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Does internet search interest for gold move the gold spot, stock and exchange rate markets? A study from India
Resources Policy xxx (xxxx) xxx–xxx
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Does internet search interest for gold move the gold spot, stock and exchange rate markets? A study from India ⁎
Anshul Jain , Pratap Chandra Biswal Management Development Institute Gurgaon, India
A R T I C LE I N FO
A B S T R A C T
Keywords: Google Search Trends Gold Nifty USDINR Exchange rate
India is the largest importer of gold in the world and it is India's second largest import. Gold is treated as a valuable safe haven commodity by investors in India, indicating the extent of its financialisation. This investment demand for gold drives its imports and hence linkages to the exchange rate and equity markets. Movements in the price of gold drives investor interest for the same, which this study aims to capture through Google Search Trends. This study examines the time varying correlation and nonlinear causality amongst Google Search Trends for gold, gold spot price in India, the Indian stock market index Nifty and the USDINR exchange rate. We find presence of bidirectional causality between gold search trends and gold spot price, along with effects on the equity and exchange rate markets. From these results, this study derives important recommendations for both the central bank (Reserve Bank of India) and investors.
1. Introduction Investors’ will always search for quicker and inexpensive sources of information, be it macro level or micro level of information. Due to wide spread internet connectivity through modern broadband connections, information gathering process has changed dramatically over the years. Moreover, internet search engines have made information gathering process much more simpler and quicker. Today, Google Search has emerged as an important source of information, especially for uninformed and retail investors. As the volume of Google searches act as proxy for the effort of uninformed investors’ attempt to gain information (Da et al., 2011), data is generated from every interaction with Google search engine through computer, mobile, watches and other electronic gadgets. Now the real question is: are investors, researchers and policy makers able to use this search volume data for decision making? Extracting information using Google search engine is one thing but drawing useful inferences using search engine volume data requires understanding about dynamic interactions among Google search volume data with various macroeconomic and financial variables. Google search volume data have immense potential for making useful decisions for consumers, investors and policy makers. Numerous papers have already been published exploring the use of Google search trend data predicting and forecasting economic and financial indicators including stock return (Ettredge et al., 2005; Guzman, 2011; Choi and Varian, 2012). However, literature on understanding dynamic interactions between Google search queries and precious metals, which are
⁎
emerging as investment class, is limited and is growing (Baur and Dimpfl, 2016). In this paper, we have made an attempt to understand the dynamic interactions among Google trend data on gold and a select macroeconomics and financial market indicators such as stock return, exchange rate and gold price return in India. We find Google Search Trends for gold to have significant asymmetric causal impact on gold prices, along with the stock market index and the currency exchange rate. Gold prices are also observed to have causal relations with the stock market index. These findings have important implications for policy makers. The remainder of the paper is structured as follows. Section 2 presents a review of the existing literature. Data and Methodology are discussed in Section 3. Section 4 presents and discusses the empirical findings. We conclude in Section 5. 2. Review of literature There exists vast literature on print media coverage and linkages to economic and financial variables (Baker et. al, 2016) but literature on internet based search is still in infancy and growing. Literature on relationship between internet based search and various economic and financial market indicators could be divided into two categories. First, forecasting and nowcasting of economic and financial market indicators based on search queries (Ettredge et al., 2005; Guzman, 2011; Choi and Varian, 2012). Second, contemporaneous and lagged relationship analysis of financial market variables such as returns, volatility, volume
Corresponding author. E-mail addresses: [email protected], [email protected] (A. Jain), [email protected] (P.C. Biswal).
https://doi.org/10.1016/j.resourpol.2018.04.016 Received 31 December 2017; Received in revised form 20 April 2018; Accepted 30 April 2018 0301-4207/ © 2018 Elsevier Ltd. All rights reserved.
