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The dynamic impact of uncertainty in causing and forecasting the distribution of oil returns and risk

G. Bonaccolto, Massimiliano Caporin () and Rangan Gupta ()

Physica A: Statistical Mechanics and its Applications, 2018, vol. 507, issue C, 446-469

Abstract: The aim of this study is to analyze the relevance of recently developed news-based measures of economic policy and equity market uncertainty in causing and predicting the conditional quantiles of crude oil returns and risk. For this purpose, we studied both the causality relationships in quantiles through a non-parametric testing method and, building on a collection of quantiles forecasts, we estimated the conditional density of oil returns and volatility, the out-of-sample performance of which was evaluated by using suitable tests. A dynamic analysis shows that the uncertainty indexes are not always relevant in causing and forecasting oil movements. Nevertheless, the informative content of the uncertainty indexes turns out to be relevant during periods of market distress, when the role of oil risk is the predominant interest, with heterogeneous effects over the different quantiles levels.

Keywords: Granger causality in quantiles; Quantile regression; Forecast of oil distribution; Forecast evaluation (search for similar items in EconPapers)
Date: 2018
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Related works:
Working Paper: The Dynamic Impact of Uncertainty in Causing and Forecasting the Distribution of Oil Returns and Risk (2015)
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