Investigating Gold Investment as an Inflationary Hedge

Abstract

Gold and Indian culture have been sharing an age-old association. India is one of the top two consumers of gold. Gold is the most popular investment avenue because of its ability to provide liquidity. The average monthly price however has grown by 1,588 percent over the whole period from 1979 to 2017 (June). In this article, we intend to investigate gold as an investment to hedge against inflation. The sample period to study the relationship between gold and inflation is 2011–2017 (March). To analyze long-run equilibrium between gold and inflation (consumer price index [CPI]), Johansen’s cointegration approach has been used. The short- and long-run causality between gold and inflation has been studied using vector error correction model (VECM) and Wald test. The results of cointegration indicate that gold and CPI series are cointegrated and bear long-run equilibrium. Both VECM and Wald test results indicate that there is only long-run causality between CPI and gold prices. However, in short run these variables do not show any causality. Thus, we infer that gold investment can be used as hedge against Inflation. The findings of this research have got direct implications for retail investors, portfolio managers, treasury and fund managers, government, and commercial traders.

Keywords

Gold inflation hedge investment cointegration causality

Introduction

Inflation is a necessary ailment for investors and governments all over the globe. The risk of getting lower real return than expected is called purchasing power risk or inflation risk. Purchasing power risk erodes the buying or purchasing power of money and the value of the real return on an investment. Thus, investors, portfolio managers, and governments intend to manage and hedge against purchasing power risk. World Gold Council Report (2017) indicates that for a 1 percent increase in inflation, gold demand increases by 2.6 percent. This reflects that gold may be used as hedging tool against purchasing power risk

Gold has become universal phenomenon across consumers of all income levels. It is not only rich but also poor’s choice for investment. That is the prime reason why gold attracts only 3 percent GST (Jain, 2017). Gold and Indian culture have been sharing an age-old association. It is used for various cultural reasons and as a store of value. Gold is the most popular investment avenue because of its ability to provide liquidity. In present digital era, we have another avenue like paper gold for making gold investment, namely exchange traded funds (ETFs), sovereign gold bonds, gold mutual funds, and e-gold. Financial advisors across the globe are today relaying a lot on gold as an investment product. The major reason behind this is that the value of gold has been increasing and has reached an all-time high in the recent past (Figure 1).

India and China are the world’s top two consumers of gold while the USA, Turkey, and Germany are among the next top three gold consumers (World Gold Council, 2015). India is second largest gold jewelry market in the world (World Gold Council, 2017). Where approximately 20 percent of the total population is covered under health insurance, gold holdings play the role of insurance for the poor in India. Indian people buy gold more for gifts, personal use, status enhancement, or on the festivals such as Dhanteras or Akshaya Tritiya, than during marriages (Jain, 2017). Given the demand and usage of gold in India, and the need to hedge against inflation, this article intends to analytically investigate gold as investment tool to hedge against inflation.

Literature Review, Research Gap, and Research Objectives

There are a few studies on the relationship between gold and inflation. Some of the important studies have been mentioned here. Table 1 shows objectives, research methodology, and major findings of these studies. From literature review, it has been observed that gold has been used as a hedging tool in many facets of investment. It is considered as a safe investment avenue because of its several benefits. It helps to hedge against inflation, political, and currency risk (Aggarwal, 1992; Capie, Mills, & Wood, 2005; Levin, Montagnoli, & Wright, 2006; Worthington & Pahlavani, 2007), hedge against stock returns (Baur & Lucey, 2010), hedge against economic policy uncertainty (Jones & Sackley, 2016), to diversify portfolio (Chua, Stick, & Woodward, 1990; Sherman, 1986), and it has forecast ability and other investment benefits (Lucey, Tully, & Poti, 2004; Smith, 2002). Gold’s durability and worldwide acceptance further places it above other investment avenues.

