Previous studies show that, in financial crises, gold is considered a 'safe haven' investment in developed markets. ⢠It is accepted that the gold price impacts on the value of gold mining companies. .... Correlation matrix for the full period (02/2006 -12/2013). .... gold mining firms' exposure over the collapse of gold price.
An Investigation into the Relationship Between the Gold Price and SA Gold Mining Industry Returns JJ Szczygielski, Z Enslin and E du Toit Faculty of Economic and Management Sciences | Department of Financial Management | University of Pretoria
Introduction
• ‘…the beauty of gold, is that it loves bad news’ (Baur and McDermott, 2010). • Previous studies show that, in financial crises, gold is considered a ‘safe haven’ investment in developed markets. • It is accepted that the gold price impacts on the value of gold mining companies. • The contribution of this study lies in its focus on the South African gold mining industry and the period 2006 to 2013 being divided into three distinct economic cycles, the metals boom, the global financial crisis and a period after. • Regression analysis is applied to investigate the relationship between gold mining returns, the gold price and the Rand Dollar exchange rate within a multifactor model motivated by Arbitrage Pricing Theory. • The results indicate that there is a strong, yet changing, relationship between the gold price, the Rand Dollar exchange rate, and gold mining returns.
Problem and purpose
• The study investigates the impact of US Dollar gold price movements on the value of the Johannesburg Stock Exchange (JSE) Gold Mining Index, as well as that of the US$/ZAR exchange rate. • The main aim is to observe whether investment in gold mining shares can be considered a safe haven investment due to a relationship between movements in the gold price and the value of the gold mining index. • The study contributes to the current body of knowledge by: - extending research into the relationship between the gold price and gold mining stock prices initiated by McDonald and Solnick (1977) and Tufano (1998) to the analysis of this relationship in the fertile research ground of the global financial crisis; - investigating the relationship in a South African context which previous research has found to differ from the context of developed countries (Faff & Hillier, 2004); and - applying the approach of Fang et al. (2007) to divide the period of investigation into three sub-periods to investigate the possible change in the relationship between the gold price and gold mining stocks in different financial situations.
Results
• Returns on the gold mining industry are driven by the gold price, corroborating current main stream findings. • A relationship between returns and the gold price is observed throughout the entire sample period and an analysis of the sub-periods shows that the importance of the gold price in explaining returns for the gold mining industry increases during the global financial crisis and remains more important after the global financial crisis. This indicates that the portfolio diversification properties of investment in gold mining stocks are more pronounced during and after periods of financial shocks. • The exchange rate plays a role in explaining returns during the global financial crisis due to the exchange rate accounting for heightened international systematic risk. • While returns for the gold mining industry are significantly related to the exchange rate over the entire sample period, this finding appears to be driven by significance during the global financial crisis period. Therefore the role the exchange rate plays in explaining gold mining stock returns is ambiguous and, in an emerging economy, appears to be influenced by international systematic risk. • There are other factors important for gold mining industry returns. For example, the market index as measured by the residualised JSE All Share Index is statistically significant throughout the period and the sub-periods. • The changing magnitude of the sensitivity of gold mining industry returns to market movements suggests that the importance of these factors changes over time. Correlation matrix for the full period (02/2006 -12/2013).