Please cite this article as: Jain, A., Resources Policy (2018), https://doi.org/10.1016/j.resourpol.2018.04.016
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Google Trends for gold related internet searches. Google Trend data for search indicates the relative intensity of search volumes over a given period of time and is indicative of broad interest in search term. To ensure that the data collected is relevant to our purpose, various filters were imposed on the Google Trends. The first filter was to remove searches where non-commodity connotations of gold were in use, for example those related to the color gold or to the gold medal awarded to competition winners. The second filter was to consider searches only from the country of India. This was done to ensure that non-Indian trends do not throw up spurious results. The financial variables considered are the spot gold prices in India, the value of the benchmark Nifty 50 stock index and the US Dollar – Indian Rupee exchange rate. The MCXGOLD (mcxgold) index disseminated by the Multi Commodity Exchange (MCX) has been taken as the proxy for spot gold prices in India. MCX collects data for gold prices from the three primary gold trading centers in India (Ahmedabad, New Delhi and Mumbai) and uses a proprietary method to arrive at a spot value for gold in India. The Nifty 50 (nifty) stock index is a free float market capitalization weighted index constituted of 50 stocks trading on the National Stock Exchange (NSE), from 23 sectors of the Indian economy. The NSE is one of the two large stock exchanges in India and accounts for more than 85% of the trading volume in the country. The stocks of this index represent more than 65% of the market cap of the NSE exchange, making the Nifty 50 index a good representative of the Indian capital markets. Most of India's exports and imports are denominated in the US Dollar and hence the USDINR(usdinr) rate is the most relevant foreign exchange rate for this economy. The MCX initiated dissemination of the MCXGOLD index from the 21st of October 2005. Google trends data is available on a weekly frequency over larger periods of time. These constraints resulted in the span of the data being financial data was collected from the Bloomberg service for a period of 23rd October 2005 to the 8th of January 2017. The frequency of the data is weekly. Google Trends data (gtrend) was collected from its website in spans of four years, which was then stitched together. Financial data was collected from the Bloomberg service. The purpose of this study is to examine the linkages between Google Trend data for gold searches, Gold prices in India, the Indian stock market and the USDINR exchange rate. The Brock-Dechert-Scheinkman (BDS, 1996) Test for nonlinear serial dependence was used to test for the presence of nonlinear structure in the series under study. All series exhibited nonlinear serial dependence, indicating that further explorations required nonlinear methodologies. Towards this end, the ADCC-GARCH (Cappiello et. al, 2006) framework has been used to observe the time varying correlation amongst the variables under study. The Non-linear Symmetric and Asymmetric Causality Tests (Kyrtsou and Labys, 2006) have been used to study the causal linkages amongst the variables under study.
and liquidity with internet search trend (Da et al., 2011; Vlastakis and Markellos, 2012; Da et al., 2014). All macroeconomic based studies using internet search queries established that ‘Google Trends’ data are related to macroeconomics series such as unemployment, inflation and sales growth of companies and they improve forecasting performance of the models (Ettredge et al., 2005; Guzman, 2011; Choi and Varian, 2012; Seabold and Coppola, 2015). Relationship between Google Trends series and financial market indicators in literature is widely evident (Da et al., 2011; Vlastakis and Markellos, 2012; Da et al., 2011; Bijl et al., 2016). Preis et al. (2013) and Heiberger (2015) find that patterns in Google Trends data could be useful for developing trading strategies in financial markets. Building on the above studies, Vozlyublennaia (2014) and Baur and Dimpfl (2016) used Google Trends data as proxy for investor's attention to financial markets indicators and analyzed their relationship with prices of commodities such as oil, gold and other precious metals. They found a positive relationship of gold price volatility and search queries and a strong asymmetric