The most imperative and common purpose of making investment in gold is to guard against inflation or purchasing power risk and to provide liquidity (Ghosh, Levin, MacMillan, & Wright, 2004; Worthington & Pahlavani, 2007). Despite of its well-known benefits, there is a dearth of research on gold’s effectiveness as a hedging tool against purchasing power risk in India—world’s second largest gold consumer economy. To contain devastating impact of gold imports on trade balance, Government of India introduced the sovereign gold bond, gold monetization scheme, and gold coin in year 2015. This motivates us to study the relationship between gold and inflation, and further investigate gold as a hedge against purchasing power risk. This study intends to investigate gold as an investment to hedge against purchasing power risk by:

analyzing long-run equilibrium between gold and inflation and

examining long- and short-run causality between gold and inflation.

Table 1.

Literature Review Details

Year	Authors	Objectives	Data & Research Methodology	Major Findings
2004	Ghosh et al.	Gold as an inflation hedge	Cointegration regression technique,Sample period: 1976−1999	The results confirm theoretical model hypothesis that gold serves as an effective hedge against inflation
2006	Worthington&Pahlavani	To analyzecointegration relation with allowance forendogenous structural breaks	Cointegration analysis with endogenous structural breaks,Zivot and Andrews test	They find a structural break in gold and inflation series in the 1970s. Gold and inflation are strongly cointegrated
2007	Demidova-Menzel & Heidorn	Gold in the investment portfolio	Risk and return analysis using graphical analysis and correlation analysis for the period 2000–2006	They find a great increase in gold prices since 2000. They suggest that Deutsche Börse’s Xetra-Gold minimizes the transaction costs. Moreover, it better than gold pool accounts as it is free of credit risk and better than gold futures as it does not have negative roll returns
2013	Ivanov	The influence of ETFs on the price discovery of gold, silver, and oil	Intra-daily dataETF and futures prices of Gold. ETF used in this study is the SPDR Gold Trust and iShares Silver Trustunit root test, zero mean modelJohansen’s cointegration testSample period March 01, 2009 to August 31, 2009	They report that indeed there is a shift in the price discovery processes for gold and silver in that the ETF market dominates the information shares of both spot and futures markets while the oil market has price discovery occurring still predominantly in the futures market
2012	Ibrahim	To examine the relation between gold return and stock market return in Malaysia	Descriptive analysis, TGARCH/EGARCH error specification for the sample period from March 2001 to 2010	They find that there is low correlation between gold and stock market returns. Also, they report that there is no change co-movement between the gold and stock market during financial crisis
2012	Mulyadi and Anwar	Gold versus stock investment: an econometric analysis	Probit econometric model,sample period:	Gold investment is better than stocks
2012	Singhvi	Gold risk return analysis during recession	Descriptive Secondary data from BSE & NSE (1990–2009), market capitalization-weighted	They find that gold has high risk but compensates that by high returns
2012	Mukul, Kumar & Ray	To compare gold ETF performance of monthly returns	Sample period: from January 2010 to August 2011	They infer that gold ETF has given better return than a diversified equity fund and investors should have a certain portion of their investment in gold
2016	Jones & Sackley	To study the effect of economic policy uncertainty on gold prices	Stepwise regression of multiple lags for each variable	Results suggest that in addition to gold being a hedge against inflation, higher uncertainty in economic policy leads to increase in gold prices.
2015	Hemavathy & Gurusamy	To analyze impact of demographic variables on the preference for different forms of gold for Indian working women	Non-parametric Pearson’s Chi-square, and one-way ANOVA	They identify some important factors such as age, marital status, education, income, role in the family, etc. They conclude that women having higher disposable income generate more investment demand than consumption demand
2015	Marwaha & Arora	To study variables influencing preferences of individual investors for gold (safer investment) with respect to stocks (risky investment)	Primary data collected through an interview, weighted average scores method, paired-sample t-test	Inflation resistance is found as the most important variable, and stability of income is found as the least influencing variable for investors toward investing in gold
2017	Jain	To study gold as an investment in India	Survey of 61,000 households in India	The survey reports that 9 out of 10 rich, 8 out of 10 poor, and 87% of the households hold gold

Source: The authors.

