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• W2959096728 endingPage "101022" @default.
• W2959096728 startingPage "101022" @default.
• W2959096728 abstract "Abstract To assess the potential nonlinear predictive impact of crude oil price volatility on aggregate equity return volatility, we consider autoregressions of monthly aggregate equity return realized volatility augmented with nonlinear transformations of crude oil price realized volatility and evaluate if they improve point forecasts. Out-of-sample results based on data from 1885 m 1 through 1895 m 12 and from 1983 m 1 through 2017 m 12 illustrate that our conclusions depend heavily on the notion of forecast improvement. At the population level, the null hypothesis of no out-of-sample predictability from crude oil price realized volatility to aggregate equity return realized volatility is rejected for the linear as well as certain nonlinear specifications. On the other hand, the null hypothesis of finite-sample equal predictive ability is rejected less frequently. Among the range of models, the autoregression augmented with the one-year net crude oil price realized volatility increase is the top performer, producing statistically significant more accurate point forecasts than the benchmark." @default.
• W2959096728 created "2019-07-23" @default.
• W2959096728 creator A5019675170 @default.
• W2959096728 date "2019-11-01" @default.
• W2959096728 modified "2023-10-16" @default.
• W2959096728 title "Forecasting aggregate equity return volatility using crude oil price volatility: The role of nonlinearities and asymmetries" @default.
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• W2959096728 doi "https://doi.org/10.1016/j.najef.2019.101022" @default.
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