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An Evaluation Framework for Alternative VaR Models
SSRN Electronic Journal, 2002In this paper we investigate the ability of different models to produce useful var-estimates for exchange rate positions. Our analysis shows that it is important to take into account parameter uncertainty, since this leads to uncertainty in the predicted var. We make this uncertainty in the var explicit by means of simulation.
Bams, Dennis +2 more
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Sparse Change-Point VAR models
SSRN Electronic Journal, 2019AbstractChange‐point (CP) VAR models face a dimensionality curse due to the proliferation of parameters that arises when new breaks are detected. We introduce the Sparse CP‐VAR model which determines which parameters truly vary when a break is detected.
Dufays, A, Li, Z, Rombouts, JVK, Song, Y
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VAR cointegration in VARMA models [PDF]
, 1999 The method for estimation and testing for cointegration put forward by Johansen assumes that the data are described by a vector autoregressive process. In this article we extend the data generating process to autoregressive moving average models without unit roots in the MA polynomial.
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2004
In this chapter we discuss alternative model reduction strategies for the specification of subset VAR models. Before we present a number of subset modeling procedures in Section 2.2, we start by introducing the basics of the VAR modeling framework in Section 2.1.
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In this chapter we discuss alternative model reduction strategies for the specification of subset VAR models. Before we present a number of subset modeling procedures in Section 2.2, we start by introducing the basics of the VAR modeling framework in Section 2.1.
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A Sequential Modelling of the VaR
SSRN Electronic Journal, 2009We consider the VaR associated with the global loss generated by a set risk sources. We propose a sequence of simple models incorporating progressively the notions of contagion due to instantaneous correlations, of serial correlation, of evolution of the instantaneous correlations, of volatility clustering, of conditional heteroskedasticity and of ...
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2016
The discussion of forecasting with VAR models proceeds in two steps. First, we assume that the parameters of the model are known. Although this assumption is unrealistic, it will nevertheless allow us to introduce and analyze important concepts and ideas.
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The discussion of forecasting with VAR models proceeds in two steps. First, we assume that the parameters of the model are known. Although this assumption is unrealistic, it will nevertheless allow us to introduce and analyze important concepts and ideas.
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Markov-switching mixed-frequency VAR models
International Journal of Forecasting, 2015Abstract This paper introduces regime switching parameters to the Mixed-Frequency VAR model. We begin by discussing estimation and inference for Markov-switching Mixed-Frequency VAR (MSMF-VAR) models. Next, we assess the finite sample performance of the technique in Monte-Carlo experiments.
Foroni, Claudia +2 more
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2006
Abstract The purpose of this chapter is to introduce the non-stationary VAR model and show that the presence of unit roots (i.e. stochastic trends) leads to a reduced rank condition on the long-run matrix .
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Abstract The purpose of this chapter is to introduce the non-stationary VAR model and show that the presence of unit roots (i.e. stochastic trends) leads to a reduced rank condition on the long-run matrix .
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A spectral decomposition for structural VAR models
Empirical Economics, 1996Based on structural VARs, this paper proposes a spectral decomposition which allows to infer the effects of changes in one variable on the other variables in the frequency domain. It is shown that there is a close relationship between this concept and conventional forecast error variance decomposition techniques for VARs.
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