The drivers of physical demand for gold

Brian M Lucey et al | The drivers of physical demand for gold BRIAN M LUCEY Trinity Business School, Trinity College Dublin, Ireland – blucey@tcd.ie FERGAL A O’CONNOR The York Management School, University of York, England, UK – fergal.oconnor@york.ac.uk SAMUEL A VIGNE Queen’s Management School, Queen’s University Belfast, Northern Ireland – s.vigne@qub.ac.uk VO XUAN VINH University of Economics HCMC – vinhvx@ueh.edu.vn Abstract Which factors drive the price of gold? Many papers have addressed this question from different angles; the answer depends on the researchers’ view and definition of the precious metal: investment asset, industrial asset, or a mixture of both? While most researchers focus on the influence of macroeconomic variables on the price of gold, this paper investigates the relationship between a set of macroeconomic variables and the physical demand for the yellow metal across a multitude of countries Different panel and non-panel models are used and tested for goodness of fit in order to derive empirical insights into the drivers of physical demand Results for total gold demand indicate a positive relationship with shortterm yields and economic uncertainty, while the exact opposite is observed for industrial gold demand, where a positive relationship with economic activity is observed Furthermore, results indicate a rising luxury demand linked to increases in national wealth, and towards a positive relationship between investment demand for gold and both inflation and economic uncertainty More specifically, we break a common myth by proving that global investors protect themselves from inflation by investing into physical gold rather than through buying jewellery Keywords: gold; physical demand Introduction Financial research about precious metals draws conclusions about empirical behaviour and aspects of gold by considering the official price originating on stock markets In this case, the demand is aggregated; no difference is made between institutional and non- institutional investors, between the private and the public sector, between demand originating from consumers and producers However, the alleged safety character of gold is the very definition of the asset’s nature; one would think that this would only truly come to light by means of a physical investment into gold While indeed an exposure to gold through holding it in an investor’s portfolio is beneficial for multiple reasons (see Baur and Lucey (2010) and Batten et al (2014)), the real safety of gold lies in holding it physically as a last resort asset in extreme situations (Starr and Tran (2008)) Financial research on gold can be divided into different categories, each considering different aspects of the precious metals (O’Connor et al (2015)) A very predominant field is on the relationship between gold and inflation; here an alleged relationship is believed to exist based on gold’s definition as both: an international currency and a production asset If gold is considered to be an international currency, an increase in expected inflation would lead to a reduction of the anticipated purchasing power, which would lead to investors driving down their proportion of cash and invest in gold, hence pushing the price upwards (Lucey et al (2016)) On the other hand, if gold is considered to be a regular asset, then its price would rise alongside the rate of inflation since the definition of inflation is that the dollar price of a typical good rises (Jaffe (1989)) The reaction to inflation from investors is therefore proactive while the reaction from producers is reactive an obvious difference in the behaviour of demand should therefore be observable A similar reasoning can be applied for the safe haven theory proposed by Baur and Lucey (2010): gold offers protection to investors during financial turmoils, which should positively impact investors demand while it should, if anything, diminish the demand from producers who are facing an economic downturn Again, a different impact on investor and producer demand can be expected While modelling the demand for physical gold can be done relying on the same classical tools used when modelling the total demand market, the task remains very complicated due to the limited availability of data and the manual allocation of the demand Extracting these figures is a very cumbersome and labour-intensive task which 