A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates
Computers and Operations Research
Neural network ensemble strategies for financial decision applications
Computers and Operations Research
Forecasting the volatility of stock price index
Expert Systems with Applications: An International Journal
Credit risk assessment with a multistage neural network ensemble learning approach
Expert Systems with Applications: An International Journal
An experimental comparison of ensemble of classifiers for bankruptcy prediction and credit scoring
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Platform for China Energy & Environmental Policy Analysis: A general design and its application
Environmental Modelling & Software
Hi-index | 12.05 |
Although the rapid expansion in derivative market in previous decades has drawn research in both theory and practice of hedging against commodity risk, recent volatile fluctuations in crude oil prices in world market have renewed profound interest in examination of existing and development of new hedging models and strategies. In this paper, we propose and develop a methodological framework for applying individual and ensembles of polynomial projection models to hedge against oil commodity price risk. The study also comparatively evaluates the hedging performances of these projection models and benchmarks them against naive hedging, VEC-GARCH model, and the case of no hedging. In addition, the empirical analysis considers a trader's level of risk aversion in commodity hedging as well as the adoption of transaction cost. Our findings indicate promising out-of-sample hedging capability by polynomial projection models. Also, different forms of integrated ensembles of projections outperform individual polynomial projections, suggesting the usefulness of ensemble structure in enhancement of hedging in an uncertain environment.