CAViaR

CAViaR: Conditional autoregressive value at risk by regression quantiles. Value at risk (VaR) is the standard measure of market risk used by financial institutions. Interpreting the VaR as the quantile of future portfolio values conditional on current information, the conditional autoregressive value at risk (CAViaR) model specifies the evolution of the quantile over time using an autoregressive process and estimates the parameters with regression quantiles. Utilizing the criterion that each period the probability of exceeding the VaR must be independent of all the past information, we introduce a new test of model adequacy, the dynamic quantile test. Applications to real data provide empirical support to this methodology.


References in zbMATH (referenced in 94 articles )

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  1. Blasques, Francisco; Gorgi, Paolo; Koopman, Siem Jan; Wintenberger, Olivier: Feasible invertibility conditions and maximum likelihood estimation for observation-driven models (2018)
  2. El Adlouni, Salaheddine; Salaou, Garba; St-Hilaire, André: Regularized Bayesian quantile regression (2018)
  3. Gregory, Karl B.; Lahiri, Soumendra N.; Nordman, Daniel J.: A smooth block bootstrap for quantile regression with time series (2018)
  4. Xu, Qifa; Cai, Chao; Jiang, Cuixia; Sun, Fang; Huang, Xue: Sampling Lasso Quantile Regression for large-scale data (2018)
  5. Boudt, Kris; Laurent, Sébastien; Lunde, Asger; Quaedvlieg, Rogier; Sauri, Orimar: Positive semidefinite integrated covariance estimation, factorizations and asynchronicity (2017)
  6. De Gooijer, Jan G.: Elements of nonlinear time series analysis and forecasting (2017)
  7. Du, Jiangze; Lai, Kin Keung: Copula-based risk management models for multivariable RMB exchange rate in the process of RMB internationalization (2017)
  8. Montes-Rojas, Gabriel: Reduced form vector directional quantiles (2017)
  9. Pitselis, Georgios: Risk measures in a quantile regression credibility framework with Fama/French data applications (2017)
  10. Taylor, James W.: Probabilistic forecasting of wind power ramp events using autoregressive logit models (2017)
  11. Ye, Wuyi; Luo, Kebing; Liu, Xiaoquan: Time-varying quantile association regression model with applications to financial contagion and VaR (2017)
  12. Allen, D. E.; Powell, R. J.; Singh, A. K.: Take it to the limit: innovative CVaR applications to extreme credit risk measurement (2016)
  13. Bernardi, Mauro; Catania, Leopoldo: Comparison of value-at-risk models using the MCS approach (2016)
  14. Chan, Ngai Hang; Sit, Tony: Artifactual unit root behavior of value at risk (VaR) (2016)
  15. David Ardia, Kris Boudt, Leopoldo Catania: Value-at-Risk Prediction in R with the GAS Package (2016) arXiv
  16. Gerlach, Richard; Peiris, Shelton; Lin, Edward M. H.: Bayesian estimation and inference for log-ACD models (2016)
  17. Han, Heejoon; Linton, Oliver; Oka, Tatsushi; Whang, Yoon-Jae: The cross-quantilogram: measuring quantile dependence and testing directional predictability between time series (2016)
  18. Kobayashi, Genya: Skew exponential power stochastic volatility model for analysis of skewness, non-normal tails, quantiles and expectiles (2016)
  19. Kou, Steven; Peng, Xianhua: On the measurement of economic tail risk (2016)
  20. Noh, Jungsik; Lee, Sangyeol: Quantile regression for location-scale time series models with conditional heteroscedasticity (2016)

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