540 | ICUEH2017 can only be done by looking into the annual surveys of the past decades computed by the Gold Fields Mineral Services Ltd and available only in physical copies at their offices in London Non-Government physical demand for gold can be broken down into three different categories:  Industrial Demand: reflecting the demand for precious metals as a production input in electronics, dentistry etc  Investment Demand: the demand for bars and coins, targeting mostly investors attracted by the safety aspects of precious metals  Luxury Demand: gold needed for the production of jewellery Important country effects might affect the physical demand for gold by influencing some of the three categories more than others In order to try and derive empirical results instead of running country-specific models, we propose working with different panel approaches and formally test whether or not pooled Ordinary Least Squares (OLS) procedures could accurately fit the data while deleting country-specific effects The choice of country is made in regard to the country’s relative importance on both the offer and/or the demand market of gold The following countries are considered: Australia, Canada, China, Egypt, Germany, India, Italy, Japan, Mexico, Russia, Saudi Arabia, South Korea, Switzerland, Thailand, Turkey, the United Kingdom of Great Britain and Northern Ireland, and finally, the United States of America This paper contributes to the field by being the first to look at physical demand for gold, breaking down the demand into different types We work with a clean and thorough methodology and derive insightful results into the effect of macroeconomic variables on the physical demand for gold The rest of this paper is organised as follows: Section offers a brief overview of the related literature in order to defend the choice of data, Section presents the methodology, while Section outlines and discusses the empirical results Finally, Section concludes Literature Review and Data Presentation FERGAL LITERATURE The annual Gold Fields Mineral Services (GFMS) surveys published Thomson Reuters provide an overview of the amount of gold supplied and demanded across various countries over the past calendar year Plotting the demand for gold and silver respectively (Figures 1) indicates a shift in the demand towards a rising importance of the investment side, the graphs are also revealing that jewellery consumption is the most important factor in demand for physical gold It should be noted that Figures is computed taking into account the global demand for gold However, the regression results in this paper are computed considering only a subset of countries, which were chosen because of their relative importance on either the supply or the demand side of the gold market respectively The countries are: Australia, Canada, China, Egypt, Germany, India, Italy, Japan, Mexico, Russia, Saudi Arabia, South Korea, Switzerland, Thailand, Turkey, the United Kingdom of Great Britain and Northern Ireland, and finally, the United States of America While the research of Starr and Tran (2008) is the only paper focused on the drivers of physical demand for gold, it is indeed the only source that can be used as a steppingstone when deciding what data to consider In line with Starr and Tran (2008), the CPI, the GDP and the exchange rate to the US Dollar have been considered Figure 1: Global Demand for Gold by Type in Tonnes The level of the national equity indices have also been considered, as well as both long term and short term interest rates in order to get a feeling for the state of the underlying economy Here, the short term interest rates considered are the Months Interbank Lending Rate, while 10 Years Government Bond Yields are used as a proxy for long term interest rates The dataset is also augmented with narrow money supply as well as the Economic Uncertainty Index if such an index is available for the country considered All data are annually and run from 1990 to 2015 Methodology 3.1 Identifying Heteroscedasticity through Residuals A major assumption of linear regression procedures is that the variance of the error terms u is constant, the assumption of homoscedasticity ( Brooks (2014)) Breusch and Pagan (1979) propose a testing procedure to detect the presence of possible heteroscedasticity in linear regression models by building upon a classical regression model of the form: y = β0 + β1x + u (1) where a set of residuals uˆ can be obtained, while an Ordinary Least Squares procedure would constrain their mean value to be In the case that this assumption might fail, the variance of the residuals might be linearly related to independent variables and the model could be examined by regressing the squared residuals on the independent variables ( Brooks (2014)): uˆ 0  1 x v = (2) Breusch and Pagan (1979) model the variances of the error term σt as: ) (3) where the function h(·), not indexed by t, is assumed to possess both a first and a second order derivative Furthermore, α is a (p ∗ 1) vector of unrestricted parameters unrelated to the β coefficients in Equation 1, while the first element in z is unity (Breusch and Pagan (1979)) Specifications in Equation allow to test for the Null Hypothesis of homoscedasticity using: H0 : α2 = = αp = (4) 2 and therefore zt α = α1 so that σt = h(α1) = σ is constant 3.2 Evaluating Estimator Consistency Hausman (1978) proposes a test that evaluates the known consistency of an estimator ˆ with another estimator ˆ efficient under the assumption being tested Theoretically, the procedure is based on the expectation that for a standard regression of the type: y = xθ + ε (5) two assumptions are made: first, that the conditional expectations of ε given x is zero and that ε have a spherical covariance matrix More specifically, in econometrical terms: E(ε|x) = (6) and V (ε|x) = σ I (7) While quite some attention is paid to testing the assumption presented in Equation 7, Hausman (1978) proposes a unified approach to test the assumption made in Equation ˆ The basic null hypothesis is both an efficient and consistent is that θ estimator of the true parameters So if a comparison of the estimates from estimator θ ˆ ˆ with the efficient estimato assumed in Equation can be made, and noting that rθ their differences is uncorrelated with estimator θ reformulated as: y = x~x v ˆ under the null hypothesis, Equation can be (8) where x˜ is a suitably transformed version of x (Hausman (1978)) The test statistic is distributed as χ with a number of degrees of freedom equal to the rank of the difference in the variance matrices and computed as follows (Stata Corporation (2013)): H = ( c   e ) (Vc e ) V ( 1 c (9)  e ) where βc is the coefficient vector from the consistent estimator θ ˆ and βe ˆ is the coefficient vector from the efficient Furthermore, Vc estimator θ is the ˆ covariance matrix of the consistent and Ve is the covariance estimator θ matrix of the ˆ efficient estimator θ 3.3 Determining Serial Correlation in the Idiosyncratic Error Term Serial correlation in panel data leads to biased standard errors and to less efficient results; Wooldridge (2002) therefore proposes a testing procedure that identifies serial correlation in the idiosyncratic error term in both random- and fixed-effects models Assume the following model: yit = X it 1 Zi 2  i it (10) i t {1,2, , {1,2, , N} Ti } where yit is the dependent variable and α, β1, and β2 are + K1 + K2 parameters (Drukker (2003)) Xit is a (1 ∗ K1) vector of time-varying covariates and Zi is a (1 ∗ K2) vector of time- invariant covariates, while µi is the individual level effect and it is the idiosyncratic error In the case that the µi are correlated with the Xit or the Zi, then the coefficients on the time-varying covariates Xit can be consistently estimated by a regression on either the withintransformed data or the first-differenced data In the case that the µi are uncorrelated with the Xit and the Zi, the coefficients on both time-varying and time-invariant covariates can be estimated consistently and efficiently using the feasible generalised least squares method known as randomeffects regression (Drukker (2003)) A discussion on the estimators of the coefficients of the covariates Xit and Zi can be found in Wooldridge (2002) and Baltagi (2013) Assuming that there is no serial correlation in the idiosyncratic errors, or assuming that ] = for all s 6= t, Wooldridge (2002) relies on the residuals obtained from a regression in firstdifferences of the form: yit  yit1 yit = ( X it X it1 )1 it it1 = Xit 1 it (11) where ∆ is the first-difference operator (Drukker (2003)) The Wooldridge (2002) procedure estimates the parameters β1 by regressing ∆yit on ∆Xit and obtains the residuals eˆit In case the it are not serially correlated, then (Drukker (2003)) Wooldridge (2002) therefore 560 | ICUEH2017 the final LSDVC procedure suggested no significant variables, relationship between them (Table 12) 4.3 Investment Demand Investment demand consists of the identified physical demand for bars and coins in each of the 17 countries mentioned above So in contrary to the two other demand facets, luxury and production demand, investment demand for physical gold is not a consumptive demand; instead the gold bought is hoarded in anticipation of rising prices or an economic downturn In a first step, the Breusch and Pagan (1979) procedure is used to test for model misspecification Table 13 Breusch and Pagan Lagrangian Multiplier Test for Random Effects: Investment Demand for Gold Var lnidemand e u  (1) Prob > 7.90524 5.15514 2.06611 sd = sqrt (Var) 2.811627 2.270495 1.437399 0.65 0.2092 2 The results in Table 13 suggest failing to reject the null hypothesis and that the variance of the unobserved fixed effects is null More specifically, there is no evidence of significant differences across the countries - a classical OLS regression is therefore appropriate The General-to-Specific procedure is applied to get an understanding of the variables that are likely to explain movements and changes in physical investment demand Table 14 General-to-Specific Modelling Algorithm: Investment Demand for Gold lnequity lnuncertaint y _cons Coef 0.53101 Std Err 0.12222 z 4.34 P>|z| 0.000 95% Confidence Interval 0.29029 0.77174 0.67404 0.19884 3.39 0.001 0.28239 1.06569 1.62370 1.49617 1.09 0.279 -1.32323 4.57063 Results in Table 14 identify two variables: the level of the national stock market index and the economic uncertainty index; the coefficients suggest a positive relationship between gold investment demand and the level of economic uncertainty in the country, 562 | ICUEH2017 an economically sound result in the light of gold’s alleged role as a protection asset during times of economic and political tensions On the other hand, a negative relationship with stock market indices could have been expected, indicating that gold would serve as an equity hedge (Baur and Lucey (2010)) However, this could be seen as an indication, that in certain countries, a new creation of wealth led to investment into physical gold for reasons of disposable income rather than for the sake of wealth protection per se An example is China, were the demand quantity for physical gold was growing alongside the level of wealth of the country (Gold Field Mineral Services Ltd (2016)) 2 However, the R and adjusted R values of the GenSpec model are very low: with 0.0886 and 0.0812 respectively, they suggest to recalibrate the model Building upon the results displayed in Table 14, the investigation is continued by running a pooled OLS regression in which the standard errors are specified as robust to possible model misspecification Table 15 Pooled OLS Regression: Investment Demand for Gold Coef -0.08018 Std Err 0.05774 z -1.39 P>|z| 0.167 lncpi 1.54311 0.55553 2.78 0.006 0.44731 2.63891 lngdp 0.06757 0.08180 0.83 0.410 -0.09378 0.22893 lnexchange -0.26088 0.07139 -3.65 0.000 -0.40170 -0.12006 lyield -0.15750 0.19332 -0.81 0.416 -0.53883 0.22382 syield 0.22624 0.13564 0.097 -0.04132 0.49380 lnequity 0.29240 0.20579 1.6 1.4 0.157 -0.11352 0.69833 0.62715 0.20004 3.14 0.002 0.23256 1.02174 -5.26810 5.06208 -1.04 0.299 15.25320 4.71700 lnmoney lnuncertain ty _cons 95% Confidence Interval -0.19409 0.03372 Regression results in Table 15 support the findings in Table 14; that there is a positive relationship between investment demand for gold and economic uncertainty However, no significant linear association is observed between the level of national equity and the amount of investment demand in Table 15, results opposed to those in Table 14 Furthermore, three additional variables are identified in the later procedure, namely: the CPI, the national exchange rate to the US Dollar, and finally, short term interest rates It should be noted that the relationship with both the CPI and the short term yields is positive In other words, investment demand for gold rises alongside inflation, a finding somewhat expected when considering results of Hoang et al (2016), Sharma (2016), or Lucey et al (2016) The positive relationship with short term interest yields is a further indication of a rising investment demand into gold when the economic climate is tense: indeed, short term debt yields can be considered a reliable