effect on negative gold price changes on search queries. Further, Da et al. (2014) developed an index of Google Trends data as a proxy for the investor sentiment that helped explaining mispricing and volatility of stocks and transfer of wealth from mutual funds to bond funds. In a slightly different fashion, Kristoufek (2013) develops portfolios with inversely proportional weights to Google search query volumes and prove that they display lower volatility than equally weighted portfolios. This finding has been extended by Vlastakis and Markellos (2012) and Dimpfl and Jank (2016) who established a relationship between stock market volatility with Google Trends data. Prior research, though limited, largely concentrated on examining bivariate linkages between ‘Google Trend’ data and financial market indicators or macroeconomic series. However, we did not find any study looking at dynamic linkages between ‘Google Trend’ and gold price in a multivariate set up. A multivariate framework is important to verify whether the information contained in Google Search Trends for gold is subsumed by other real economy variables or whether the information is unique. If the multivariate tests indicate that Google Trends do not have unique information, then they can be ignored from a policy perspective. On the other hand, if Google Trend is found to contain unique information, then it is an important indicator for policy makers. This study makes an attempt to examine dynamic interactions between ‘Google Trend’ data on gold search and gold price in India along with two other relevant financial market indicators which are the stock index price and exchange rate. This study has got enough significance for India as it is the largest importer of gold in the world. Gold is also India's second largest imported commodity after crude oil. Fluctuations in gold prices could have significant impact on exchange rate and stock market. It is also observed that when stock market in India is on bullrun, foreign investment flow increases leading to an appreciation of exchange rate. On the other hand during bearish trend in Indian stock market, investors shift their investment destination towards gold which is emerging as an investment asset class in India and considered to a safe heaven. Hence, it is important that investors, policy makers and portfolio managers do understand and appreciate the dynamic linkages between ‘Google Trend’ on gold search and gold price along with exchange rate and stock market. It could be altogether possible that relationship between google trend and gold price might impact the dynamic linkages among gold price, exchange rate and stock price. This paper aims to examine the dynamic contemporaneous linkages among Google Trend, gold price, stock market and exchange rate in India using ADCC-GARCH model. We also test lead-lag relationship among the above variables by applying non-linear non-causality tests.
3.1. Asymmetric generalised dynamic conditional correlation - GARCH models The DCC-GARCH model is used to examine the time varying correlations between two or more series and was developed by Engle (2002). Its generalised extension was developed by Sheppard (2002), which was extended by Cappiello, Engle and Sheppard (2006) to develop the AGDCC specification (also referred to as ADCC). This specification is able to capture the asymmetric impact of returns on correlation. A Vector Autoregression (VAR) model is fit to the series and its residuals are standardised by dividing them by their corresponding GARCH conditional standard deviation. Engle (2009) described this process as "De-GARCHing". The DCC model then uses these standardised residuals to estimate the Dynamic Conditional Correlations. The GARCH(p,q) model is estimated using Maximum Likelihood Estimation (MLE) methods. It can be represented by the following equations where yt is a residual from one of the VAR equations:
3. Data and methodology This study utilizes a mix of non-financial and financial data. The variable being studied, outside of the realm of finance literature, is the 2
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yt = θ0 + ϵt
(1)
ϵt ~N (0,σt2)
(2)
log(σt2) = α 0 +
p
Yt = α21
q
∑ j=1 βj log(σt2−j) + ∑i =1 αi ϵt2−i
(3)
ϵt σt
(4)
The Generalised DCC process is then defined (Sheppard, 2002) by Qt as:
Qt = (Q − A′Q A − B′Q B − G′ N G) + A′ϵt −1ϵ′t −1A + B′Qt −1 B + G′nt −1 nt′−1 G 1
SF =
(5) 1
R = diag {Qt }− 2 Qt diag {Qt }− 2
(7)
