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 62 articles )

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  1. De Gooijer, Jan G.: Elements of nonlinear time series analysis and forecasting (2017)
  2. Allen, D.E.; Powell, R.J.; Singh, A.K.: Take it to the limit: innovative CVaR applications to extreme credit risk measurement (2016)
  3. Bernardi, Mauro; Catania, Leopoldo: Comparison of value-at-risk models using the MCS approach (2016)
  4. Chan, Ngai Hang; Sit, Tony: Artifactual unit root behavior of value at risk (VaR) (2016)
  5. David Ardia, Kris Boudt, Leopoldo Catania: Value-at-Risk Prediction in R with the GAS Package (2016) arXiv
  6. Gerlach, Richard; Peiris, Shelton; Lin, Edward M.H.: Bayesian estimation and inference for log-ACD models (2016)
  7. Han, Heejoon; Linton, Oliver; Oka, Tatsushi; Whang, Yoon-Jae: The cross-quantilogram: measuring quantile dependence and testing directional predictability between time series (2016)
  8. Kobayashi, Genya: Skew exponential power stochastic volatility model for analysis of skewness, non-normal tails, quantiles and expectiles (2016)
  9. Kou, Steven; Peng, Xianhua: On the measurement of economic tail risk (2016)
  10. Pitselis, Georgios: Credible risk measures with applications in actuarial sciences and finance (2016)
  11. Wang, Chuan-Sheng; Zhao, Zhibiao: Conditional Value-at-Risk: semiparametric estimation and inference (2016)
  12. Ye, Wuyi; Zhu, Yangguang; Wu, Yuehua; Miao, Baiqi: Markov regime-switching quantile regression models and financial contagion detection (2016)
  13. Andreou, Elena; Werker, Bas J.M.: Residual-based rank specification tests for AR-GARCH type models (2015)
  14. Bekaert, Geert; Engstrom, Eric; Ermolov, Andrey: Bad environments, good environments: a non-Gaussian asymmetric volatility model (2015)
  15. Cho, Jin Seo; Kim, Tae-hwan; Shin, Yongcheol: Quantile cointegration in the autoregressive distributed-lag modeling framework (2015)
  16. Lin, Wei; Cai, Zongwu; Li, Zheng; Su, Li: Optimal smoothing in nonparametric conditional quantile derivative function estimation (2015)
  17. Liu, Shouwei; Tse, Yiu-Kuen: Intraday value-at-risk: an asymmetric autoregressive conditional duration approach (2015)
  18. So, Mike K.P.; Chung, Ray S.W.: Statistical inference for conditional quantiles in nonlinear time series models (2015)
  19. White, Halbert; Kim, Tae-Hwan; Manganelli, Simone: VAR for VaR: measuring tail dependence using multivariate regression quantiles (2015)
  20. Yu, Jing-Rung; Chiou, Wan-Jiun Paul; Mu, Da-Ren: A linearized value-at-risk model with transaction costs and short selling (2015)

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