Data and Research Methodology

Data

The sample period of this study is from 1979 to 2017 (June) for descriptive statistics analysis of gold returns. However, the sample period to study the relationship between gold and inflation is from 2011 to 2017 (March). Due to non-availability of consumer price index (CPI) for April–June 2017, we have restricted the study up to March 2017. Monthly data on gold and CPI inflation is collected from the websites of World Gold Council and Reserve Bank of India (RBI), respectively.

Research Methodology

Summary Statistics and Normality Test

The preliminary analysis of data includes descriptive analysis and test of normality. Descriptive statistics encompasses of mean (average monthly return), standard deviation, skewness and kurtosis of both prices and returns series of gold. Jarque–Bera test is performed twice, first for the individual series of gold and then as multivariate extensions under the null hypothesis of normal distribution.

Unit Root Test

Augmented Dickey–Fuller (ADF) test has been used to test unit roots. The select price series are expected to be non-stationary while their return series are expected to be stationary. The test has been conducted in all three forms; constant, trend and intercept, and none. We test the null hypothesis of non-stationarity of series. If the null hypothesis is rejected then the series is said to stationary otherwise it is non-stationary.

Serial Correlation

An important assumption on the error terms of a time series is that there is no covariance between the error terms over time. If the errors are correlated with one another, it would imply that they are “serially correlated.” Thus we need to test this assumption of no autocorrelation. Here, we have used the multivariate Lagrange multiplier (LM) test for residual serial correlation. Multivariate Lagrange multiplier (LM) test is preferred over other serial correlation tests because it is relatively simple to estimate and easily available in econometric packages like EViews. Under the null hypothesis of no serial correlation of order s, the LM statistic is asymptotically distributed as χ2 with s × k2 degrees of freedom.

Johansen’s Cointegration Test

Short- and long-run relationship study has been used to investigate gold investment as inflation hedge by a number of researchers (Chappell & Dowd, 1997; Laurent, 1994; Mahdavi & Zhou, 1997; Moore, 1990). The most common approach to study long-run relationship found in literature is cointegration (Ghosh et al., 2004; Worthington & Pahlavani, 2007), and Johansen’s cointegration test has been used widely in literature to study cointegration or long-run equilibrium relationship between two non-stationary variables (Ivanov, 2013). Thus in this study, we use Johansen’s cointegration approach to test the long-run relationship between gold and inflation.

Two variables are said to be cointegrated if their combination is a stationary variable. For Johansen’s cointegration test, a VAR model with k lags containing the given two variables is represented as follows:

Δ Y_{t} = μ + Π Y_{t - 1} + \sum_{i = 1}^{k - 1} Γ_{i} Δ Y_{t - 1} + ε_{t}

(1)

where Y _t is the vector to be tested for cointegration, is trace statistics and is max eigen-value statistics.

Vector Error Correction Model and Wald Test

We test the relationship between the two markets for causality and error correction using vector error correction model (VECM) shown in Equations (2) and (3).

Δ P_{c, t} = a_{c} + a_{c} P_{c, t - 1} + β_{g} P_{g, t - 1} + \sum_{i = 1}^{k} a_{c, i} Δ P_{c, t - i} + \sum_{j = 1}^{k} a_{g, j} Δ P_{g, t - j} + ε_{c, t}

(2)

Δ P_{g, t} = a_{g} + a_{g} P_{g, t - 1} + β_{c} P_{c, t - 1} + \sum_{i = 1}^{k} a_{g, i} Δ P_{g, t - i} + \sum_{j = 1}^{k} a_{c, j} Δ P_{c, t - j} + ε_{g, t}

(3)

where subscripts c and g refer to CPI and gold futures market respectively. The error correction terms, $a_{m} P_{m, t - 1} + β_{m} P_{m, t - 1}$ or $a_{m} P_{m, t - 1} + β_{m} P_{m, t - 1}$ , represent the speed of adjustment toward long-run equilibrium. Here, both long- and short-term usality has been studied using VECM. If these αs and βs are significant then there is a long-term causality between the two markets. However, the short-run coefficients a_ci, a_gi, a_cj, and a_gj measure short-term causality. Further, short-term causality has been analyzed using Wald test.