Y Xt − τ1 − δ21 Xt −1 + α22 t −ct 22 − δ22 Yt −1 + μt 1+Xtc−1τ1 1+Yt − τ 2
(8)
Where, αij and δij are the parameters to be estimated and the residuals are normally distributed. τi are integer delays and ci are constants to be determined prior to estimation by maximizing the likelihood of the model. For this study, delays of one to five and constant exponents of one to two were tested for each model fitted with a pair of variables. Most models had maximum likelihood using a delay of one and constant exponent of two. The test is carried out in two steps. In the first step, the unconstrained model is estimated using OLS. To test for Y causing X, in the second step a constrained model with α12=0 is estimated. The KyrtsouLabys test statistic can be derived from the SSRs of the constrained and unconstrained models and it follows an F distribution. If the test statistic is higher than the critical value, then we can reject the null hypothesis of Y not causing X. The test statistic is as follows:
ϵt is the standardised residual from removing the mean from the VAR residual series. The log of its volatility is modelled in the last equation as a function of its own lagged values and lagged standardised residuals. The β’s represent the persistence of volatility and α’s the GARCH effect. For the purpose of this research, GARCH (1,1) has been used to standardise the residuals. The standardised residual from all VAR equations, ϵi, t , is further standardised with respect to its standard deviation, σi, t as follows: si, t =
Xt − τ1 Y − δ11 Xt −1 + α12 t − τc 22 − δ12 Yt −1 + εt 1+Xtc−1τ1 1+Yt − τ 2
Xt = α11
(SC − SU )/nrestr ~Fn , N − nfree −1 SU /(N − nfree −1) restr
(9)
Where, nfree is the number of free parameters in the model and nrestr-1 is the number of parameters set to zero while testing the constrained model. This study explores the symmetric as well as the asymmetric causal relationship. The symmetric causal relationship indicates the direction of causality amongst the variables but does not indicate the type or size of effect. For this study, the asymmetric test is defined to test the effect of positive or negative changes in the causal variable on the dependent variable. An increase (or decrease) in the causal variable might cause increase or decrease in the dependent variable, which the Asymmetric test will help us ascertain. To test whether nonnegative returns in the series Y cause the series X, an observation (Xi, Yi) is included for regression only if Y(t-τ2) ≥ 0. The test is then run in similar way as defined before. Testing the reverse causality employs the same method with the order of series reversed. Hristu-Varsakelis and Kyrtsou (2007) highlight that asymmetric causality testing "sharpens" the common symmetric causality test. It yields further insights into the impact of the causal variable on the dependent variable.
(6)
where A, B, and G are diagonal parameter matrixes. For the ADCC specification (Cappiello et al., 2006) A = [aij] = [√a], B = [bij] = [√b], G = [gij] = [√g], where the three matrices A, B and G only consist of one element a, b and g respectively. R is the time varying correlation amongst the variables under study and can be plotted against time. The parameters a, b and g are restricted to be positive and to have a total less than one. Dependence on only these parameters is one of the strengths and weaknesses of this model. Irrespective of number of variables, only these three parameters need to be estimated, making it more likely to reach the optimal solution. Contrary to this, the restriction on all the variables to be following the dynamic process defined by these three common parameters is a restrictive condition. When the standardised residuals from two variables rise or fall together, then they will push the correlation up. This elevated level will gradually decrease back to the average level with the passage of time due to complete absorption of information. When the residuals move in different directions, they will pull the correlation down, which will move up with the passage of time. The speed of this process is controlled by the parameters a and b. Parameter g is responsible for the asymmetric effect of positive and negative returns on the correlation. The ADCC-GARCH model will be used to study the time varying correlations amongst the four variables considered in this study. The correlations thus obtained will shed light on the time varying contemporaneous relationships amongst the variables.