proxy for the state of the economic climate Finally, the negative relationship between national exchange rates to the US Dollar and physical investment demand is in line with the argumentation of O’Connor et al (2015), that a weak US Dollar makes it cheaper for other countries to buy gold Indeed, the results are an indication that when a currency grows in strength against the Dollar, the market actors of that given economy tend to purchase more physical gold for investment reasons 4.4 Production Demand Production demand for gold is composed of three main elements: electronics, dental and medical, and other, nonattributable industrial demand Being, alongside jewellery consumption, a facet of demand where gold is consumed rather than hoarded, this section will identify the drivers of industrial gold demand using linear and non-linear modelling approaches on a panel dataset consisting of 17 different countries A Breusch and Pagan (1979) Lagrange Multiplier test is used to test for model misspecification Table 16 Breusch and Pagan Lagrangian Multiplier Test for Random Effects: Production Demand for Gold Var lnpdemand e u 2 (1) Prob >  4.11745 0.150918 0.993694 sd = sqrt (Var) 2.029151 0.3884819 0.9968424 798.93 0.0000 Table 16 suggests that the variance of the unobserved fixed effects is different than 0, indicating that a pooled OLS regression might not be the appropriate model to use In preparation of specifying panel data models, the Hausman (1978) procedure is used to determine whether the coefficients in a model should be determined by a random or a fixed effect model Table 17 Hausman Specification Test: Production Demand for Gold (b) (B) lnmoney Fixe d -0.28321 Rando m 0.05790 lncpi -0.55639 -0.77090 lngdp -0.24091 -0.69860 lnexchange 0.33240 0.29565 lyield -0.07306 -0.06713 syield 0.01325 0.02990 lnequity 0.15890 0.11904 lnuncertainty -0.13302 -0.12901 (b-B) Differen ce 0.3411 0.2145 0.4576 Vb VB )) S.E 0.101 39 0.456 59 0.514 76 0.186 87 0.010 63 0.000 00 0.026 90 0.000 00 0.0367 -6 0.0059 0.0166 0.0506 -2 0.0040 2 (8) Prob > sqrt(diag( 15.71 2 0.046 Results in Table 17 advice to use a fixed effect specification A linear panel data model approximating the coefficients by a fixed effect estimator is therefore run in a final step Table 18 Fixed Effects Linear Regression Model: Production Demand for Gold lnmoney Coef -0.28321 Std Err 0.13782 z -2.05 P>|z| 0.041 95% Confidence Interval -0.01124 0.55518 0.60322 1.71601 -1.29584 0.81403 lncpi -0.55639 0.58765 -0.95 0.345 lngdp -0.24091 0.53460 -0.45 0.653 lnexchange 0.33240 0.20895 0.113 -0.07993 0.74473 lyield -0.07306 0.03522 1.5 -2.07 0.039 -0.14256 -0.00356 syield 0.01325 0.02256 0.59 0.558 -0.03126 0.05776 lnequity 0.15890 0.11464 0.167 -0.06731 0.38512 0.09105 1.3 -1.46 lnuncertain ty _cons -0.13302 0.146 -0.31269 0.04664 17.9562 5.21280 3.44 0.001 7.66984 28.2427 u e  2.16163 0.38848 0.96871 Results from a panel linear regression approach in Table 18 support the identified association between production demand for physical gold and both money supply and long-term interest rates However, the R value of 0.0098 strongly suggests to consider more sophisticated dynamic approaches in light of the dataset on hand An important issue to clarify before calibrating dynamic linear panel models is to identify possible serial correlation in the idiosyncratic errors of the model A Wooldridge (2002) test is implemented and the results are displayed in Table 19 Table 19 Wooldridge Test for Autocorrelation in Panel Data: Production Demand for Gold F (1, 11) = 123.699 Prob > F = 0.0000 With evidence for first-order autocorrelation on hand, a dynamic regression model able to fit low-order moving average correlation in the idiosyncratic error is considered Table 20 Linear Dynamic Panel-Data Estimation: Production Demand for Gold Coef Std Err z P>|z| 95% Confidence Interval lnmoney -0.28081 0.09269 -3.03 0.002 -0.46248 -0.09914 lncpi -0.56467 0.39805 -1.42 0.156 -1.34484 0.21550 lngdp -0.25168 0.36210 -0.70 0.487 -0.96139 0.45804 lnexchange 0.33372 0.14215 2.35 0.019 0.05511 0.61233 lyield -0.07392 0.02378 -3.11 0.002 -0.12054 -0.02731 syield 0.01451 0.01527 0.95 0.342 0.04444 lnequity 0.15612 0.07815 2.00 0.046 0.01541 0.00295 lnuncertain ty _cons -0.12830 0.06130 -2.09 0.036 -0.24845 -0.00816 18.1044 3.50421 5.17 0.000 11.2363 24.9725 0.30929 A significant relationship is identified between the level of demand for gold