The impact of global liquidity on commodity prices - evidence from a Markov-switching vector error correction model Joscha Beckmann∗
Ansgar Belke†
Robert Czudaj‡
Extended Abstract, January 15, 2013 Abstract This paper contributes to the rich literature on global liquidity in two different ways: On the one hand, we base our analysis on two different measures: Firstly, we follow the guidelines provided by Beyer, Doornik and Hendry [2000] to achieve a global series based on the aggregation of individual time series. As an alternative measure, we propose a distinction between common and idiosyncratic factors across economies as proposed by Bai and Ng [2004]. The second innovation stems from the consideration of a Markov-switching error correction model when analyzing the time-varying short-run dynamics. Our results show that the underlying relationships are indeed characterized by regime-dependence, implying that the impact of a global liquidity measure on prices varies over time. Analyzing the pattern of regime switches, one regime approximately accounts for times where no impact is observed while the second regime corresponds to periods where commodity prices adjust to long-run disequilibria. Keywords: global liquidity, cointegration, commodity prices, Markov-switching error correction, price level JEL classification: C32, E52, E58
∗ University of Duisburg-Essen, Department of Economics, Chair for Macroeconomics, D-45117 Essen, e-mail:
[email protected], phone: (0049)-201-183-3215, fax: (0049)-201-183-4181. † IZA Bonn and University of Duisburg-Essen, Department of Economics, Chair for Macroeconomics, D-45117 Essen, e-mail:
[email protected], phone: (0049)-201-183-2277, fax: (0049)-201-183-4181. ‡ University of Duisburg-Essen, Department of Economics, Chair for Econometrics, D-45117 Essen, e-mail:
[email protected], phone: (0049)-201-1833516, fax: (0049)-201-1833995 and FOM Hochschule f¨ ur Oekonomie & Management, University of Applied Sciences, Herkulesstr. 32, D-45127 Essen.
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Introduction
Although the transmission of monetary policy is a key issue in monetary economics, the cross-border implications of domestic policy decisions have only recently attracted interests from empirical researchers. A straightforward question in the global context of monetary policy is the impact of monetary policy decisions on commodity prices which has been the core of an intensive debate among policymakers and researchers. Strictly speaking, two strings of studies can be distinguished: The first kind of studies focus on the adequate response of monetary policy to commodity price shocks which is still subject to controversies [Cologni and Manera, 2008]. The second string examines the opposite question of whether an increase in domestic money supply increases commodity prices. In this paper, we follow the latter line of reasoning. Against the background of the recent crisis, global liquidity measures have attracted considerable attention in this context owing to the fact that focusing on national aggregates neglects important cross-border dynamics. In particular, the question whether an increase of global liquidity is responsible for speculative pressure and volatility of commodity prices remains controversial. A broad line of research has already focused on the link between global liquidity and commodity prices, for instance, in terms of a cointegrating relationship. An overview will be provided in the next section. However, to the best of our knowledge, previous research has exclusively focused on linear models, neglecting the possibility that the underlying dynamics are subject to nonlinearities. The aim of this study is to close this gap by applying more sophisticated cointegration methods. Based on an aggregation of country-specific time series for OECD countries, we start by creating a global liquidity measure, following the methodology introduced by Beyer et al. [2000] and adopted by Belke, Bordon and Hendricks [2010a]. In comparison to that, we propose an alternative measure of global liquidity which can be derived as the common component from the monetary aggregates of the individual economies following Bai and Ng [2004]. For both measures, we then apply a Markov-switching vector error correction model (MS-VECM) which allows for a distinction between long-run and time-varying short-run dynamics for a sample period ranging from January 1981 to March 2012. The variables under consideration include consumer prices, different commodity prices (oil, gold, agricultures), real GDP and the interest rate. The multivariate long-run estimates correspond to the underlying equilibria between those variables while the time-varying adjustment coefficients are able to discriminate between periods with and without a response of commodity prices to disequilibria. The reminder of this paper is organized as follows. The following section provides a brief summary of previous empirical findings. Section 3 gives a motivation for adopting our framework.
