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 167 articles )

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  1. Cai, Yuzhi; Li, Guodong: A quantile function approach to the distribution of financial returns following TGARCH models (2021)
  2. Chao, Shih-Kang; Härdle, Wolfgang K.; Yuan, Ming: Factorisable multitask quantile regression (2021)
  3. Jiang, Yingying; Lin, Fuming; Zhou, Yong: The (k) th power expectile regression (2021)
  4. Kithinji, Martin M.; Mwita, Peter N.; Kube, Ananda O.: Adjusted extreme conditional quantile autoregression with application to risk measurement (2021)
  5. Wang, Zheng-Xin; Jv, Yue-Qi: A novel grey prediction model based on quantile regression (2021)
  6. Zhu, Qianqian; Li, Guodong; Xiao, Zhijie: Quantile estimation of regression models with GARCH-X errors (2021)
  7. Ando, Tomohiro; Bai, Jushan: Quantile co-movement in financial markets: a panel quantile model with unobserved heterogeneity (2020)
  8. Bruzda, Joanna: Multistep quantile forecasts for supply chain and logistics operations: bootstrapping, the GARCH model and quantile regression based approaches (2020)
  9. Gerlach, Richard; Naimoli, Antonio; Storti, Giuseppe: Time-varying parameters realized GARCH models for tracking attenuation bias in volatility dynamics (2020)
  10. He, Yi; Hou, Yanxi; Peng, Liang; Shen, Haipeng: Inference for conditional value-at-risk of a predictive regression (2020)
  11. Jiménez, Inés; Mora-Valencia, Andrés; Perote, Javier: Risk quantification and validation for Bitcoin (2020)
  12. Meng, Xiaochun; Taylor, James W.: Estimating value-at-risk and expected shortfall using the intraday low and range data (2020)
  13. Nguyen, Giang; Engle, Robert; Fleming, Michael; Ghysels, Eric: Liquidity and volatility in the U.S. Treasury market (2020)
  14. Tay, Hao-Zhe; Ng, Kok-Haur; Koh, You-Beng; Ng, Kooi-Huat: Model selection based on value-at-risk backtesting approach for GARCH-type models (2020)
  15. Uwilingiyimana, Charline; Diongue, Abdou Kâ; Ogouyandjou, Carlos: Adaptive hyperbolic asymmetric power ARCH (A-HY-APARCH) model: stability and estimation (2020)
  16. Wu, Xiaofei; Zhu, Shuzhen; Zhou, Junjie: Research on RMB exchange rate volatility risk based on MSGARCH-VaR model (2020)
  17. Alaudin, Ros Idayuwati; Ismail, Noriszura; Isa, Zaidi: Retirement consumption puzzle in Malaysia: evidence from Bayesian quantile regression model (2019)
  18. Altun, Emrah: Two-sided exponential-geometric distribution: inference and volatility modeling (2019)
  19. Amédée-Manesme, Charles-Olivier; Barthélémy, Fabrice; Maillard, Didier: Computation of the corrected Cornish-Fisher expansion using the response surface methodology: application to \textitVaRand \textitCVaR (2019)
  20. Bu, Di; Liao, Yin; Shi, Jing; Peng, Hongfeng: Dynamic expected shortfall: a spectral decomposition of tail risk across time horizons (2019)

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