as an industrial production factor and money supply, the US Dollar exchange rate, long term debt yields, stock market indices, and finally, economic uncertainty In order to close the investigation into the drivers of physical gold demand for industrial production purposes, the Least-Squares Dummy Variables dynamic panel data estimator is used to identify possible effects uncovered by a procedure specifically designed for panels consisting of a relatively small amount of data Table 21 LSDVC Dynamic Panel-Data Estimation: Production Demand for Gold L1.lngdema nd lnmoney Coef 0.58650 Std Err 0.05900 z 9.94 P>|z| 0.000 95% Confidence Interval 0.47086 0.70214 -0.11848 0.11211 -1.06 0.291 -0.33822 0.10125 0.47556 -0.23 0.815 -1.04322 0.82095 lngdp 0.11114 -0.44790 0.43063 -1.04 0.298 -1.29193 0.39612 lnexchange 0.06308 0.17028 0.37 0.711 -0.27066 0.39682 lyield -0.04376 0.02854 -1.53 0.125 -0.09971 0.01219 syield 0.00827 0.01825 0.45 0.651 -0.02750 0.04403 lnequity 0.08902 0.09267 0.96 0.337 -0.09261 0.27064 lnuncertaint y 0.00497 0.07468 0.07 0.947 -0.14139 0.15134 lncpi Results in Table 21 fail to identify a signification association between production demand for gold and any of the variables suggested So reconciling the results identified throughout the section, it seems very difficult to identify an empirical set of variables that would have a significant relationship with the production demand for gold However, some individual variables appeared throughout the different models and deserve to be mentioned in the concluding part of this section National stock indices are deemed to have a positive linear association with production demand; indeed one can easily imagine that higher equity prices reflect greater industrial activity and therefore a higher demand for gold coming from industry as a mean of production A similar argumentation can be made for the negative relationship between production demand and long-term interest rates; where higher long term yields reflect a slowdown of industrial activity and therefore a slowing down of production demand for gold The positive relationship between gold demand and exchange rates to the US Dollar is, on the other hand, somewhat puzzling While discussed above, the relationship is in direct opposition to what was observed when considering the total demand level for gold, suggesting that the argumentation of O’Connor et al (2015) that a weaker US Dollar makes it cheaper for non-American to buy gold, might only hold at the aggregated demand level and not when individual demand aspects are industrial considered Finally, the relationship between demand for gold and money supply was calls for a more formal investigation into the matter Conclusion A formal panel data investigation into the physical gold demand of 17 countries shed light onto the relationship of gold demand and a set of macroeconomic indicators Considering total demand, three variables were identified at the 10 % level: long term yields, short term yields, and the Economic Uncertainty index Results pointing towards the alleged economic safety aspects of gold during troublesome times Jewellery consumption is positively associated with increases in national equity prices, reflecting increases national wealth Furthermore, a negative relationship with inflation indices was identified, indicating that consumers tend to spend more on essential goods when facing periods of decreasing purchasing power This is a major contribution to the field as it proves that the positive relationship between inflation and gold is channeled through investment and not through jewellery consumption So the widely held belief that market actors also invest in jewellery in order to protect themselves from inflation is indeed wrong Results obtained for investment demand indicate that countryeffects are negligible and that a classical linear regression is appropriate Insightful results point towards the positive relationship between investment demand and inflation as well as towards the negative US Dollar effect on global demand for gold Further results indicate that, as is expected, investment demand is positively associated with greater insecurity in the economy Clear results for the industrial demand for gold are more complex to obtain, indicating the importance of country-specific effects and calling for a more precise investigation 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