Empirical Results and Analysis

Statistics Summary and Normality Test

In Table 2, we have shown descriptive statistics of monthly gold prices and returns from 1979 to 2017 (June). The whole period has been divided into four sub-periods: 1979–1988, 1989–1998, 1999–2009, and 2009–2017 (June). The average monthly return has fallen from 1.02 percent in the period 1979–1988 to 0.65 percent in the period 2009–2017 (June). Similarly the volatility has also decreased from 6.47 percent to 3.71 percent over the same period. The returns throughout have been positively skewed and leptokurtic in nature. This shows that gold returns are not normally distributed. Jarque–Bera probability further shows that the gold returns distribution is non-normal. The average monthly price however has grown by 1,588 percent over the whole period from 1979 to 2017 (June).

Table 2.

Statistics Summary over 1979–2017*

Period→ ↓ Descriptives	Prices (₹/Troy Ounce)				Returns
Period→ ↓ Descriptives	1979–1988	1989–1998	1999–2008	2009–2017*	1979–1988	1989–1998	1999–2008	2009–2017*
Mean	4,348.070	10,422.43	20,130.57	73,394.29	1.024822	0.597741	0.992293	0.653390
SD	1,045.968	2,543.533	8,204.742	14,058.62	6.470933	3.246531	4.156579	3.711205
Skewness	−0.048366	−0.528199	0.954828	−0.731663	1.530180	1.621844	0.173844	0.601763
Kurtosis	2.864165	1.910771	2.773447	2.403908	10.15820	11.96570	4.210286	3.987050
JB prob.	0.932841	0.003164	0.000097	0.004965	0.000000	0.000000	0.019621	0.004965

Source: The authors.

Note: *Till June 2017.

However, the sample period to study the relationship between gold and inflation is 2011–2017 (March). Table 3 shows the results of the multivariate extensions of the Jarque–Bera residual normality test. It reports results of joint test of normality. From the results, it is evident that the null hypothesis of normal distribution cannot be rejected. Thus, we infer that the residuals follow normal distribution.

Table 3.

Results of Normality Test for the Period 2011–2017 (March)

	χ²	df	p-Value
Skewness	4.408076	2	0.1104
Kurtosis	2.985052	2	0.2248
Jarque–Bera	7.393128	2	0.1165

Source: The authors.

Unit Root Test

The ADF test results report that the logarithmic series of gold price and CPI are non-stationary in levels (see Table 4). However, their return series are found to be stationary. The null hypothesis of non-stationary is rejected for return series. So we can proceed to apply Johansen’s cointegration test on log series of gold prices and CPI.

Table 4.

ADF Test Results for Crude Oil Spot and Future Price Series on Levels

Price	Model Form	Critical Values at 5%	Test Statistics	p-value
Ln(CPI)	Trend and intercept	−3.470851	−1.806193	0.6920
Ln(CPI)	None	−1.945199	3.398708	0.9998
Ln(Gold)	Trend and intercept	−3.470032	−2.826142	0.1928
Ln(Gold)	None	−1.945139	0.783452	0.8803
Return on ln(CPI)	Trend and intercept	−3.476275	−6.527908	0.0000*
Return on ln(CPI)	None	−1.945199	−3.666091	0.0004*
Return on ln(Gold)	Trend and intercept	−3.470851	−7.370349	0.0000*
Return on ln(Gold)	None	−1.945199	−7.266281	0.0000*

Source: The authors.

Note: *Represents significant results.

