Forecasting the realized variance of oil-price returns: a disaggregated analysis of the role of uncertainty and geopolitical risk

被引：18
|
作者：
Gupta, Rangan [1 ]
Pierdzioch, Christian [2 ]
机构：
[1] Univ Pretoria, Dept Econ, Private Bag X20, ZA-0028 Hatfield, South Africa
[2] Helmut Schmidt Univ, Dept Econ, Holstenhofweg 85,POB 700822, D-22008 Hamburg, Germany
来源：
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH | 2022年 / 29卷 / 34期
基金：
英国科研创新办公室;
关键词：
Realized variance; Oil price; Forecasting; Machine learning; Uncertainty; Geopolitical risk; CRUDE-OIL; VOLATILITY; MODELS; IMPACT;
D O I：
10.1007/s11356-022-19152-8
中图分类号：
X [环境科学、安全科学];
学科分类号：
08 ; 0830 ;
摘要：
We contribute to the empirical literature on the predictability of oil-market volatility by comparing the predictive role of aggregate versus several disaggregated metrics of policy-related and equity-market uncertainties of the USA and geopolitical risks for forecasting the future realized volatility of oil-price (WTI) returns over the monthly period from 1985:01 to 2021:08. Using machine-learning techniques, we find that adding the disaggregated metrics to the array of predictors improves the accuracy of forecasts at intermediate and long forecast horizons, and mainly when we use random forests to estimate our forecasting model.
引用
收藏
页码：52070 / 52082
页数：13
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