Andreani, M., Candila, V., & Petrella, L. (2022). Quantile Regression Forest for Value-at-Risk Forecasting Via Mixed-Frequency Data. In Mathematical and Statistical Methods for Actuarial Sciences and Finance: MAF 2022 (pp. 13–18). Cham: Springer International Publishing. http://doi.org/10.1007/978-3-030-99638-3
Angabini, A., Wasiuzzaman, S. (2011). GARCH Models and the Financial Crisis: A Study of the Malaysian. The International Journal of Applied Economics and Finance, 5(3), 226–236. https://doi.org/10.3923/ijaef.2011.226.236
Armstrong, J. S. (1989). Combining forecasts: The end of the beginning or the beginning of the end? International Journal of Forecasting, 5(4), 585–588. https://doi.org/10.1016/0169-2070(89)90013-7
Basel Committee. (1996). Overview of the Amendment to the Capital Accord to Incorporate Market Risks. Discussion Paper, Basel Committee on Banking Supervision.
BCBS (2010). The Basel III Capital Framework: A Decisive Breakthrough. Speech by Hervé Hannoun at BoJ-BIS High Level Seminar on Financial Regulatory Reform: Implications for Asia and the Pacific, Hong Kong SAR.
Bernardi, M., Catania, L. (2016). Comparison of Value-at-Risk models using the MCS approach. Computational Statistics, 31(2), 579–608. https://doi.org/10.1007/s00180-016-0646-6
Bhowmik, R., & Wang, S. (2020). Stock market volatility and return analysis: A systematic literature review. Entropy, 22(5), 522. https://doi.org/10.3390/e22050522
Bollerslev, T. (1987). Conditionally heteroskedastic time series model for speculative prices and rates of return. The Review of Economics and Statistics, 69(3), 542–547. https://doi.org/10.2307/1925546
Bollerslev, T., Woolridge, J. M. (1992). Quasi-maximum likelihood estimation and inference in dynamic models with time-varying covariances Econometric Reviews 11. https://doi.org/10.1080/07474939208800229
Buczyński, M., Chlebus, M. (2018). Comparison of semi-parametric and benchmark value-at-risk models in several time periods with different volatility levels. e-Finanse: Financial Internet Quarterly, 14(2), 67–82. https://doi.org/10.2478/fiqf-2018-0013
Buczyński, M., & Chlebus, M. (2019). Old-fashioned parametric models are still the best: a comparison of value-at-risk approaches in several volatility states. Journal of Risk Model Validation, 14(2).
Caillault, É. P., Lefebvre, A., and Bigand, A. (2017). Dynamic time warping-based imputation for univariate time series data. Pattern Recognition Letters. https://doi.org/10.1016/j.patrec.2017.08.019
Cannon, A. J. (2010). A flexible nonlinear modelling framework for nonstationary generalized extreme value analysis in hydroclimatology. Hydrological Processes: An International Journal, 24(6), 673–685. https://doi.org/10.1002/hyp.7506
Cannon, A. J. (2011). Quantile regression neural networks: Implementation in R and application to precipitation downscaling. Computers & Geosciences, 37(9), 1277–1284. https://doi.org/10.1016/j.cageo.2010.07.005
Clemen, R. T., Winkler, R. L. (1986). Combining economic forecasts. Journal of Business & Economic Statistics, 4(1), 39–46. https://doi.org/10.2307/1391385
Danielsson, J., Morimoto, Y. (2000). Forecasting extreme financial risk: A critical analysis of practical methods for the Japanese market. Institute for Monetary and Economic Studies, Bank of Japan.
