Lithium industry and the U.S. crude oil prices. A fractional cointegration VAR and a Continuous Wavelet Transform analysis

Abstract This paper analyzes the dynamics of U.S. lithium mining companies, the lithium industry and West Texas Intermediate (WTI) crude oil prices using a Fractional Cointegration Vector AutoRegressive model (FCVAR model) and a Continuous Wavelet Transform (CWT) for its resolution. The results indicate evidence of a negative relationship between FMC Corp with Albermale and SQM stock prices. These results are similar if we analyze the risk based on the beta term structure of each company. Analyzing the fractional differencing parameter for the stock prices and their logs, we observe that they are very persistent, and there are no long-term deviations in the stock prices. The same happens when analyzing the beta term structure. Based on Continuous Wavelet Transform (CWT) methods, our results show that lithium mining companies and the lithium industry are weakly correlated with WTI crude oil prices at higher frequencies (short-run) and persist through the sample period. At lower frequencies (long-term) the time series reached a high level of dependence between late 2012 to mid 2016, concluding that the lithium mining companies and the lithium industry reflect and foreshadow the responsiveness of the WTI crude oil prices during the period mentioned above.

[1]  Luis A. Gil-Alana,et al.  Fractional integration and structural breaks at unknown periods of time , 2007 .

[2]  Wei‐Xing Zhou Multifractal detrended cross-correlation analysis for two nonstationary signals. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  Lutz Kilian,et al.  The Impact of the Shale Oil Revolution on U.S. Oil and Gasoline Prices , 2014, Review of Environmental Economics and Policy.

[4]  L. Gil‐Alana,et al.  A fractional cointegration var analysis of exchange rate dynamics , 2020 .

[5]  P. Phillips Unit root log periodogram regression , 2007 .

[6]  Fallaw Sowell Modeling long-run behavior with the fractional ARIMA model , 1992 .

[7]  R. Fouquet Lessons from energy history for climate policy: Technological change, demand and economic development☆ , 2016 .

[8]  S. Johansen Likelihood-Based Inference in Cointegrated Vector Autoregressive Models , 1996 .

[9]  Michael Höck,et al.  Lithium market research – global supply, future demand and price development , 2017 .

[10]  Lukas Vacha,et al.  Co-movement of energy commodities revisited: Evidence from wavelet coherence analysis , 2012, 1201.4776.

[11]  P. Robinson,et al.  Testing of unit root and other nonstationary hypotheses in macroeconomic time series , 1996 .

[12]  Benjamin Sovacool How Long Will It Take? Conceptualizing the Temporal Dynamics of Energy Transitions , 2016 .

[13]  P. Robinson Log-Periodogram Regression of Time Series with Long Range Dependence , 1995 .

[14]  M. Nielsen,et al.  A Fractionally Cointegrated VAR Analysis of Economic Voting and Political Support , 2014 .

[15]  L. Kilian The Impact of the Fracking Boom on Arab Oil Producers , 2016, SSRN Electronic Journal.

[16]  P. Perron,et al.  Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power , 2001 .

[17]  Luis A. Gil-Alana,et al.  Automobile components: Lithium and cobalt. Evidence of persistence , 2019, Energy.

[18]  L. Gil‐Alana,et al.  The Lithium Industry and Analysis of the Beta Term Structure of Oil Companies , 2020, Risks.

[19]  Chris Chatfield,et al.  Introduction to Statistical Time Series. , 1976 .

[20]  Uwe Hassler,et al.  On the power of unit root tests against fractional alternatives , 1994 .

[21]  Maria Joana Soares,et al.  Using wavelets to decompose the time–frequency effects of monetary policy , 2008 .

[22]  Rania Jammazi,et al.  Time-varying causality between crude oil and stock markets: What can we learn from a multiscale perspective? , 2017 .

[23]  Joseph Sarkis,et al.  Carbon footprint of global passenger cars: Scenarios through 2050 , 2016 .

[24]  Judy Anderson,et al.  Electric and Hybrid Cars: A History , 2004 .

[25]  Zhi-Qiang Jiang,et al.  Multifractal detrending moving-average cross-correlation analysis. , 2011, Physical review. E, Statistical, nonlinear, and soft matter physics.

[26]  A. Rossen What are metal prices like? Co-movement, price cycles and long-run trends , 2015 .

[27]  P. Phillips,et al.  Testing the null hypothesis of stationarity against the alternative of a unit root: How sure are we that economic time series have a unit root? , 1992 .

[28]  P. Narayan,et al.  Stock return predictability and determinants of predictability and profits , 2016 .

[29]  N. Apergis,et al.  Precious metal markets, stock markets and the macroeconomic environment: a FAVAR model approach , 2014 .

[30]  H. Stanley,et al.  Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series. , 2007, Physical review letters.

[31]  P. Narayan,et al.  Economic Significance of Commodity Return Forecasts from the Fractionally Cointegrated VAR Model , 2017 .

[32]  J. Geweke,et al.  THE ESTIMATION AND APPLICATION OF LONG MEMORY TIME SERIES MODELS , 1983 .

[33]  Luis A. Gil-Alana,et al.  U.S. shale oil production and WTI prices behaviour , 2017 .

[34]  Evaluation of dynamic pass-through of carbon prices into electricity prices - a cointegrated VECM analysis , 2013 .

