (VT). By defi?nition the VT estimator is a two-step estimator and the theory presented is based on expansions of the modifi?ed likelihood function, or estimating function, corresponding to...

Persistent link: https://www.econbiz.de/10010851199

A Lagrange Multiplier Test for Testing the Adequacy of the Constant Conditional Correlation GARCH Model

Catani, Paul; Teräsvirta, Timo; Yin, Meiqun - School of Economics and Management, University of Aarhus - 2014

A Lagrange multiplier test for testing the parametric structure of a constant conditional correlation generalized autoregressive conditional heteroskedasticity (CCC-GARCH) model is proposed. The test is based on decomposing the CCC-GARCH model multiplicatively into two components, one of which...

Persistent link: https://www.econbiz.de/10010851267

Multivariate GARCH models

Silvennoinen, Annastiina; Teräsvirta, Timo - School of Economics and Management, University of Aarhus - 2008

This article contains a review of multivariate GARCH models. Most common GARCH models are presented and their properties considered. This also includes nonparametric and semiparametric models. Existing specification and misspecification tests are discussed. Finally, there is an empirical example...

Persistent link: https://www.econbiz.de/10005114123

Modelling Multivariate Autoregressive Conditional Heteroskedasticity with the Double Smooth Transition Conditional Correlation GARCH Model

Silvennoinen, Annastiina; Teräsvirta, Timo - School of Economics and Management, University of Aarhus - 2008

In this paper we propose a multivariate GARCH model with a time-varying conditional correlation structure. The new Double Smooth Transition Conditional Correlation GARCH model extends the Smooth Transition Conditional Correlation GARCH model of Silvennoinen and Ter¨asvirta (2005) by including...

Persistent link: https://www.econbiz.de/10005114133

Stochastische Risikomodellierung und statistische Methoden : ein anwendungsorientiertes Lehrbuch für Aktuare

Becker, Torsten; Herrmann, Richard; Sandor, Viktor; … - 2016 - 1. Auflage

Persistent link: https://www.econbiz.de/10011450287

Diffusion Indexes with Sparse Loadings

Kristensen, Johannes Tang - School of Economics and Management, University of Aarhus - 2013

The use of large-dimensional factor models in forecasting has received much attention in the literature with the … model which is better suited for forecasting compared to the traditional principal components (PC) approach.We provide an … asymptotic analysis of the estimator and illustrate its merits empirically in a forecasting experiment based on US macroeconomic …

Persistent link: https://www.econbiz.de/10010851192

Estimating High-Dimensional Time Series Models

Medeiros, Marcelo C.; Mendes, Eduardo F. - School of Economics and Management, University of Aarhus - 2012

We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time-series models. We assume both the number of covariates in the model and candidate variables can increase with the number of observations and the number of candidate variables is,...

Persistent link: https://www.econbiz.de/10010851219

Nonlinearity, Breaks, and Long-Range Dependence in Time-Series Models

Hillebrand, Eric; Medeiros, Marcelo C. - School of Economics and Management, University of Aarhus - 2012

We study the simultaneous occurrence of long memory and nonlinear effects, such as parameter changes and threshold effects, in ARMA time series models and apply our modeling framework to daily realized volatility. Asymptotic theory for parameter estimation is developed and two model building...

Persistent link: https://www.econbiz.de/10010851244