Dudziński, J. (2016). Ceny w handlu międzynarodowym w drugiej dekadzie XXI wieku. Kierunki zmian i ich czynniki. International Business and Global Economy, 35(2), 249–260. https://doi.org/10.4467/23539496IB.16.061.5642
Engle, R. F., Manganelli, S. (2004). CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles. Journal of Business & Economic Statistics, 22(4), 367–381. http://doi.org/10.1198/073500104000000370
Fameliti, S. P., & Skintzi, V. D. (2020). Predictive ability and economic gains from volatility forecast combinations. Journal of Forecasting, 39(2), 200–219. http://doi.org/10.1002/for.2622
Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of Statistics, 1189–1232. http://dx.doi.org/10.1214/aos/1013203451
Gençay, R., Selçuk, F., Ulugülyaǧci, A. (2003). High volatility, thick tails and extreme value theory in value-at-risk estimation. Insurance: Mathematics and Economics, 33(2), 337–356. http://dx.doi.org/10.1016/j.insmatheco.2003.07.004
Giacomini, R., Komunjer, I. (2005). Evaluation and combination of conditional quantile forecasts. Journal of Business and Economic Statistics, 23(4), 416–431. http://doi.org/10.1198/073500105000000018
Grömping, U. (2009). Variable importance assessment in regression: linear regression versus random forest. The American Statistician, 63(4), 308–319. https://doi.org/10.1198/tast.2009.08199
Halbleib, R., Pohlmeier, W. (2012). Improving the value at risk forecasts: Theory and evidence from the financial crisis. Journal of Economic Dynamics and Control, 36(8), 1212–1228. https://doi.org/10.1016/j.jedc.2011.10.005
Ichev, R., Marinč, M. (2018). Stock prices and geographic proximity of information: Evidence from the Ebola outbreak. International Review of Financial Analysis, 56, 153–166. https://doi.org/10.1016/j.irfa.2017.12.004
Jeon, J., Taylor, J. W. (2013). Using CAViaR models with implied volatility for Value-at-Risk estimation. Journal of Forecasting, 32(1), 62–74. http://dx.doi.org/10.1002/for.1251
Kupiec, P. (1995). Techniques for verifying the accuracy of risk management models. Journal of Derivatives, 3 (2), 73–84. https://doi.org/10.3905/jod.1995.407942
Laurent, S., Rombouts, J. V., & Violante, F. (2012). On the forecasting accuracy of multivariate GARCH models. Journal of Applied Econometrics, 27(6), 934–955. https://doi.org/10.1002/jae.1248
Mashrur, A., Luo, W., Zaidi, N. A., & Robles-Kelly, A. (2020). Machine learning for financial risk management: a survey. IEEE Access, 8, 203203–203223.
McAleer, M., Jimenez-Martin, J. A., Perez Amaral, T. (2010). Has the Basel II Accord encouraged risk management during the 2008–09 financial crisis? SSRN Electronic Journal, http://dx.doi.org/10.2139/ssrn.1397239
Mensi, W., Sensoy, A., Vo, X. V., Kang, S. H. (2020). Impact of COVID-19 outbreak on asymmetric multifractality of gold and oil prices. Resources Policy, 69, 101829. https://doi.org/10.1016%2Fj.resourpol.2020.101829
Phillips, P. C., Yu, J. (2011). Dating the timeline of financial bubbles during the subprime crisis. Quantitative Economics, 2(3), 455–491. http://dx.doi.org/10.3982/QE82
Parot, A., Michell, K., & Kristjanpoller, W. D. (2019). Using Artificial Neural Networks to forecast Exchange Rate, including VAR-VECM residual analysis and prediction linear combination. Intelligent Systems in Accounting, Finance and Management, 26(1), 3–15. https://doi.org/10.1002/isaf.1440
Rundo, F., Trenta, F., di Stallo, A. L., & Battiato, S. (2019). Machine learning for quantitative finance applications: A survey. Applied Sciences, 9(24), 5574.
Szakmary, A. C., Shen, Q., Sharma, S. C. (2010). Trend-following trading strategies in commodity futures: A re-examination. Journal of Banking & Finance, 34(2), 409–426. http://dx.doi.org/10.1016/j.jbankfin.2009.08.004
Tibshirani, R. (1996). Regression shrinkage and selection via the LASSO. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x
Terui, N., Van Dijk, H. K. (2002). Combined forecasts from linear and nonlinear time series models. International Journal of Forecasting, 18(3), 421–438. https://doi.org/10.1016/S0169-2070(01)00120-0
Tse, Y. (2016). Asymmetric volatility, skewness, and downside risk in different asset classes: Evidence from futures markets. Financial Review, 51(1), 83–111. https://doi.org/10.1111/fire.12095
Wasserbacher, H., & Spindler, M. (2022). Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls. Digital Finance, 4(1), 63–88.
Xiao, D., Su, J., & Ayub, B. (2022). Economic policy uncertainty and commodity market volatility: implications for economic recovery. Environmental Science and Pollution Research, 29(40), 60662–60673.
Youssef, M., Belkacem, L., Mokni, K., 2015. Value-at-Risk estimation of energy commodities: A long-memory GARCH–EVT approach. Energy Economics, 51, 99–110. https://doi.org/10.1016/j.eneco.2015.06.010