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Empirical Economics

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25-09-2023

Nonlinear responses of crude oil prices to the US dollar exchange rates: the role of inventories

Authors: Zhepeng Hu, Lei Yan

Published in: Empirical Economics | Issue 4/2024

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Abstract

It has been widely documented that the relationship between crude oil prices and the value of US dollar changes over time (Beckmann et al. in Energy Econ 88:104772, 2020). However, the underlying economic driver for the time-varying relationship is not clear. Based on the competitive storage theory, we provide theoretical evidence that greater inelasticity of market demand for crude oil induced by low inventories is expected to lead to higher responsiveness of crude oil prices to exchange rate changes. We empirically test this hypothesis using the threshold vector autoregressive (TVAR) model and show that crude oil prices respond to shocks to the US dollar exchange rates in an asymmetric manner. Changes in the US dollar exchange rates have greater and more significant influence on crude oil prices in the low-inventory regime than in the high-inventory regime.

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See Beckmann et al. (2020) who provided an excellent review on this issue.

Another popular method for estimating regime-switching impulse responses is using the local projection (LP) approach developed by Jordà (2005). However, we do not consider the LP approach for two reasons. First, although the LP method is widely used due to its computational simplicity, its asymptotic validity in estimating impulse responses in non-linear cases has not been well-established. A recent study by Gonçalves et al. (2022) reveal that when the regime switching variable is endogenous, which is the main focus of this paper, the LP estimator of the impulse response function tends to be asymptotically biased. Second, the TVAR model has the advantage of being able to estimate the threshold variable based on test statistics, whereas the LP approach does not.

The Federal Reserve made several important changes to the foreign exchange rate indices in February 2019. Previously, the AFE dollar index was named the major currencies dollar index.

Both prices were obtained from the EIA and deflated using the U.S. Consumer Price Index.

Recently, Kilian (2022a) advocates to use an alternative global oil inventory series provided by the Energy Intelligence Group (EIG), which includes non-OECD commercial inventories. However, those data are only available to subscribers. As shown in Kilian (2022a), the non-OECD commercial inventory presents a strong trending behavior. In the following section, we create the threshold variable by using detrended crude oil inventories as our interest is to measure the relative abundance and scarcity of global crude oil inventory. Hence, our results are not likely to be sensitive to the missing non-OECD commercial inventories.

Notice that we do not estimate a structural model as we do not focus on estimating the elasticity of demand. Instead, our interest is to show how regime switches in the crude oil inventory level, which shifts the demand elasticity, affect the responsiveness of crude oil prices to exchange rate stocks.

A shock at time t triggers a switching of regime until time t + d.

Data series used for the analysis all cross their means several times but wander for long periods before they revert back to their means which are typical near unit root behaviors (Kilian and Lütkepohl 2017).

Kilian (2009) suggests including 24 lags to capture of global business cycle. Although we selected one lag, our results are robust to using longer lags.

Using a short delay parameter is consistent with TVAR studies that employ low frequency data (Balke 2000; Baum and Koester 2011; Ferraro et al. 2015).

Notice that the GIRFs are also affected by the sign of the shock. We also tried using a one standard deviation negative shock and found the responses are visibly symmetric. For clarity, we only present the responses to a one standard deviation positive shock.

Akram QF (2004) Oil prices and exchange rates: Norwegian evidence. Economet J 7:476–504. https://doi.org/10.1111/j.1368-423X.2004.00140.xMathSciNetCrossRef

Akram QF (2009) Commodity prices, interest rates and the dollar. Energy Econ 31:838–851. https://doi.org/10.1016/j.eneco.2009.05.016CrossRef

Albulescu CT, Ajmi AN (2021) Oil price and US dollar exchange rate: Change detection of bi-directional causal impact. Energy Econ 100:105385. https://doi.org/10.1016/j.eneco.2021.105385CrossRef

