The Dynamic Impact of Uncertainty in Causing and Forecasting the Distribution of Oil Returns and Risk
Giovanni Bonaccolto (),
Massimiliano Caporin () and
Rangan Gupta ()
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Giovanni Bonaccolto: Department of Statistical Sciences, University of Padova, via C. Battisti 241, 35121 Padova, Italy
No 201564, Working Papers from University of Pretoria, Department of Economics
The aim of the work is to analyse the relevance of recently developed news-based measures of economic policy uncertainty and equity market uncertainty in causing and predicting the conditional quantiles and distribution of the crude oil variations, defined both as returns and squared returns. For this purpose, on the one hand, we study the causality relations in quantiles through a nonparametric testing method; on the other hand, we forecast the conditional distribution on the basis of the quantile regression approach and the predictive accuracy is evaluated by means of several suitable tests. Given the presence of structural breaks over time, we implement a rolling window procedure to capture the dynamic relations among the variables.
Keywords: Granger Causality in Quantiles; Quantile Regression; Forecast of Oil Distribution; Foecast Evaluation (search for similar items in EconPapers)
JEL-codes: C58 C32 C53 Q02 Q35 (search for similar items in EconPapers)
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Journal Article: The dynamic impact of uncertainty in causing and forecasting the distribution of oil returns and risk (2018)
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