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SSRN Electronic Journal, 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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Structural VAR models in the Frequency Domain
Journal of Econometrics, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guay, Alain, Pelgrin, Florian
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2023
Credit value at risk (VaR) is used for measuring and analyzing credit risk of a portfolio. The basic methodology of the Credit VaR employs the credit migration approach spearheaded by RiskMetrics. It assumes that obligor's credit quality is determined by the obligor's asset value, which in turn is approximated by its standardized equity return.
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Credit value at risk (VaR) is used for measuring and analyzing credit risk of a portfolio. The basic methodology of the Credit VaR employs the credit migration approach spearheaded by RiskMetrics. It assumes that obligor's credit quality is determined by the obligor's asset value, which in turn is approximated by its standardized equity return.
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, 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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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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