The dynamic quantile approach for VaR estimation: empirical evidence from Indonesia banking industry

References

• Adrian, T., & Brunnermeier, M. K. (2016). CoVaR. American Economic Review, 106(7), 1–11. https://doi.org/10.1257/aer.20120555
   Web of Science ®Google Scholar
• Al Janabi, M. A. (2007). On the use of value-at-risk for managing foreign-exchange exposure in large portfolios. The Journal of Risk Finance, 8(3), 260–287. https://doi.org/10.1108/15265940710750503
   Google Scholar
• Bautista, R. S., & Mora, N. (2021). Value-at-risk predictive performance: a comparison between the CaViaR and GARCH models for the MILA and ASEAN-5 stock market. Journal of Economics, Finance and Administrative Science, 26(52), 197–221. https://doi.org/10.1108/JEFAS-03-2021-0009
   Web of Science ®Google Scholar
• Berger, T., & Gençay, R. (2018). Improving daily value-at-risk forecasts: The relevance of short-run volatility for regulatory quality assessment. Journal of Economic Dynamics and Control, 92, 30–46. https://doi.org/10.1016/j.jedc.2018.03.016
   Web of Science ®Google Scholar
• Boothe, P., & Glassman, D. (1987). The statistical distribution of exchange rates: Empirical evidence and economic implications. Journal of International Economics, 22(3-4), 297–319. https://doi.org/10.1016/S0022-1996(87)80025-9
   Web of Science ®Google Scholar
• Chen, C. W. S., Gerlach, R., Hwang, B. B. K., & McAleer, M. (2012). Forecasting value-at-risk using nonlinear regression quantiles and the intra-day range. International Journal of Forecasting, 28(3), 557–574. https://doi.org/10.1016/j.ijforecast.2011.12.004
   Web of Science ®Google Scholar
• Davino, C., Furno, M., & Dan Vistocco, D. (2014). Quantile regression: Theory and applications. John Wiley.
   Google Scholar
• De Lange, P. E., Risstad, M., & Westgaard, S. (2022). Estimating value-at-risk using quantile regression and implied volatilities. Journal of Risk Model Validation, 16(1), 53–76.
   Web of Science ®Google Scholar
• Dimitrakopoulos, D. N., Kavussanos, M. G., & Spyrou, S. I. (2010). Value at risk models for volatile emerging markets equity portfolios. The Quarterly Review of Economics and Finance, 50(4), 515–526. https://doi.org/10.1016/j.qref.2010.06.006
   Google Scholar
• Dumitrescu, E.-I., Hurlin, C., & Pham, V. (2012). Backtesting value-at-risk: from dynamic quantile to dynamic binary tests. Finance, 33(1), 79–112. https://doi.org/10.3917/fina.331.0079
   Google Scholar
• Emenogu, N. G., Adenomon, M. O., & Nweze, N. O. (2020). On the volatility of daily stock returns of Total Nigeria Plc: Evidence from GARCH models, value-at-risk and backtesting. Financial Innovation, 6(1), 1–25. https://doi.org/10.1186/s40854-020-00178-1
   Web of Science ®Google Scholar
• Engle, R. F., & Manganelli, S. (2004). CAViaR: Conditional autoregressive value at risk by regression quantiles. Journal of Business & Economic Statistics, 22(4), 367–381. https://doi.org/10.1198/073500104000000370
   Web of Science ®Google Scholar
• Hansen, P. R., & Lunde, A. (2005). A forecast comparison of volatility models: Does anything beat a GARCH(1,1)? Journal of Applied Econometrics, 20(7), 873–889. https://doi.org/10.1002/jae.800
   Web of Science ®Google Scholar
• Hermsen, O. (2010). The impact of the choice of VaR models on the level of regulatory capital according to Basel II. Quantitative Finance, 10(10), 1215–1224. https://doi.org/10.1080/14697680903419701
   Web of Science ®Google Scholar
• Jiang, C., Ding, X., Xu, Q., & Tong, Y. (2020). A TVM-copula-MIDAS-GARCH model with applications to VaR-based portfolio selection. The North American Journal of Economics and Finance, 51, 101074. https://doi.org/10.1016/j.najef.2019.101074
   Web of Science ®Google Scholar
• Khan, M. S., Khan, K. I., Mahmood, S., & Sheeraz, M. (2019). Symmetric and asymmetric volatility clustering Via GARCH family models: An evidence from religion dominant countries. Paradigms, 13(1), 20–25.
   Google Scholar
• Koenker, R. (2005). Quantile regression. Cambridge University Press.
   Google Scholar
• Kuester, K., Mittnik, S., & Paolella, M. S. (2005). Value-at-risk prediction: a comparison of alternative strategies. Journal of Financial Econometrics, 4(1), 53–89. https://doi.org/10.1093/jjfinec/nbj002
   Google Scholar
• Naqvi, S. M. W. A., Khan, K. I., Ghafoor, M. M., & Rizvi, S. K. A. (2019). Evidence of volatility clustering and asymmetric behavior of returns in Asian emerging stock markets. Pakistan Economic and Social Review, 57(2), 163–197.
   Google Scholar
• Rubia, A., & Sanchis-Marco, L. (2013). On downside risk predictability through liquidity and trading activity: a dynamic quantile approach. International Journal of Forecasting, 29(1), 202–219. https://doi.org/10.1016/j.ijforecast.2012.09.001
   Web of Science ®Google Scholar
• Smolović, J. C., Lipovina-Božović, M., & Vujošević, S. (2017). GARCH models in value at risk estimation: Empirical evidence from the Montenegrin stock exchange. Economic Research-Ekonomska Istraživanja, 30(1), 477–498. https://doi.org/10.1080/1331677X.2017.1305773
   Web of Science ®Google Scholar
• Wu, D. (2020). CAViaR and the empirical study on China’s stock market. Journal of Physics: Conference Series, 1634(1), 012096. https://doi.org/10.1088/1742-6596/1634/1/012096
   Google Scholar
• Xiao, Z., Guo, H., & Lam, M. S. (2015). Quantile regression and value at risk. In C. F. Lee & J. Lee (Eds.), Handbook of financial econometrics and statistics (pp. 1143-1167). Springer. https://doi.org/10.1007/978-1-4614-7750-1_41
   Google Scholar