Enhancing the predictability of crude oil markets with hybrid wavelet approaches. (English) Zbl 1418.91398
Summary: We explore the robustness, efficiency and accuracy of the multi-scale forecasting in crude oil markets. We adopt a novel hybrid wavelet auto-ARMA model to detect the inherent nonlinear dynamics of crude oil returns with an explicitly defined hierarchical structure. Entropic estimation is employed to calculate the optimal level of the decomposition. The wavelet-based forecasting method accounts for the chaotic behavior of oil series, whilst captures drifts, spikes and other non-stationary effects which common frequency-domain methods miss out completely. These results shed new light upon the predictability of crude oil markets in nonstationary settings.
MSC:
| 91B74 | Economic models of real-world systems (e.g., electricity markets, etc.) |
| 42C40 | Nontrigonometric harmonic analysis involving wavelets and other special systems |
| 62P20 | Applications of statistics to economics |
| 62M20 | Inference from stochastic processes and prediction |
References:
| [1] | Aguiar-Conraria, L.; Soares, M. J., The continuous wavelet transform: moving beyond uni and bivariate analysis, J. Econ. Surv., 28, 2, 344-375 (2014) |
| [2] | Bekiros, S.; Marcellino, M., The multiscale causal dynamics of foreign exchange markets, J. Int. Money Finance, 33, 282-305 (2013) |
| [3] | Bekiros, S.; Nguyen, D. K.; Uddin, G. S.; Sjö, B., Business cycle (de) synchronization in the aftermath of the global financial crisis: implications for the Euro area, Stud. Nonlinear Dynam. Econ., 19, 5, 609-624 (2015) · Zbl 1506.62482 |
| [5] | Bianchi, D., Büchner, M., Tamoni, A., 2018. Bond Risk Premia with Machine Learning. Available at SSRN 3232721.; Bianchi, D., Büchner, M., Tamoni, A., 2018. Bond Risk Premia with Machine Learning. Available at SSRN 3232721. |
| [6] | Bianchi, D., Tamoni, A., 2016. The dynamics of expected returns: Evidence from multi-scale time series modeling. Available at SSRN 2684728.; Bianchi, D., Tamoni, A., 2016. The dynamics of expected returns: Evidence from multi-scale time series modeling. Available at SSRN 2684728. |
| [9] | Ghysels, E.; Santa-Clara, P.; Valkanov, R., The MIDAS Touch: Mixed Data Sampling Regression Models (2004) |
| [10] | Gu, S., Kelly, B., Xiu, D., 2018. Empirical asset pricing via machine learning. National Bureau of Economic Research, No. w25398.; Gu, S., Kelly, B., Xiu, D., 2018. Empirical asset pricing via machine learning. National Bureau of Economic Research, No. w25398. |
| [11] | Hyndman, R. J.; Ahmed, R. A.; Athanasopoulos, G.; Shang, H. L., Optimal combination forecasts for hierarchical time series, Comput. Statist. Data Anal., 55, 9, 2579-2589 (2011) · Zbl 1464.62095 |
| [12] | Khandakar, Y.; Hyndman, R. J., Automatic time series forecasting: the forecast Package for R, J. Stat. Softw., 27, 03, 1-22 (2008) |
| [13] | Schorfheide, F.; Song, D., Real-time forecasting with a mixed-frequency VAR, J. Bus. Econom. Statist., 33, 3, 366-380 (2015) |
| [14] | Zhang, K.; Gençay, R.; Yazgan, M. E., Application of wavelet decomposition in time-series forecasting, Econom. Lett., 158, 41-46 (2017) · Zbl 1396.62221 |
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
