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Hands-On Value-at-Risk and expected shortfall : a practical primer
Auer, Martin - 2018
cation -- 11 Support Measures -- Part II OPERATIONS -- 12 Properties of VaR -- 13 Properties of ES -- 14 VaR Noise -- 15 …
Persistent link: https://www.econbiz.de/10013341689
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Comparing long monthly Chinese and selected European temperature series using the Vector Seasonal Shifting Mean and Covariance Autoregressive model
He, Changli; Kang, Jian; Teräsvirta, Timo; Zhang, Shuhua - In: Energy economics 97 (2021), pp. 1-14
Persistent link: https://www.econbiz.de/10012821325
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Modelling volatilities and conditional correlations in futures markets with a multivariate t distribution
Pesaran, Bahram (contributor);  … - 2007
Persistent link: https://www.econbiz.de/10003491106
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Modelling volatilities and conditional correlations in futures markets with a multivariate t distribution
Pesaran, Bahram (contributor);  … - 2007
This paper considers a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by … than by GARCH type volatility estimates. The t-DCC estimation procedure is applied to a portfolio of daily returns on …-DCC specification. The t-DCC model also passes a number of VaR diagnostic tests over an evaluation sample. The estimation results …
Persistent link: https://www.econbiz.de/10003586562
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Modelling volatilities and conditional correlations in futures markets with a multivariate t distribution
Pesaran, Bahram (contributor);  … - 2007
Persistent link: https://www.econbiz.de/10003499671
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Beyond connectedness: a covariance decomposition based network risk model
Akovalı, Umut - 2020
This study extends the Diebold-Yilmaz Connectedness Index (DYCI) methodology and, based on forecast error covariance decompositions, derives a network risk model for a portfolio of assets. As a normalized measure of the sum of variance contributions, system-wide connectedness averages out the...
Persistent link: https://www.econbiz.de/10012170580
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Modelling Volatilities and Conditional Correlations in Futures Markets with a Multivariate T Distribution
Pesaran, Bahram; Pesaran, M. Hashem - 2021
This paper considers a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by … than by GARCH type volatility estimates. The t-DCC estimation procedure is applied to a portfolio of daily returns on …-DCC specification. The t-DCC model also passes a number of VaR diagnostic tests over an evaluation sample. The estimation results …
Persistent link: https://www.econbiz.de/10013316934
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A variance spillover analysis without covariances : what do we miss?
Fengler, Matthias; Gisler, Katja, I. M. - 2014
Persistent link: https://www.econbiz.de/10010439167
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Does Modeling Jumps Help? A Comparison of Realized Volatility Models for Risk Prediction
Liao, Yin - 2012
volatility models are compared in terms of their VaR forecasting performances through a Monte Carlo study and an analysis based …Recent literature has focuses on realized volatility models to predict financial risk. This paper studies the benefit … of explicitly modeling jumps in this class of models for value at risk (VaR) prediction. Several popular realized …
Persistent link: https://www.econbiz.de/10013105658
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Risk Measures under a Stochastic Volatility Model with a Mixture-of-Normal Error Distribution
Xu, Dinghai - 2013
This paper constructs Value at Risk (VaR) measures from a stochastic volatility model with a discrete bivariate mixture …-of-normal error distribution - henceforth SV-MN. This volatility-gnerating model is able to accommodate many of the salient features … of financial asset returns, such as time-varying volatility, volatility clustering, excess skewness and kurtosis in the …
Persistent link: https://www.econbiz.de/10013084063