4. Results and discussion Table 1 presents the descriptive statistics for the four series (gtrend, mcxgold, nifty and usdinr) under study and the log difference form (dl) of the three financial series. Chart 1 presents the graphs of the series in levels to help visualize the data. It can be observed that the google trends series (gtrend) has the maximum skewness, maximum kurtosis and also relative difference between its maximum and minimum is higher as compared to other series. These deviations in behavior from the other series point to it not being a financial series. We would like to highlight the period of April 2013 in Chart 1. In this period, Google Search Trends for gold spiked by a factor of 4, gold spot was on the decline, stock markets were volatile and currency was sharply depreciating. In the next 6 months, gold prices had a sharp reversal, Google Search Trends for gold came down to normal ranges and the depreciation of currency was halted. This indicates possible causal relations amongst the variables under study, which are explored later. The ADCC-GARCH methodology takes as input the residuals from a Vector Autoregressive (VAR) model. To appropriately fit the VAR model, the series were examined for the presence of unit root using five different tests for robustness. The tests used were Augmented Dickey Fuller test (Dickey and Fuller, 1979), Phillip Perron test (Phillips and
3.2. Kyrtsou-Labys non linear symmetric and asymmetric causality test Granger's linear test for non-causality (Granger, 1969) is one of the most widely used to study lead lag relationships between variables. Hiemstra and Jones (1994) proposed a nonlinear version of the Granger test for non-causality, but recent research by Diks and Panchenko (2005) has raised issues with the power of the test in large samples. Kyrtsou and Labys (2006) and Hristu-Varsakelis and Kyrtsou (2007) have proposed a nonlinear symmetric and asymmetric test for noncausality by replacing the Vector Autoregression structure of the Granger test with a Mackey Glass model to capture the nonlinear relationships. This test does not suffer from power issues with large sample sizes and has been used by Muñoz and Dickey (2009), Ajmi et. al. (2013) and Bildirici and Turkmen (2015) amongst many others. For a bivariate case with two variables Xt and Yt the model used in this study is as follows: 3
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Table 1 Descriptive Statistics.
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera Probability
gtrend
mcxgold
nifty
usdinr
dlmcxgold
dlnifty
dlusdinr
53.69140 51.75238 433.3333 24.00000 26.33710 6.179040 79.36051 146,100.8 0.000000
20,707.30 22,120.00 32,207.00 6840.000 8231.873 − 0.245875 1.473690 62.78604 0.000000
5602.958 5385.025 8937.750 2316.050 1702.993 0.254055 2.220978 21.12166 0.000026
52.18224 48.99250 68.63250 39.28000 8.854458 0.406180 1.757906 53.78327 0.000000
0.002428 0.00178 0.091252 − 0.11006 0.024135 − 0.21492 5.294013 132.7769 0.000000
0.002079 0.003852 0.143568 − 0.17376 0.031411 − 0.46466 6.434183 308.5201 0.000000
0.000702 9.61E− 05 0.044811 − 0.04691 0.010155 0.185951 5.190202 120.2978 0.000000
Chart 1. Visual Representation of Series under Study.
It can be seen that all three coefficients (a, b and g) are highly significant, indicating that the model is a good fit. The low value of "a" and the high value of "b" indicate that the correlation process is resistant to shocks and reverts to the mean quickly. This indicates that the time varying correlations amongst the variables should be stable without many outliers. Chart 2 presents the graphical output of the time varying correlations from the ADCC-GARCH model. From Chart 2, it is observed that gtrend has a low negative correlation with both gold and nifty for most of the period under study. This negative correlation indicates that increases/declines in the Google Search interest for gold corresponds with the decline/increase in spot prices of gold and the benchmark Nifty index. The presence of moving correlation indicates that google trends for gold does not capture information only about gold prices, rather is a source of other information and uncertainty too. Stable high negative correlation between the exchange rate and Nifty index indicates the importance of exchange rate for the Indian economy. India is amongst the largest importers of crude oil and gold in the world, which are traded in US Dollar values globally. Depreciation in India's currency against US Dollar leads to increased inflation, thus affecting the stock markets and the benchmark Nifty index. Gold prices
Perron, 1988) and KPSS test (Kwiatkowski et al., 1992), ERS test (Elliott et al., 1996) and Ng-Perron test (Ng et al., 2001). Lag length for unit root tests are chosen by Schwarz Information Criteria (SIC). Presence of a significant test statistic in the ADF, PP, ERS and Ng-Perron test indicates absence of unit root whereas it indicates presence of unit root in the case of the KPSS test. Table 2 presents the results. All the five tests concur on the presence-absence of a unit root in all cases. On the level series, gtrend is the only series which is stationary. The other three series are non stationary and have a unit root. Log-differencing these series, as indicated by the “dl” prefix, removes the unit root. These differenced variables are then fitted to a VAR model. The optimal lag length is estimated using the AIC criterion and is found to be four. GARCH and EGARCH models were used to de-GARCH the residuals. It was observed that the GARCH model fit was better than the EGARCH model fit and hence the GARCH model was used henceforth.1 An ADCC model was then fit on the standardized residuals from the GARCH model. The following table highlights the model parameters. (Table 3)
1 Estimates for the VAR model and the de-GARCHing step have not been included due to paucity of space. They are available with the authors and can be shared on request.