Amano RA, van Norden S (1998) Oil prices and the rise and fall of the US real exchange rate. J Int Money Financ 17:299–316. https://doi.org/10.1016/S0261-5606(98)00004-7CrossRef

Balke NS (2000) Credit and economic activity: credit regimes and nonlinear propagation of shocks. Rev Econ Stat 82:344–349. https://doi.org/10.1162/rest.2000.82.2.344CrossRef

Baum A, Koester G (2011) The Impact of Fiscal Policy on Economic Activity Over the Business Cycle - Evidence from a Threshold VAR Analysis https://papers.ssrn.com/abstract=2785397

Baumeister C, Kilian L (2016) Forty years of oil price fluctuations: why the price of oil may still surprise Us. J Econ Perspect 30:139–160CrossRef

Beckmann J, Czudaj R (2013) Oil prices and effective dollar exchange rates. Int Rev Econ Financ 27:621–636. https://doi.org/10.1016/j.iref.2012.12.002CrossRef

Beckmann J, Berger T, Czudaj R (2016) Oil price and FX-rates dependency. Quant Financ 16:477–488. https://doi.org/10.1080/14697688.2015.1045930MathSciNetCrossRef

Beckmann J, Czudaj RL, Arora V (2020) The relationship between oil prices and exchange rates: Revisiting theory and evidence. Energy Econ 88:104772. https://doi.org/10.1016/j.eneco.2020.104772CrossRef

Cafiero C, Bobenrieth HESA, Bobenrieth HJRA, Wright BD (2011) The empirical relevance of the competitive storage model. J Econ 162:44–54. https://doi.org/10.1016/j.jeconom.2009.10.008MathSciNetCrossRef

Deaton A, Laroque G (1992) On the Behaviour of Commodity Prices. Rev Econ Stud 59:1–23. https://doi.org/10.2307/2297923CrossRef

Deaton A, Laroque G (1995) Estimating a nonlinear rational expectations commodity price model with unobservable state variables. J Appl Econ 10:S9–S40. https://doi.org/10.1002/jae.3950100503CrossRef

Fan Y, Xu J-H (2011) What has driven oil prices since 2000? A structural change perspective. Energy Econ 33:1082–1094. https://doi.org/10.1016/j.eneco.2011.05.017CrossRef

Ferraro D, Rogoff K, Rossi B (2015) Can oil prices forecast exchange rates? An empirical analysis of the relationship between commodity prices and exchange rates. J Int Money Financ 54:116–141. https://doi.org/10.1016/j.jimonfin.2015.03.001CrossRef

Geman H, Ohana S (2009) Forward curves, scarcity and price volatility in oil and natural gas markets. Energy Econ 31:576–585. https://doi.org/10.1016/j.eneco.2009.01.014CrossRef

Gonçalves S, Herrera AM, Kilian L, Pesavento E (2022) When do state-dependent local projections work? https://papers.ssrn.com/abstract=4106149

Gustafson RL (1958) Carryover levels for grains: A method for determining amounts that are optimal under specified conditions. United States Department of Agriculture, Economic Research Service https://econpapers.repec.org/paper/agsuerstb/157231.htm

Hansen BE (1999) Threshold effects in non-dynamic panels: Estimation, testing, and inference. J Econ 93:345–368. https://doi.org/10.1016/S0304-4076(99)00025-1MathSciNetCrossRef

Hatzenbuehler PL, Abbott PC, Foster KA et al (2016) Agricultural commodity prices and exchange rates under structural change. J Agric Resour Econ. https://doi.org/10.22004/AG.ECON.235153CrossRef

Hodrick R, Prescott E (1997) Postwar U.S. business cycles: an empirical investigation. J Money, Credit, Bank 29:1–16CrossRef

Jordà Ò (2005) Estimation and inference of impulse responses by local projections. Am Econ Rev 95:161–182CrossRef