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Review of the literature
As the traditional workhorse in international economics, the Mundell-Fleming framework also provides a suggestion about the international transmission of domestic monetary policy shocks. Domestic money supply is assumed to be exogenous with uncovered interest rate parity as the key factor for transmission: An increase in domestic money supply results in a lower domestic interest rate, a depreciation of the domestic currency and an increasing demand for domestic goods.1 A correlation between domestic and foreign money supply results if domestic authorities also increase money supply [R¨ uffer and Stracca, 2006]. As an extension, models of the ‘New Open Economy’ type in the spirit of Obstfeld and Rogoff [1996] still stick with uncovered interest rate parity, but introduce intertemporal optimization and an endogenous reaction of domestic money supply with regard to foreign money supply. Transmission still works through exchange rates and interest rate effects with the overall results not being necessarily clear-cut. Finally, a direct transmission arises through pricing of domestic companies engaged in foreign markets if an increase in domestic prices is not matched by a proportional depreciation as suggested by purchasing power parity and suppliers adjust foreign prices as a result [R¨ uffer and Stracca, 2006]. However, the arguments raised above focus on cross-country transmission with regard to either consumer prices or output. In the context of commodity prices, Frankel [1986] has modified Dornbusch’s [1976] theory of exchange rate overshooting by allowing for a more than proportionately reaction of commodity prices after an increase in domestic money supply owing to the fact that prices of other goods are sticky. Hence, consumer prices only adjust in the long-run, commodity prices are responsible to clear markets in the short-run. In a similar fashion, Browne and Cronin [2007] argue that the price adjustment process in commodity markets is relatively fast, since participants are more equally empowered. Being auction-based traded in markets with efficient information, commodities could be characterized as flexible goods in contrast to consumer goods, resulting in a quicker response to changes of monetary policy. Based on these arguments, Belke et al. [2010a] analyze the question whether different adjustment patterns of commodity prices and consumer prices to a monetary shock can be considered as an explanation for the shift in relative prices between commodities and consumer goods. Since there is no unique definition of global liquidity, previous studies have differed not only with regard to the topic of investigation according to the theoretical suggestions mentioned above, they also have adopted different measures. In an early study Canova, Ciccarelli and Ortega [2007] have focused on the transmission of U.S. monetary policy shocks into other economies using traditional VAR techniques. Belke, Orth and Setzer [2010b] also apply the VAR approach, however base their analysis on a global measure obtained by an aggregation of domestic measures as proposed by Beyer et al. [2000]. Using a similar measure, Giese and Tuxen [2007] and Belke et al. [2010a] extend the VAR framework by distinguishing between short-run and long-run dynamics based on a multivariate 1 Since domestic output increases while foreign output decreases, this kind of policy is often labeled as a ‘beggar thy neighbor’ policy.
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cointegration approach. However, while the latter study focuses on the impact of global liquidity on commodity prices, the former is interested in share prices. Sousa and Zaghini [2006] also construct a global liquidity measure for the G5 economies and apply a structural VAR approach focusing on the impact of global output and inflation. In nutshell, the evidence with regard to other prices than that of consumer goods is not clearcut. R¨ uffer and Stracca [2006] suggest that the composite real asset price index that incorporates property and equity prices does not show any significant reaction to a global liquidity shock. Giese and Tuxen [2007] also find no evidence that share prices increase as liquidity expands but they cannot empirically reject a cointegrating relationships which suggests a positive impact of global liquidity on house prices. However, Belke, Bordon and Volz [2013] conclude that global liquidity is a useful indicator of commodity price inflation and of a more generally defined inflationary pressure at a global level.
