The crude oil price bubbling and universal scaling dynamics of price volatility

The main goal of this paper is to reveal the effect of crude oil price bubbling on the price volatility dynamics. For this purpose, the time series of volatility at different horizons are mapped into a model of kinetic roughening of interface growing in a stochastic environment. In this way, we found that the volatility dynamics obeys the Family-Viscek dynamic scaling ansatz. Although during the period from January 2, 1986 to July 25, 2014 the volatility remains a slightly anti-persistent, the dynamic exponent is found to be quite different during different regimes of price evolution. Accordingly, we define the intrinsic time of price volatility and metric of volatility horizons. This allows us to construct the Langevin-type equation governing the volatility dynamics during bubble and non-bubble periods. The data analysis indicates that the bubbling does not affect the intrinsic time of volatility, but strongly affect the metric of volatility horizons. In this regard, numerical data suggest the existence of two universal metrics characterizing the volatility dynamics during the bubble and non-bubble regimes of crude oil price evolution, respectively. The results of this work help us to get a further insight into the dynamics of crude oil price volatility.

[1]  Zhi-Qiang Jiang,et al.  Testing the weak-form efficiency of the WTI crude oil futures market , 2012, 1211.4686.

[2]  Jose Alvarez-Ramirez,et al.  Multifractal Hurst analysis of crude oil prices , 2002 .

[3]  Afees A. Salisu,et al.  Modelling oil price volatility with structural breaks , 2013 .

[4]  Li Liu,et al.  A study of correlations between crude oil spot and futures markets: A rolling sample test , 2011 .

[5]  Apostolos Serletis,et al.  The Hurst exponent in energy futures prices , 2007 .

[6]  Jose Alvarez-Ramirez,et al.  Asymmetric long-term autocorrelations in crude oil markets , 2015 .

[7]  Yudong Wang,et al.  Detrended fluctuation analysis on spot and futures markets of West Texas Intermediate crude oil , 2011 .

[8]  Franck Raynaud,et al.  Statistical Properties of Derivatives: A Journey in Term Structures , 2010, 1010.6026.

[9]  Complete spectral scaling of time series: towards a classification of 1/f noise. , 2014, Physical review. E, Statistical, nonlinear, and soft matter physics.

[10]  Didier Sornette,et al.  The 2006–2008 oil bubble: Evidence of speculation, and prediction , 2009 .

[11]  Gabriel J. Power,et al.  Long-range dependence in the volatility of commodity futures prices: Wavelet-based evidence , 2010 .

[12]  Ying Yuan,et al.  Analysis of the temporal properties of price shock sequences in crude oil markets , 2014 .

[13]  Jose Alvarez-Ramirez,et al.  A multi-model approach for describing crude oil price dynamics , 2004 .

[14]  R. Hernández-Pérez,et al.  Allan deviation analysis of financial return series , 2012 .

[15]  Yu Wei,et al.  Multifractal detrended cross-correlation analysis of carbon and crude oil markets , 2014 .

[16]  Li Liu,et al.  Cross-correlation between crude oil and refined product prices , 2014 .

[17]  J. Álvarez-Ramírez,et al.  Power-law periodicity in the 2003–2004 crude oil price dynamics ☆ , 2005 .

[18]  Rania Jammazi,et al.  Dependence and risk assessment for oil prices and exchange rate portfolios: A wavelet based approach , 2015 .

[19]  Jose Alvarez-Ramirez,et al.  Short-term predictability of crude oil markets: A detrended fluctuation analysis approach , 2008 .

[20]  Ling-Yun He,et al.  Are crude oil markets multifractal? Evidence from MF-DFA and MF-SSA perspectives , 2010 .

[21]  Janusz A. Holyst,et al.  Correlations in commodity markets , 2008, 0803.3884.

[22]  Jing Wang,et al.  Exploring the WTI crude oil price bubble process using the Markov regime switching model , 2015 .

[23]  L. Kilian.  Not All Oil Price Shocks are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market , 2006 .

[24]  H. An,et al.  Identifying the multiscale impacts of crude oil price shocks on the stock market in China at the sector level , 2015 .

[25]  Haizhong An,et al.  The role of fluctuating modes of autocorrelation in crude oil prices , 2014 .

[26]  Ling-Yun He,et al.  A new approach to quantify power-law cross-correlation and its application to commodity markets , 2011 .

[27]  Rongbao Gu,et al.  Multifractal analysis on international crude oil markets based on the multifractal detrended fluctuation analysis , 2010 .

[28]  Li Liu,et al.  Cross-correlations between crude oil and agricultural commodity markets , 2014 .

[29]  Ying Fan,et al.  How Does Oil Price Volatility Affect Non-Energy Commodity Markets? , 2012 .

[30]  A. Balankin,et al.  Crossover from antipersistent to persistent behavior in time series possessing the generalyzed dynamic scaling law. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Yudong Wang,et al.  Is WTI crude oil market becoming weakly efficient over time?: New evidence from multiscale analysis based on detrended fluctuation analysis , 2010 .

[32]  Feng Ma,et al.  Multifractal detrended cross-correlation analysis of the oil-dependent economies: Evidence from the West Texas intermediate crude oil and the GCC stock markets , 2014 .

[33]  Yi-Ming Wei,et al.  A generalized pattern matching approach for multi-step prediction of crude oil price , 2008 .

[34]  Rosario N. Mantegna,et al.  Book Review: An Introduction to Econophysics, Correlations, and Complexity in Finance, N. Rosario, H. Mantegna, and H. E. Stanley, Cambridge University Press, Cambridge, 2000. , 2000 .

[35]  J. Álvarez-Ramírez,et al.  Multiscale entropy analysis of crude oil price dynamics , 2011 .

[36]  Lopez,et al.  Generic dynamic scaling in kinetic roughening , 2000, Physical review letters.

[37]  Yu Wei,et al.  Auto-correlated behavior of WTI crude oil volatilities: A multiscale perspective , 2010 .

[38]  P. Manimaran,et al.  Multifractal detrended cross-correlation analysis on gold, crude oil and foreign exchange rate time series , 2014 .

[39]  L. Kilian,et al.  Why Agnostic Sign Restrictions are not Enough: Understanding the Dynamics of Oil Market VAR Models , 2009 .

[40]  J. Kwapień,et al.  Physical approach to complex systems , 2012 .

[41]  C. Zapart.  Econophysics: A challenge to econometricians , 2015 .

[42]  Mehmet Balcilar,et al.  Are there really bubbles in oil prices , 2014 .

[43]  Alexander S. Balankin,et al.  Scaling dynamics of seismic activity fluctuations , 2009 .

[44]  Cross-correlations between West Texas Intermediate crude oil and the stock markets of the BRIC , 2013 .

[45]  Tao Zhang,et al.  Nonlinear joint dynamics between prices of crude oil and refined products , 2015 .

[46]  Alexander S Balankin.  Dynamic scaling approach to study time series fluctuations. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[47]  Seong‐Min Yoon,et al.  Structural changes and volatility transmission in crude oil markets , 2011 .

[48]  A. Barabasi,et al.  Fractal concepts in surface growth , 1995 .

[49]  Stable distribution and long-range correlation of Brent crude oil market , 2014 .