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Table 2 Unit Root tests. Variable
ADF Test
PP Test
KPSS Test
ERS Test
Ng-Perron Test
Order of Integration
gtrend mcxgold nifty usdinr dlmcxgold dlnifty dlusdinr
− 12.3247 * − 1.0151 − 2.5471 − 2.5421 − 11.5564 * − 14.8198 * − 14.7932 *
− 13.0145 * − 1.2594 − 2.5887 − 2.3701 − 22.6070 * − 23.5509 * − 21.1697 *
0.1031 0.5130 * 0.2041 * 0.4516 * 0.0474 0.0596 0.0555
− 12.2469 * − 1.4854 − 2.3761 − 1.3998 − 20.3779 * − 4.3830 * − 21.1482 *
− 190.313 * − 5.2347 − 11.1230 − 4.2515 − 283.620 * − 22.8765 − 286.930
I(0) I(1) I(1) I(1) I(0) I(0) I(0)
Note: “*” indicates test statistic significant at 5% level of significance. MZa test statistic value reported for Ng-Perron test.
and the exchange rate have a small but significant positive relation, and gold prices and the index have a negative relation. This indicates the importance of gold as an inflation-hedge safe harbor asset. Investors in India tend to move investments out of the stock markets and into gold, especially during times of high inflation and stock market declines. The time varying correlations motivate us to explore the effects of causal linkages amongst the variables. Tests for causality can be conducted using both traditional linear (Granger) or more contemporary nonlinear (Krytsou-Labys) methodologies. Choice of either depends on the nature of the data, for which purpose the Brock-DechertScheinkman test (Brock et al., 1996) for nonlinear serial dependence
Table 3 Parameters from the ADCC-GARCH model. Parameter
Coefficient
t-stat
a b g Akaike Information Criterion (AIC) Hannan Quinn Information Criterion (HQIC)
0.015841 ** 0.934058 *** 0.003539 *** − 9.0382 − 8.9386
2.4361 55.0732 6.5726
Note: Numbers in parenthesis indicate the t-stat of the coefficient. "*","**" and "***" denote significance at the 10%, 5% and 1% level of significance respectively.
Chart 2. Time Varying Correlations amongst the variables under study.
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The last three pairs do not involve the google trends, and hence are not of primary interest. In line with existing literature, it is observed from the results of pair four, that increase/decrease in gold prices causes decline/increase in the benchmark Nifty index. This relationship can be used to take positions in gold to hedge against stock market volatility. This further reinforces the common belief in gold as a safe haven for retail investors and emergence of gold as an asset class. Pair five indicates that fall in gold prices cause an appreciation in the exchange rate and depreciation in the exchange rate causes an increase in gold prices. This is in line with the market structure; fall in gold demand causes fall in gold prices and eases the import burden, which allows the currency to appreciate. Depreciating currency causes the prices of imported gold to increase. Policy makers in the central bank, whose primary goal is to manage currency volatility, thus need to keep watch on gold prices and google search trends of gold. It is observed from the last pair that the Nifty index has an effect on the exchange rate but not vice versa, which is in line with existing literature. The ADCC test indicates strong negative causality between the Nifty index and the currency rate; yet correlation does not imply presence of causality. Hence the weak causality observed from Nifty index to currency is as expected. Results from both time varying correlation and causal tests point towards similar conclusions. There is an effect of search interest for gold on the prices of gold in the spot markets in India. Both prices and google trends cause each other. Both the gold spot and google trends for gold searches have causal relations with the benchmark Nifty index and the USDINR exchange rate. As India imports most of its crude oil, denominated in USD rates, any movements in the USDINR rate has an effect on the inflation in the economy. These linkages have far reaching implications, both from a portfolio managers and a central bankers perspective.