Kilian L (2009) Not all oil price shocks are alike: disentangling demand and supply shocks in the crude oil market. Am Econ Rev 99:1053–1069CrossRef

Kilian L (2022a) Facts and fiction in oil market modeling. Energy Econ 110:105973. https://doi.org/10.1016/j.eneco.2022.105973CrossRef

Kilian L (2022b) Understanding the estimation of oil demand and oil supply elasticities. Energy Econ 107:105844. https://doi.org/10.1016/j.eneco.2022.105844CrossRef

Kilian L, Lütkepohl H (2017) Structural Vector Autoregressive Analysis. Cambridge University Press, CambridgeCrossRef

Kilian L, Murphy DP (2014) The role of inventories and speculative trading in the global market for crude oil. J Appl Economet 29:454–478MathSciNetCrossRef

Kilian L, Zhou X (2022) Oil prices, exchange rates and interest rates. J Int Money and Financ 126:102679. https://doi.org/10.1016/j.jimonfin.2022.102679CrossRef

Koop G, Pesaran MH, Potter SM (1996) Impulse response analysis in nonlinear multivariate models. J Econ 74:119–147. https://doi.org/10.1016/0304-4076(95)01753-4MathSciNetCrossRef

Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y (1992) 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? J Econ 54:159–178. https://doi.org/10.1016/0304-4076(92)90104-YCrossRef

Lo MC, Zivot E (2001) Threshold cointegration and nonlinear adjustment to the law of one price. Macroecon Dyn 5:533–576. https://doi.org/10.1017/S1365100501023057CrossRef

Nikitopoulos CS, Squires M, Thorp S, Yeung D (2017) Determinants of the crude oil futures curve: Inventory, consumption and volatility. J Bank Financ 84:53–67. https://doi.org/10.1016/j.jbankfin.2017.07.006CrossRef

Pesaran HH, Shin Y (1998) Generalized impulse response analysis in linear multivariate models. Econ Lett 58:17–29. https://doi.org/10.1016/S0165-1765(97)00214-0MathSciNetCrossRef

Ravn M, Uhlig H (2002) On adjusting the Hodrick-Prescott filter for the frequency of observations. Rev Econ Stat 84:371–375CrossRef

Reboredo JC (2012) Modelling oil price and exchange rate co-movements. J Policy Modeling 34:419–440. https://doi.org/10.1016/j.jpolmod.2011.10.005CrossRef

Reboredo JC, Rivera-Castro MA (2013) A wavelet decomposition approach to crude oil price and exchange rate dependence. Econ Model 32:42–57. https://doi.org/10.1016/j.econmod.2012.12.028CrossRef

Tiwari AK, Albulescu CT (2016) Oil price and exchange rate in India: Fresh evidence from continuous wavelet approach and asymmetric, multi-horizon Granger-causality tests. Appl Energy 179:272–283. https://doi.org/10.1016/j.apenergy.2016.06.139ADSCrossRef

Tsay RS (1998) Testing and modeling multivariate threshold models. J Am Stat Assoc 93:1188–1202. https://doi.org/10.2307/2669861MathSciNetCrossRef

Wright BD (2011) The economics of grain price volatility. Appl Econ Perspect Policy 33:32–58. https://doi.org/10.1093/aepp/ppq033CrossRef

Ye M, Zyren J, Shore J (2006) Forecasting short-run crude oil price using high- and low-inventory variables. Energy Policy 34:2736–2743. https://doi.org/10.1016/j.enpol.2005.03.017CrossRef

Title: Nonlinear responses of crude oil prices to the US dollar exchange rates: the role of inventories
Authors: Zhepeng Hu
Lei Yan
Publication date: 25-09-2023
Publisher: Springer Berlin Heidelberg
Published in: Empirical Economics / Issue 4/2024
Print ISSN: 0377-7332
Electronic ISSN: 1435-8921
DOI: https://doi.org/10.1007/s00181-023-02502-x

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