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Modeling and forecasting the oil volatility index


  • Mariti, Massimo B.
  • Gonçalves Mazzeu, Joao Henrique
  • Lopes Moreira Da Veiga, María Helena


This paper models and forecasts the crude oil ETF volatility index (OVX). Themotivation lies on the evidence that the OVX has been used in the last years as an important alternative measure to track and analyze the volatility of future oil prices. The analysis of the OVX suggests that it presents similar features to those of the daily market volatility index. The main characteristic is the long range dependence that is modeled either by autoregressive fractional integrated moving averaging (ARFIMA) models or by heterogeneous autoregressive (HAR) specifications. Regarding the latter family of models, we first propose extensions of the HAR model that are based on the net and scale measures of oil prices changes. The aim is to improve the HAR model by including predictors that better capture the impact of oil price changes on the economy. Second, we test the forecasting performance of the new proposals and benchmarks with the model confidence set (MCS) and the Generalized-AutoContouR (G-ACR) tests interms of point forecasts and density forecasting, respectively. Our main findings are as follows: the new asymmetric proposals have superior predictive ability than the heterogeneous autoregressive leverage (HARL) model under two known loss functions. Regarding density forecasting, the best model is the one that includes the scale measureas a proxy of oil price changes and considers a flexible distribution for the errors.

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  • Mariti, Massimo B. & Gonçalves Mazzeu, Joao Henrique & Lopes Moreira Da Veiga, María Helena, 2017. "Modeling and forecasting the oil volatility index," DES - Working Papers. Statistics and Econometrics. WS 25985, Universidad Carlos III de Madrid. Departamento de Estadística.
  • Handle: RePEc:cte:wsrepe:25985

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    References listed on IDEAS

    1. Adam Clements & Joanne Fuller, 2012. "Forecasting increases in the VIX: A time-varying long volatility hedge for equities," NCER Working Paper Series 88, National Centre for Econometric Research.
    2. Chia-Lin Chang & Michael Mcaleer, 2009. "Daily Tourist Arrivals, Exchange Rates and Voatility for Korea and Taiwan," Korean Economic Review, Korean Economic Association, vol. 25, pages 241-267.
    3. Basher, Syed A. & Sadorsky, Perry, 2006. "Oil price risk and emerging stock markets," Global Finance Journal, Elsevier, vol. 17(2), pages 224-251, December.
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    More about this item


    Heterogeneous autoregression;

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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