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Global liquidity and nonlinear adjustment patterns
As outlined in the previous subsection, empirical research has analyzed the role of global liquidity in many different aspects. However, when applying cointegration techniques, most studies rely on the concept of linear adjustment of each variable to disequilibria from the identified long-run relations. This assumption obviously is quiet restrictive. There are numerous reasons for nonlinearities in the context of a global analysis. In particular, different exogenous shocks come into play when analyzing more than one country simultaneously. As an example, the stance of monetary policy has been subject to several changes after new appointments of decision makers. Straightforward examples are the United States and Japan. The same wisdom holds for global income with several productivity shocks changing the path of the global business cycle. Finally, commodity markets have undergone several structural changes, facing different kinds of regulation, varying market sizes, and kind of market players. With regard to the oil price, which is also under investigation in this study, a common view is that major oil price shocks are triggered by exogenous factor. Kilian [2008] has recently shown that this view is supported by the data for the 1980/81 and 1990/91 oil price shocks, but not necessarily for shocks occurring afterwards.2 Finally, cross-country differences may also be responsible for exogenous shocks. From an econometric point of view, a MS-VECM is well suited to account for those issues since the regime switching is treated as an exogenous stochastic process, allowing the identification of the potentially latent regimes in the data [Ihle and von Cramon-Taubadel, 2008]. On the opposite, a threshold VECM in the spirit of Balke and Fomby [1997] or Ter¨ asvirta [1994] relies on an endogenous determination of the regime variable and therefore seem less adequate in our case. 2
More recent shocks include the Asian financial crisis in 1997/98, production target cuts by the OPEC in 1999, September 11, 2001, the shortage of spare capacity in 2005, the global financial crisis that started in 2007, and further production target cuts by the OPEC in 2009.
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References Bai J, Ng S. 2004. A panic attack on unit roots and cointegration. Econometrica 72: 1127–1177. Balke NS, Fomby TB. 1997. Threshold cointegration. International Economic Review 38: 627–645. Belke A, Bordon IG, Hendricks T. 2010a. Global liquidity and commodity prices - A cointegrated VAR approach for OECD countries. Applied Financial Economics 20: 227–242. Belke A, Bordon IG, Volz U. 2013. Effects of global liquidity on commodity and food prices. World Development forthcoming. Belke A, Orth W, Setzer R. 2010b. Liquidity and the dynamic pattern of asset price adjustment: A global view. Journal of Banking & Finance 34: 1933–1945. Beyer A, Doornik JA, Hendry DF. 2000. Constructing historical euro-zone data. Economic Journal 111: 308–327. Browne F, Cronin D. 2007. Commodity prices, money and inflation. ECB Working Paper No. 738. Canova F, Ciccarelli M, Ortega E. 2007. Similarities and convergence in G-7 cycles. Journal of Monetary Economics 54: 850–878. Cologni A, Manera M. 2008. Oil prices, inflation and interest rates in a structural cointegrated VAR model for the G-7 countries. Energy Economics 30: 856–888. Dornbusch R. 1976. Expectations and exchange rate dynamics. Journal of Political Economy 84: 1161–1176. Frankel JA. 1986. Expectations and commodity price dynamics: The overshooting model. American Journal of Agricultural Economics 68: 344–348. Giese JV, Tuxen CK. 2007. Global liquidity and asset prices in a cointegrated var. Nuffield College, University of Oxford, Department of Economics, Copenhagen University . Ihle R, von Cramon-Taubadel S. 2008. A comparison of threshold cointegration and Markov-switching vector error correction models in price transmission analysis. Proceedings of the NCCC-134 Conference on Applied Commodity Price Analysis, Forecasting, and Market Risk Management, St. Louis, MO. . Kilian L. 2008. Exogenous oil supply shocks: How big are they and how much do they matter for the U.S. economy? Review of Economics and Statistics 90: 216–240. Obstfeld M, Rogoff K. 1996. Foundations of International Macroeconomics. Cambridge: MIT Press. R¨ uffer R, Stracca L. 2006. What is global excess liquidity, and does it matter? ECB Working Paper No. 696. Sousa JM, Zaghini A. 2006. Global monetary policy shocks in the G5: A SVAR approach. CFS Working Paper No. 2006/30. Ter¨ asvirta T. 1994. Specification, estimation and evaluation of smooth transition autoregressive models. Journal of the American Statistical Association 89: 208–218.
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