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 110 articles )

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  1. Berger, Theo; Gençay, Ramazan: Improving daily value-at-risk forecasts: the relevance of short-run volatility for regulatory quality assessment (2018)
  2. Blasques, Francisco; Gorgi, Paolo; Koopman, Siem Jan; Wintenberger, Olivier: Feasible invertibility conditions and maximum likelihood estimation for observation-driven models (2018)
  3. De Luca, Giovanni; Rivieccio, Giorgia; Corsaro, Stefania: A copula-based quantile model (2018)
  4. El Adlouni, Salaheddine; Salaou, Garba; St-Hilaire, André: Regularized Bayesian quantile regression (2018)
  5. Faria, Adriano; Almeida, Caio: A hybrid spline-based parametric model for the yield curve (2018)
  6. Gregory, Karl B.; Lahiri, Soumendra N.; Nordman, Daniel J.: A smooth block bootstrap for quantile regression with time series (2018)
  7. Naguez, Naceur: Dynamic portfolio insurance strategies: risk management under Johnson distributions (2018)
  8. Scheller, Felix; Auer, Benjamin R.: How does the choice of Value-at-Risk estimator influence asset allocation decisions? (2018)
  9. Tsiotas, Georgios: A Bayesian encompassing test using combined value-at-risk estimates (2018)
  10. Xu, Qifa; Cai, Chao; Jiang, Cuixia; Sun, Fang; Huang, Xue: Sampling Lasso quantile regression for large-scale data (2018)
  11. Zhu, Qianqian; Zheng, Yao; Li, Guodong: Linear double autoregression (2018)
  12. Beckers, Benjamin; Herwartz, Helmut; Seidel, Moritz: Risk forecasting in (T)GARCH models with uncorrelated dependent innovations (2017)
  13. Boudt, Kris; Laurent, Sébastien; Lunde, Asger; Quaedvlieg, Rogier; Sauri, Orimar: Positive semidefinite integrated covariance estimation, factorizations and asynchronicity (2017)
  14. De Gooijer, Jan G.: Elements of nonlinear time series analysis and forecasting (2017)
  15. Du, Jiangze; Lai, Kin Keung: Copula-based risk management models for multivariable RMB exchange rate in the process of RMB internationalization (2017)
  16. Gerlach, Richard; Walpole, Declan; Wang, Chao: Semi-parametric Bayesian tail risk forecasting incorporating realized measures of volatility (2017)
  17. Montes-Rojas, Gabriel: Reduced form vector directional quantiles (2017)
  18. Pitselis, Georgios: Risk measures in a quantile regression credibility framework with Fama/French data applications (2017)
  19. Taylor, James W.: Probabilistic forecasting of wind power ramp events using autoregressive logit models (2017)
  20. Wang, Gang-Jin; Xie, Chi; He, Kaijian; Stanley, H. Eugene: Extreme risk spillover network: application to financial institutions (2017)

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