Combining forecasts? Keep it simple

• Authors: Szymon Lis , Marcin Chlebus

Abstracts

EN

This study contrasts GARCH models with diverse combined forecast techniques for Commodities Value at Risk (VaR)modeling, aiming to enhance accuracy and provide novel insights. Employing daily returns data from 2000 to 2020 forgold, silver, oil, gas, and copper, various combination methods are evaluated using the Model Confidence Set (MCS) procedure. Results show individual models excel in forecasting VaR at a 0.975 confidence level, while combined methods outperform at 0.99 confidence. Especially during high uncertainty, as during COVID-19, combined forecasts prove more effective. Surprisingly, simple methods such as mean or lowest VaR yield optimal results, highlighting their efficacy. This study contributes by offering a broad comparison of forecasting methods, covering a substantial period, and dissecting crisis and prosperity phases. This advances understanding in financial forecasting, benefiting both academia and practitioners.

Keywords

EN

Machine learning; GARCH model; combined forecasts; commodities; VaR

• Publisher: Uniwersytet Warszawski. Wydział Nauk Ekonomicznych
• Journal: Central European Economic Journal
• Year: 2023
• Volume: 10
• Issue: 57
• Pages: 343-370

Dates

published

2023

Contributors

author

Szymon Lis

• University of Warsaw

author

Marcin Chlebus

• University of Warsaw

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

Identifiers

DOI

10.2478/ceej-2023-0020

Biblioteka Nauki

22443122