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A common factor of stochastic volatilities between oil and commodity prices

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  • Eunhee Lee
  • Doo Bong Han
  • Rodolfo M. Nayga

Abstract

This article analyses the multivariate stochastic volatilities (SVs) with a common factor influencing volatilities in the prices of crude oil and agricultural commodities, used for both biofuel and nonbiofuel purposes. Modelling the volatility is crucial because the volatility is an important variable for asset allocation, risk management and derivative pricing. We develop a SV model comprising a latent common volatility factor with two asymptotic regimes with a smooth transition between them. In contrast to conventional volatility models, SVs are generated by the logistic transformation of latent factors, which comprise two components: the common volatility factor and an idiosyncratic component. We present a SV model with a common factor for oil, corn and wheat from 8 August 2005 to 10 October 2014, using a Markov chain Monte Carlo method to estimate the SVs and extract the common volatility factor. We find that the volatilities of oil and grain markets are persistent. According to the estimated common volatility factor, high volatility periods match the 2007–2009 recession and the 2007–2008 financial crisis quite well. Finally, the extracted common volatility factor exhibits a distinct pattern.

Suggested Citation

  • Eunhee Lee & Doo Bong Han & Rodolfo M. Nayga, 2017. "A common factor of stochastic volatilities between oil and commodity prices," Applied Economics, Taylor & Francis Journals, vol. 49(22), pages 2203-2215, May.
  • Handle: RePEc:taf:applec:v:49:y:2017:i:22:p:2203-2215
    DOI: 10.1080/00036846.2016.1234701
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    Cited by:

    1. Hualin Xie & Bohao Wang, 2017. "An Empirical Analysis of the Impact of Agricultural Product Price Fluctuations on China’s Grain Yield," Sustainability, MDPI, vol. 9(6), pages 1-14, May.

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