Serial Correlation

The results of multivariate Lagrange multiplier (LM) test for residual serial correlation are shown in Table 5. It is evident from the results that there is no serial correlation in the residuals up to 12 lags at 5 percent level of significance. All the reported p-values are well above 0.05 (level of significance). Thus, we can proceed further to analyze the cointegration and causality relationship between gold and inflation.

Johansen’s Cointegration Test

Before employing Johansen’s cointegration test, it is important to find optimal lag length. Here, optimal lag length has been decided by using Schwarz information criterion (SIC) and Lutkepohl criterion. There is a consensus between the results of these two criterions. Both the criterions suggest an optimal number of lags of 2. Thus, we run Johansen’s test with 2 lag length. In Table 6, the trace test values indicate that the null hypothesis of r = 0 is rejected while null hypothesis of r ≤ 1 is accepted at 5 percent significance level. It means the results suggest that there is one cointegrating relationship between the two variables. However, the λ_max probability value suggests contradictory results. In this case, the null hypothesis of r = 0 cannot be rejected. In case of conflict between the results of trace and max eigen-value statistics, we should prefer trace statistics (Johansen & Juselius, 1990). Thus we can conclude that gold and CPI series are cointegrated and bear long-run equilibrium. Similar results were reported by Worthington and Pahlavani (2007). It means gold investment can be used as guard against inflation risk.

Table 5.

Results of LM Test Serial Correlation

Lags	LM-stat	Prob.
1	1.967161	0.7418
2	3.727418	0.4442
3	2.334081	0.6746
4	9.134291	0.0590
5	5.783486	0.2159
6	8.584744	0.0724
7	3.492213	0.4791
8	7.745236	0.1014
9	1.159761	0.8847
10	1.622847	0.8047
11	1.294018	0.8624
12	6.804022	0.1466

Source: The authors.

Table 6.

Johansen’s Cointegration Test Results

Hypothesized No. of CE(s)	Eigen-value	λ_trace Statistics	Prob. for Trace Test	λ_max Statistics	Prob. for Max. Eigen-value Test
r = 0 (none)	0.15454	15.55033(15.4947)	0.0491	12.42295 (14.26460)	0.0958
r ≤ 1 (at most 1)	0.04138	3.127383 (3.8414)	0.0770	3.127383(3.84147)	0.0770

Source: The authors.

Note: Critical values have been shown in parentheses.

Vector Error Correction Model and Wald Test

In Table 7, we have shown parameter estimates of VECM and Wald test results to test for long- and short-run causality. VECM model given in Equation (4) has been presented in a different form as Equation (5). In Equation (5), C(1) represents the speed of adjustment toward long-run equilibrium. It should be negative in sign and significant. C(1) is found to be –0.066938 with p = 0.0015. So we can say that there is long-run causality from CPI to gold. In Table 7, it has been shown separately for a, α, and β. It is evident from the results that the error correction term in Equation (4) is significant. All these coefficients, namely a, α, and β are significant. This indicates that CPI has influence on gold in the long run. In other words, there is long-run causality running from CPI to gold. Further, short-term causality has been analyzed by using Wald test. The null hypothesis, b₁ = b₂ = 0, of no short-term causality from CPI to gold prices cannot be rejected. It shows that there is no short-term causality running from CPI to gold prices. Thus we conclude that there is only long-run causality between CPI and gold prices. However, in short run these variables do not show any causality. It further supports the findings of Johansen’s cointegration test that gold investment can be used as hedge against purchasing power risk. Similar results were reported by Ghosh et al. (2004).

Table 7.

Parameter Estimates of VECM of Gold Prices and Wald Test

Parameter	Coefficient	Prob.
a	1.83556*	0.0015
α	−0.13451*	0.0015
β	−0.06693*	0.0015
a ₁	0.159396	0.1639
a ₂	−0.107063	0.3468
b ₁	0.087095	0.9145
b ₂	0.215993	0.7784
Wald Test for Short Term Causality
Null hypothesis	Chi²	Prob.
b₁ = b₂ = 0	0.172936	0.9172

Source: The authors.

Note: *Denotes significance of parameters at 5 percent level of significance.

