References
- 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 - Aziz, S., & Dowling, M. (2019). Machine learning and AI for risk management. Disrupting finance: FinTech and strategy in the 21st century, 33–50.
- Basel Committee. (1996). Overview of the Amendment to the Capital Accord to Incorporate Market Risks. Discussion Paper, Basel Committee on Banking Supervision.
- Bayer, S. (2018). Combining value-at-risk forecasts using penalized quantile regressions. Econometrics and statistics, 8, 56–77.
https://doi.org/10.1016/j.ecosta.2017.08.001 - BCBS (1996). Supervisory Framework for the Use of ‘Backtesting’ in Conjunction with the Internal Models Approach to Market Risk Capital Requirements.
- 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. (1986). Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics. 31(3), 307–327.
https://doi.org/10.1016/0304-4076(86)90063-1 - 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 - Christoffersen, P. (1998). Evaluating interval forecasts. International Economic Review, 39(4), 841–862.
https://doi.org/10.2307/2527341 - 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. (2013). The new market-risk regulations. VoxEU.
- 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 - Duffie, D., Pan, J. (1997). An overview of value at risk. Journal of Derivatives, 4(3), 7–49.
http://doi.org/10.3905/jod.1997.407971 - 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 - Hansen, P. R., Lunde, A., Nason, J. M. (2011). The model confidence set. Econometrica, 79(2), 453–497.
https://doi.org/10.3982/ECTA5771 - Holthausen, D. M., Hughes, J. S. (1978). Commodity returns and capital asset pricing. Financial Management, 37–44.
https://doi.org/10.1177/0972262912460186 - Huang, H., Lee, T. H. (2013). Forecasting value-at-risk using high-frequency information. Econometrics, 1(1), 127–140.
https://doi.org/10.3390/econometrics1010127 - 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 - Laporta, A. G., Merlo, L., & Petrella, L. (2018). Selection of value at risk models for energy commodities. Energy Economics 74, 628–643.
- 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 - Lyócsa, Š., Todorova, N., & Výrost, T. (2021). Predicting risk in energy markets: low-frequency data still matter. Applied Energy, 282, 116–146.
- 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 - Meinshausen, N., Ridgeway, G. (2006). Quantile regression forests. Journal of Machine Learning Research, 7(6).
- 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 - Pradeepkumar, D., & Ravi, V. (2017). Forecasting financial time series volatility using particle swarm optimisation trained quantile regression neural network. Applied Soft Computing, 58, 35–52.
https://doi.org/10.1016/j.asoc.2017.04.014 - Rundo, F., Trenta, F., di Stallo, A. L., & Battiato, S. (2019). Machine learning for quantitative finance applications: A survey. Applied Sciences, 9(24), 5574.
- Stuermer, M., & Valckx, N. (2021). Four Factors Behind the Metals Price Rally. IMF.
- 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 - Taylor, J. W. (2020). Forecast combinations for value at risk and expected shortfall. International Journal of Forecasting, 36(2), 428–441.
https://doi.org/10.1016/j.ijforecast.2019.05.014 - 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 - Timmermann, A. (2006). Forecast combinations. Handbook of economic forecasting, 1, 135–196.
https://doi.org/10.1016/S1574-0706(05)01004-9 - Tsay, R. S. (2005). Analysis of Financial Time Series (Vol. 543). John Wiley & Sons.
- 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