Panel A: Full Period Variable
RGUt
RZUt
RMt
RGMt
1
RGUt
0.602***
1
RZUt
-0.103**
-0.301***
1
RMt
0.420***
0.285***
-0.450***
1
Panel B: Subperiods
Data
• Data is obtained from the IRESS Database. • The data is of a weekly frequency for the period January 2006 to December 2013. • The period is divided into three sub-periods: - January 2006 to November 2007 - The metals boom (MB) - December 2007 to June 2010 - The global financial crisis (GFC) - July 2010 to December 2013 - After the global financial crisis (AGFC)
RGMt
RGUt
MB 0.640***
GFC 0.631***
AGFC 0.530***
RZUt
-0.051
-0.201**
0.048
RMt
0.640***
0.391***
0.353***
Note: 1. *** Indicates statistical significance at the 1 percent level of significance. ** Indicates statistical significance at the 5 percent level of significance. * Indicates statistical significance at the 10 percent level of significance.. 2. In Panel B, MB is the Metals Boom period (01/2006-11/2007), GFC is the global financial crisis period (12/2007-06/2010) and AGFC is the after the global financial crisis period (07/2010 – 12/2013). 3. The correlations reported above are for the pre-whitened variables. The reported correlation coefficients show almost no deviation from correlations between the returns on the gold mining industry and the original variable series.
Source: Authors’ own
Breakpoint test results
Period
Full
MB/GFC
GFC/AGFC
Breakpoint
Dec 2007 and July 2010
Dec 2007
July 2010
F-statistic
1.657
16.523
2.045*
13.432*
6.749
8.283*
Log likelihood ratio
Sample period, January 2006 to December 2013. Source: Authors’ own.
Method
• The study uses a multi-factor APT-type model to investigate how the share price of SA listed gold mining companies is affected by changes in the gold price and other factors (see Liow, 2004). This is supplemented by an investigation of how changes in the US$/ZAR exchange rate affect gold company share prices. • Other general economic factors and the general economic state are also recognised, as these affect the share price of gold mining companies (see Khoury, 1984; Rock, 1988). • Following APT tradition, non-gold related and general factors are proxied for using residualised (orthogonalised) returns on the JSE All Share Index (see Burmeister and Wall, 1986; Czaja, Scholz and Wilken, 2010). (1) RGMt = GU RGUt + it • RGMt represents the returns on the gold mining industry and RZUt is the change in the Rand/Dollar exchange rate. Returns on the JSE (2) RGMt = ZUt RZUt + it All Share Index are denoted by RMt. Sensitivities to the variables (3) RGMt = Mt RMt + it are represented by their respective betas. • Equation (1) to (3) are univariate regressions used to RGMt = GU RGUt + ZUt RZUt + Mt RMt + it (4) gain preliminary insight into the explanatory power of each of the variables and equation (4) is the unrestricted model, the multi-factor APT-type specification which is the focus of the analysis. • As the full sample period of January 2006 to December 2013 is turbulent and spans three hypothetically distinct periods, the structural stability of equation (4) is tested using a References CUSUM test. Baur, D.G. & McDermott, T.K. 2010. ‘Is gold a safe • The Chow breakpoint test is then haven? International applied to test whether there are indeed evidence’, Journal of Banking & Finance, 34(8): 1886–1898. three distinct sub-periods as suggested Burmeister, E. & Wall, K.D. 1986. ‘The by Humphreys (2010), Shafiee & Topal arbitrage pricing theory and (2010) and Baur & McDermott (2010) macroeconomic factor measures’, Financial Review, 21(1): 1–21. and Fei & Adibe (2010). Czaja, M., Scholz, H. & Wilkens, M. 2010. ‘Interest • Additional analyses rate risk rewards in stock returns of financial corporations: evidence from Germany’, estimates equation (4) European Financial Management, 16(1): 124-153. for each sub-period. Faff, R. & Hillier, D. 2004. ‘An international investigation of the factors that determine conditional gold betas’, This permits an analysis Financial Review, 39(3): 473–488. of the (potentially Fang, V., Lin, C.T. & Poon, W. 2007. ‘An examination of Australian changing) relationship gold mining firms' exposure over the collapse of gold price in the late 1990s’, International Journal of Accounting & between returns on Information Management, 15(2): 37–49. the gold mining Fei, F. & Adibe, K. 2010. ‘Theories of gold price movements: common wisdom or myths?’, Undergraduate Economic Review, 6(1): 1–25. industry, the gold Humphreys D. 2010. ‘The great metals boom: a retrospective’, Resources price and the Policy, 35(1): 1–13. exchange rate Khoury, S.J. 1984. Speculative markets. New York: MacMillan. across subLiow, K.H. 2004. ‘Time-varying macroeconomic risk and commercial real estate: an asset pricing perspective’, Journal of Real Estate Portfolio Management, 10(1): periods. 47-57. McDonald, J.G. & Solnick, B.H. 1977. ‘Valuation and strategy for gold stocks*’, The Journal of Portfolio Management, 3(3): 29–33. Rock, A. 1988. ‘Gold: protection from calamity but a clunky investment otherwise’, Money, 17(12): 201–202. Shafiee, S. & Topal, E. 2010. ‘An overview of global gold market and gold price forecasting’, Resources Policy, 35(2010): 178–189. Tufano, P. 1998. ‘The determinants of stock price exposure: financial engineering and the gold mining industry’, Journal of Finance, 53(3): 1015–1052.
