Results 271 to 280 of about 1,019,702 (312)
, 2014 We confirm that standard time-series models for US output growth, inflation, interest rates and stock market returns feature non-Gaussian error structure. We build a 4-variable VAR model where the orthogonolised shocks have a Student t-distribution with a time-varying variance.
Ching-Wai (Jeremy) Chiu +2 more
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2005
This paper analyses several volatility models by examining their ability to forecast the Value-at-Risk (VaR) for two different time periods and two capitalization weighting schemes. Specifically, VaR is calculated for large and small capitalization stocks, based on Dow Jones (DJ) Euro Stoxx indices and is modeled for long and short trading positions by
Timotheos Angelidis +2 more
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This paper analyses several volatility models by examining their ability to forecast the Value-at-Risk (VaR) for two different time periods and two capitalization weighting schemes. Specifically, VaR is calculated for large and small capitalization stocks, based on Dow Jones (DJ) Euro Stoxx indices and is modeled for long and short trading positions by
Timotheos Angelidis +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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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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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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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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VaR Modelling on Long Run Horizons
Automation and Remote Control, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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1995
In the minds of some economic theorists and traditional econometricians, the vector autoregressive (VAR) approach to time-series data is unscientific, obscure, confusing, or simply wrong. Since the publication of Sims’s original contributions (1972, 1980a, 1980b, 1982), the methodology has spurred endless debates. Critics claim that the methodology has
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In the minds of some economic theorists and traditional econometricians, the vector autoregressive (VAR) approach to time-series data is unscientific, obscure, confusing, or simply wrong. Since the publication of Sims’s original contributions (1972, 1980a, 1980b, 1982), the methodology has spurred endless debates. Critics claim that the methodology has
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Vector Autoregression (VAR) Model
2018In the regression analysis endogenous and exogenous variables are used alongside each other. An endogenous variable is a variable (generated by a statistical model), which is explained by the relationships between functions within the model. For example, the equilibrium price of a good in a supply and demand model is endogenous because it is set by a ...
Abdulkader Aljandali, Motasam Tatahi
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