[35]  Luís Aguiar-Conraria,et al.  The Continuous Wavelet Transform: Moving Beyond Uni‐ and Bivariate Analysis , 2014 .

[36]  Ilona Weinreich,et al.  Wavelet-based prediction of oil prices , 2005 .

[37]  H. Akaike A Bayesian extension of the minimum AIC procedure of autoregressive model fitting , 1979 .

[38]  P. Robinson Gaussian Semiparametric Estimation of Long Range Dependence , 1995 .

[39]  Maria Joana Soares,et al.  Oil and the macroeconomy: using wavelets to analyze old issues , 2011 .

[40]  Leandro Maciel Technical analysis based on high and low stock prices forecasts: evidence for Brazil using a fractionally cointegrated VAR model , 2018, Empirical Economics.

[41]  W. Fuller,et al.  Distribution of the Estimators for Autoregressive Time Series with a Unit Root , 1979 .

[42]  Connor Jeff,et al.  Wavelet Transforms and Commodity Prices , 2005 .

[43]  Roger Fouquet,et al.  The slow search for solutions: lessons from historical energy transitions by sector and service , 2010 .

[44]  Walter C. Labys,et al.  The existence of metal price cycles , 1998 .

[45]  Jozef Baruník,et al.  An empirical model of fractionally cointegrated daily high and low stock market prices , 2015 .

[46]  Wei-Xing Zhou,et al.  Detrending moving average algorithm for multifractals. , 2010, Physical review. E, Statistical, nonlinear, and soft matter physics.

[47]  Cetin Ciner On the long run relationship between gold and silver prices A note , 2001 .

[48]  P. Phillips Testing for a Unit Root in Time Series Regression , 1988 .

[49]  J. Stock,et al.  Efficient Tests for an Autoregressive Unit Root , 1992 .

[50]  S. Johansen,et al.  Likelihood Inference for a Fractionally Cointegrated Vector Autoregressive Model , 2010 .

[51]  P. Robinson Efficient Tests of Nonstationary Hypotheses , 1994 .

[52]  H. Akaike Maximum likelihood identification of Gaussian autoregressive moving average models , 1973 .

[53]  B. Auer Superstitious seasonality in precious metals markets? Evidence from GARCH models with time-varying skewness and kurtosis , 2015 .

[54]  A. Malliaris,et al.  Energy Sector Pricing: On the Role of Neglected Nonlinearity , 2008 .

[55]  R. Davidson,et al.  Walvelet Analysis of Commodity Price Behavior , 1998 .

[56]  P. Perron,et al.  Testing for a Unit Root in a Time Series with a Changing Mean: Corrections and Extensions , 1992 .

[57]  S. Johansen,et al.  Likelihood Inference for a Nonstationary Fractional Autoregressive Model , 2007 .

[58]  Aviral Kumar Tiwari,et al.  Continuous wavelet transform and rolling correlation of European stock markets , 2016 .

[59]  Glenn D. Rudebusch,et al.  On the Power of Dickey-Fuller Tests against Fractional Alternatives , 1991, Business Cycles.

[60]  A. M. Masih,et al.  Contagion and interdependence across Asia-Pacific equity markets: An analysis based on multi-horizon discrete and continuous wavelet transformations , 2016 .

[61]  S. Mitra,et al.  Oil price and automobile stock return co-movement: A wavelet coherence analysis , 2019, Economic Modelling.

[62]  L. Gil‐Alana,et al.  Lithium industry in the behavior of the mergers and acquisitions in the US oil and gas industry , 2018, Energy Sources, Part B: Economics, Planning, and Policy.

[63]  L. Gil‐Alana,et al.  Does gold act as a hedge against inflation in the UK? Evidence from a fractional cointegration approach over 1257 to 2016 , 2017 .

[64]  Nebojsa Nakicenovic,et al.  THE AUTOMOBILE ROAD TO TECHNOLOGICAL CHANGE: DIFFUSION OF THE AUTOMOBILE AS A PROCESS OF TECHNOLOGICAL SUBSTITUTION. IN: THE AUTOMOBILE , 1986 .

[65]  P. Phillips Discrete Fourier Transforms of Fractional Processes , 1999 .

[66]  Ramazan Sarı,et al.  Dynamics of oil price, precious metal prices, and exchange rate , 2010 .

[67]  Søren Johansen,et al.  A REPRESENTATION THEORY FOR A CLASS OF VECTOR AUTOREGRESSIVE MODELS FOR FRACTIONAL PROCESSES , 2008, Econometric Theory.

[68]  P. Perron,et al.  Computation and Analysis of Multiple Structural-Change Models , 1998 .

[69]  L. Gil‐Alana,et al.  Lithium: Production and estimated consumption. Evidence of persistence , 2019, Resources Policy.

[70]  J. Mo,et al.  The Impact of Electric Vehicle Demand and Battery Recycling on Price Dynamics of Lithium-Ion Battery Cathode Materials: A Vector Error Correction Model (VECM) Analysis , 2018, Sustainability.

[71]  Gillian Dooley,et al.  An assessment of time series methods in metal price forecasting , 2005 .

[72]  P. Schmidt,et al.  On the Power of the KPSS Test of Stationarity Against Fractionally-Integrated Alternatives , 1996 .