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The Dynamic Impact of Uncertainty in Causing and Forecasting the Distribution of Oil Returns and Risk

Author

Listed:
  • Giovanni Bonaccolto

    (Department of Statistical Sciences, University of Padova, via C. Battisti 241, 35121 Padova, Italy)

  • Massimiliano Caporin

    (Department of Economics and Management “Marco Fanno”, University of Padova, via del Santo 33, 35123 Padova, Italy.)

  • Rangan Gupta

    (Department of Economics, University of Pretoria)

Abstract

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.

Suggested Citation

  • Giovanni Bonaccolto & Massimiliano Caporin & Rangan Gupta, 2015. "The Dynamic Impact of Uncertainty in Causing and Forecasting the Distribution of Oil Returns and Risk," Working Papers 201564, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201564
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    More about this item

    Keywords

    Granger Causality in Quantiles; Quantile Regression; Forecast of Oil Distribution; Foecast Evaluation;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market
    • Q35 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Nonrenewable Resources and Conservation - - - Hydrocarbon Resources

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