Note: 1. *** Indicates statistical significance at the 1 percent level of significance. ** Indicates statistical significance at the 5 percent level of significance. * Indicates statistical significance at the 10 percent level of significance. 2. Full refers to estimation over the full period, January 2006 to December 2013. MB/GFC are the Metals Boom and global financial crisis between January 2006 and June 2010. GFC/AGFC are the global financial crisis and after global financial crisis between December 2007 and December 2013.
Source: Authors’ own.
GARCH(1,1) model results Intercept βGUt βZUt βMt
-
ω α1 β1 R¯ 2 AIC F-Statistic Q(1) Q(5) Q 2(1) Q 2(5)
Full
4.41E-05 1.027*** 0.114** 0.627*** 6.07E-05* 0.114*** 0.845***
0.468 -3.784 144.692*** 0.1064 4.363 1.519 3.414 1.506 0.654
-
-
MB
0.003 0.786*** 0.137 0.902*** 0.000 0.043 0.538
0.572 -3.961 39.416*** 0.174 1.954 0.010 4.149 0.010 0.796
GFC
-
-
0.004* 1.220*** 0.391*** 0.302*** 2.06E-05 0.105** 0.894***
0.447 3.464 73.427*** 1.091 7.849 0.248 7.791 0.238 1.493
-
-
AGFC
0.001 1.002*** 0.010 0.674*** 0.001* 0.207* 0.164 0.421 3.978 44.224*** 0.110 1.528 0.166 8.662 0.161 1.883*
ARCH(1) ARCH(5) Note: 1. *** Indicates statistical significance at the 1 percent level of significance. ** Indicates statistical significance at the 5 percent level of significance. * Indicates statistical significance at the 10 percent level of significance. 2. F-statistics are reported for Wald’s test of linear restrictions testing the null hypothesis of coefficients jointly equalling zero (Nelson, 1991; McMillan and Ruiz, 2009). 3. Q(1) and Q(5) are Ljung-Box test statistics for residual serial correlation at the 1st and 5th orders. 4. Q 2(1) and Q 2(5) are Ljung-Box test statistics for squared residual serial correlation at the 1st and 5th orders. 5. ARCH(1) and ARCH(5) are LM test statistics for residual ARCH effects at the 1st and 5th orders.
Source: Authors’ own
Implications
• This study extends the understanding of the changing South African gold mining industry in a world still recovering from the global financial crisis. • The findings of the research are of interest to investors, market analysts and the management of mining companies as they provide more insight on the expected behaviour the mining sector in relation to the gold price in different financial circumstances. • Areas for further research that follow from this study are to investigate the macroeconomic determinants of gold mining industry returns in the broader sense and the risk inherent in gold mining stocks and in the gold price from an investors perspective. The former area is suggested by a finding that the market index, which can be seen as a proxy for factors omitted from the model, explains returns. The latter area may be investigated by studying and comparing the first two moments of the gold mining stock prices and gold.
