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.

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  1. Bonaccolto, Giovanni; Caporin, Massimiliano; Maillet, Bertrand B.: Dynamic large financial networks \textitviaconditional expected shortfalls (2022)
  2. de Castro, Luciano; Galvao, Antonio F.; Montes-Rojas, Gabriel; Olmo, Jose: Portfolio selection in quantile decision models (2022)
  3. Frezza, Massimiliano; Bianchi, Sergio; Pianese, Augusto: Forecasting value-at-risk in turbulent stock markets via the local regularity of the price process (2022)
  4. Kim, Minjo; Lee, Sangyeol: Risk measurement for conditionally heteroscedastic location-scale time series models with ASTD and AEPD innovations (2022)
  5. Lee, Sangyeol; Kim, Chang Kyeom: Test for conditional quantile change in GARCH models (2022)
  6. Lee, Sangyeol; Meintanis, Simos G.; Pretorius, Charl: Monitoring procedures for strict stationarity based on the multivariate characteristic function (2022)
  7. Montes-Rojas, Gabriel: Estimating impulse-response functions for macroeconomic models using directional quantiles (2022)
  8. Wang, Guochang; Zhu, Ke; Li, Guodong; Li, Wai Keung: Hybrid quantile estimation for asymmetric power GARCH models (2022)
  9. Zhao, Jun; Zhang, Yi; Wu, Sheng; Shen, Liming: Data-driven and distribution-free estimation of tailed-related risks for GARCH models using composite asymmetric least squares regression (2022)
  10. Cai, Yuzhi; Li, Guodong: A quantile function approach to the distribution of financial returns following TGARCH models (2021)
  11. Chao, Shih-Kang; Härdle, Wolfgang K.; Yuan, Ming: Factorisable multitask quantile regression (2021)
  12. Jiang, Yingying; Lin, Fuming; Zhou, Yong: The (k)th power expectile regression (2021)
  13. Kim, Minjoo; Yang, Junhong; Song, Pengcheng; Zhao, Yang: The dependence structure between equity and foreign exchange markets and tail risk forecasts of foreign investments (2021)
  14. Kithinji, Martin M.; Mwita, Peter N.; Kube, Ananda O.: Adjusted extreme conditional quantile autoregression with application to risk measurement (2021)
  15. Lee, Dong Jin; Kim, Tae-Hwan; Mizen, Paul: Impulse response analysis in conditional quantile models with an application to monetary policy (2021)
  16. Sekar, R.; Ravi, G.: Differential pulse code modulation and motion aligned optimal reconstruction for block-based compressive video sensing using conditional autoregressive-salp swarm algorithm (2021)
  17. Seyfi, Seyed Mohammad Sina; Sharifi, Azin; Arian, Hamidreza: Portfolio value-at-risk and expected-shortfall using an efficient simulation approach based on Gaussian mixture model (2021)
  18. Troster, Victor; Wied, Dominik: A specification test for dynamic conditional distribution models with function-valued parameters (2021)
  19. Wang, Zheng-Xin; Jv, Yue-Qi: A novel grey prediction model based on quantile regression (2021)
  20. Wu, Chuanzhen: Window effect with Markov-switching GARCH model in cryptocurrency market (2021)

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