Δ L G P_{t} = a + α L G P_{t - 1} + β L C P_{t - 1} + \sum_{i = 1}^{k} a_{i} Δ L G P_{t - i} + \sum_{j = 1}^{k} b_{i} Δ L G P_{t - j} + ε_{i}

(4)

D (LGP) = C {(1)}^{*} (LCP (- 1) + 2.009650822 * LGP (- 1) - 27.421843167) + C (2) * D (LCP (- 1)) + C (3) * D (LCP (- 2)) + C (4) * D (LGP (- 1)) + C (5) * D (LGP (- 2)) + C (6)

(5)

Discussion

Inflation erodes the buying or purchasing power of money and the value of the real return on an investment. According to Fisher’s effect, inflation rate is inversely related to real rate of interest on an investment. Higher the inflation rate, the lower will be the real rate of interest. In other words, higher inflation rate in an economy decreases the chances of getting high real return or interest rate on investment. Thus, investors, portfolio managers, and governments intend to manage and hedge against inflation risk. Gold and Indian culture have been sharing an age-old association. Gold is used for various cultural reasons and as a store of value. It is the most popular investment avenue because of its ability to provide liquidity. Thus in this article, we intend to investigate gold as an investment to hedge against inflation in Indian context.

From the results and analysis section, it is evident that gold and CPI series are cointegrated and bear long-run equilibrium. VECM and Wald test results indicate that there is only long-run causality between CPI and gold prices. However, in short run these variables do not show any causality. Thus, we conclude that gold investment can be used as hedge against inflation risk. Similar results were reported also by Jones and Sackley (2016) and Ghosh et al. (2004). For a country like India where people buy gold more for gifts, personal use, status enhancement, or on the festivals—the results of this study imply inherent tendency of Indians to hedge against inflation by investing in gold.

Moreover, India’s middle class comprises one of the world’s largest populations of an average 225 million people. This number is expected to reach 500 million’s mark by the year 2025 as per NCAER estimates. This is important to discuss as World Gold Council Report (2017) indicates that income level is the prime deciding factor in gold consumption. It reports that 1 percent jump in income results in 1 percent increase in the gold demand. Also, for a 1 percent increase in inflation, gold demand increases by 2.6 percent. This shows that people buy gold to hedge against inflation. Thus, our results are similar to the findings of World Gold Council Report (2017).

Implications of the Study

The findings of this research have got direct implications for retail investors, portfolio managers, treasury and fund managers, government, commercial traders, and so on. In emerging economy like India, the retail investors may invest in gold through different options such as bullions, gold ETFs, gold bonds, and so on that would help to hedge against purchasing power risk. Similarly, portfolio managers may include gold in their portfolio to guard against inflation and immune real returns of their portfolio.

Concluding Remarks

This study intends to analyze the gold investment as a hedge against Inflation in Indian context. The sample period to study the relationship between gold and inflation is from 2011 to 2017 (March). To analyze long-run equilibrium between gold and inflation (CPI), Johansen’s cointegration approach has been used. The short- and long-run causality between gold and inflation has been studied using VECM and Wald test. Before applying these tests, we diagnose the VAR system for serial correlation and normality. Using multivariate Lagrange multiplier (LM) test for residual serial correlation and multivariate extensions of the Jarque–Bera test for normality of residuals, we do not find any evidence of serial correlation and non-normality. The results of cointegration indicate that gold and CPI series are cointegrated and bear long-run equilibrium. VECM and Wald test results indicate that there is only long-run causality between CPI and gold prices. However, in short run these variables do not show any causality. Thus, we conclude that gold investment can be used as hedge against inflation risk. The results of this study imply that Indians buy gold due to their inherent tendency to hedge against inflation by investing in gold. The findings of this research have got direct implications for retail investors, portfolio managers, treasury and fund managers, government, commercial traders, and so on.

Figure 1.

Source: Authors’ compilation of data obtained from World Gold Council.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

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