262
Views
0
CrossRef citations to date
0
Altmetric
Research Papers

A semi-parametric conditional autoregressive joint value-at-risk and expected shortfall modeling framework incorporating realized measures

, ORCID Icon &
Pages 309-334 | Received 15 Mar 2021, Accepted 02 Dec 2022, Published online: 05 Jan 2023

References

  • Acerbi, C. and Tasche, D., Expected shortfall: A natural coherent alternative to value at risk. Econ. Notes, 2002, 31(2), 379–388.
  • Andersen, T.G. and Bollerslev, T., Answering the skeptics: Yes, standard volatility models do provide accurate forecasts. Int. Econ. Rev., 1998, 39(4), 885–905.
  • Andersen, T.G., Bollerslev, T., Diebold, F.X. and Labys, P., Modeling and forecasting realized volatility. Econometrica, 2003, 71(2), 579–625.
  • Artzner, P., Delbaen, F., Eber, J.M. and Heath, D., Thinking coherently. Risk, 1997, 10, 68–71.
  • Artzener, P., Delbaen, F., Eber, J.M. and Heath, D., Coherent measures of risk. Math. Finance, 1999, 9, 203–228.
  • Basel Committee on Banking Supervision, Minimum Capital Requirements for Market Risk, 2019 (Bank for International Settlements).
  • Bollerslev, T., Generalized autoregressive conditional heteroskedasticity. J. Econom., 1986, 31, 307–327.
  • Chen, W., Peters, G., Gerlach, R. and Sisson, S., Dynamic quantile function models. arXiv:1707.02587, 2017.
  • Christensen, K. and Podolskij, M., Realized range-based estimation of integrated variance. J. Econom., 2007, 141(2), 323–349.
  • Contino, C. and Gerlach, R., Bayesian tail-risk forecasting using realized GARCH. Appl. Stoch. Models Bus. Ind., 2017, 33(2), 213–236.
  • Creal, D., Koopman, S.J. and Lucas, A., Generalized autoregressive score models with applications. J. Appl. Econom., 2013, 28(5), 777–795.
  • Engle, R.F., Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflations. Econometrica, 1982, 50, 987–1007.
  • Engle, R.F. and Manganelli, S., CAViaR: Conditional autoregressive value at risk by regression quantiles. J. Bus. Econ. Stat., 2004, 22(4), 367–381.
  • Fissler, T. and Ziegel, J.F., Higher order elicibility and Osband's principle. Ann. Stat., 2016, 44(4), 1680–1707.
  • Francq, C. and Zakoïan, J.M., Risk-parameter estimation in volatility models. J. Econom., 2015, 184(1), 158–173.
  • Gao, F. and Song, F., Estimation risk in GARCH VaR and ES estimates. Econom. Theory, 2008, 24(5), 1404–1424.
  • Gelman, A., Carlin, J.B., Stern, H.S. and Rubin, D.B., Bayesian Data Analysis (Vol. 2). 2014 (CRC Press: Boca Raton, FL).
  • Gerlach, R., Chen, C.W. and Chan, N.Y., Bayesian time-varying quantile forecasting for value-at-risk in financial markets. J. Bus. Econ. Stat., 2011, 29(4), 481–492.
  • Gerlach, R. and Wang, C., Forecasting risk via realized GARCH, incorporating the realized range. Quant. Finance, 2016, 16(4), 501–511.
  • Gerlach, R. and Wang, C., Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting, incorporating realized measures. Int. J. Forecast., 2020, 36(2), 489–506.
  • Gerlach, R. and Wang, C., Bayesian semi-parametric realized conditional autoregressive expectile models for tail risk forecasting. J. Financ. Econom., 2022, 20(1), 105–138.
  • Gneiting, T., Making and evaluating point forecasts. J. Am. Stat. Assoc., 2011, 106(494), 746–762.
  • Hansen, P.R. and Huang, Z., Exponential GARCH modeling with realized measures of volatility. J. Bus. Econ. Stat., 2016, 34(2), 269–287.
  • Hansen, P.R., Huang, Z. and Shek, H.H., Realized GARCH: A joint model for returns and realized measures of volatility. J. Appl. Econom., 2012, 27(6), 877–906.
  • Hansen, P.R., Lunde, A. and Nason, J.M., The model confidence set. Econometrica, 2011, 79(2), 453–497.
  • Harvey, A.C., Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series (Vol. 52). 2013 (Cambridge University Press: Cambridge).
  • Harvey, A.C. and Chakravarty, T., Beta-t-EGARCH. Working Paper. Earlier version appeared in 2008 as a Cambridge Working Paper in Economics, CWPE 0840, 2009,
  • Koenker, R. and Machado, J.A., Goodness of fit and related inference processes for quantile regression. J. Am. Stat. Assoc., 1999, 94(448), 1296–1310.
  • Martens, M. and van Dijk, D., Measuring volatility with the realized range. J. Econom., 2007, 138(1), 181–207.
  • Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. and Teller, E., Equation of state calculations by fast computing machines. J. Chem. Phys., 1953, 21, 1087–1092.
  • Nolde, N. and Ziegel, J.F., Elicitability and backtesting: Perspectives for banking regulation. Ann. Appl. Stat., 2017, 11(4), 1833–1874.
  • Patton, A.J., Ziegel, J.F. and Chen, R., Dynamic semiparametric models for expected shortfall (and value-at-risk). J. Econom., 2019, 211(2), 388–413.
  • Roberts, G.O., Gelman, A. and Gilks, W.R., Weak convergence and optimal scaling of random walk Metropolis algorithms. Ann. Appl. Probab., 1997, 7(1), 110–120.
  • Taylor, J., Forecasting value at risk and expected shortfall using a semiparametric approach based on the asymmetric Laplace distribution. J. Bus. Econ. Stat., 2019, 37(1), 121–133.
  • Watanabe, T., Quantile forecasts of financial returns using realized GARCH models. Jpn. Econ. Rev., 2012, 63(1), 68–80.
  • Zhang, L., Mykland, P.A. and Aït-Sahalia, Y., A tale of two time scales. J. Am. Stat. Assoc., 2005, 100(472), 1394–1411.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.