Table 4 BDS test statistic. Embedding Dimension (m) Series
2
3
4
5
6
gtrend dlmcxgold dlnifty dlusdinr
0.1350 * 0.0228 * 0.0219 * 0.0221 *
0.2226 * 0.0356 * 0.0446 * 0.0414 *
0.2747 * 0.0470 * 0.0641 * 0.0545 *
0.3018 * 0.0550 * 0.0748 * 0.0625 *
0.3119 * 0.0581 * 0.0792 * 0.0631 *
Note: “*” indicates significant test statistic at the 5% level of significance.
(BDS test) was used. Table 4 presents the results from the BDS test. The test statistic is significant at all dimensions for all the series under study, indicating that the null of absence of nonlinear serial dependence is rejected. As indicated by the BDS test, all the series under study have nonlinear serial dependence. Nonlinear Causality tests by Kyrtsou and Labys (2006) were chosen to explore the causal linkages. Both symmetric and asymmetric tests were conducted. Test for symmetric non linear causality only tests for the presence of causality, but asymmetric tests in this context provide information regarding return-direction causality. Results are presented in Table 5. The first two causal tests are on the google trend – gold spot pair. Symmetric causality exists from gold spot to the google trend, but not vice versa. The Asymmetric tests indicate bidirectional causality. As the “P” statistic is significant in this pair when google trend is the causal variable, it indicates that increase in google trend causes gold spot prices. Looking at the positive sign of the corresponding coefficient, it is observed that increase in google trends causes an increase in gold spot prices. With gold spot as the causal variable, it is observed that fall in gold spot causes an increase in google trends. Thus there is a bidirectional causality between gold spot and google search trends for gold. The causality from google search for gold to gold prices can be seen to be driven from retail investor interest in purchasing gold. Fall in gold prices causes an increase in google search trend for gold, as gold is considered a safe haven investment in India and retail investors are always on the lookout for short term downtrends in gold prices, so that they can buy gold at cheaper than the usual rates. In the second pair of tests, it is observed that increased search interest for gold causes a decline in the Nifty index. No direct inference from the same can be drawn, but it is postulated that the relation is observed due to the intervening effect of gold spot prices and its relationship to both the variables. Similarly in the third pair it is observed that increase in search interest for gold causes depreciation in the USDINR exchange rate.
5. Conclusion Individual investors in India consider gold as a safe haven commodity. Change in gold spot prices drives investor interest, which this study has captured through Google Search Trends for gold. India is the largest importer of gold in the world and at the same time gold is India's second largest imported commodity. Thus gold prices are expected to have an effect on the exchange rate and equity markets, which has been observed in this study. This study examines a data span of approximately eleven years and explores time varying correlation and nonlinear causality amongst Google Search Trends for gold, gold spot prices in India, the level of the benchmark Indian equity index (Nifty) and the US dollar – Indian Rupee rate. It was observed that there exists stable negative correlation between the Google Search Trends for gold
Table 5 Symmetric and asymmetric nonlinear causality test statistics and coefficients. Asymmetric
Pair Number 1 1 2 2 3 3 4 4 5 5 6 6
dlgtrend - > dlmcxgold dlmcxgold - > dlgtrend dlgtrend - > dlnifty dlnifty - > dlgtrend dlgtrend - > dlusdinr dlusdinr - > dlgtrend dlmcxgold - > dlnifty dlnifty - > dlmcxgold dlmcxgold - > dlusdinr dlusdinr - > dlmcxgold dlnifty - > dlusdinr dlusdinr - > dlnifty
Symmetric
Test Statistic
Test Statistic
P
N
P
N
1.7556 4.9334* 0.8005 0.7225 0.7243 0.0973 16.1705* 0.0328 12.4257* 6.4206* 0.6177 1.92
22.5409* 1.2718 7.9685* 2.0836 6.6214* 3.3496 7.5131* 2.1434 2.6894 23.4133* 14.6613* 2.253
1.969 8.3294* 0.2505 0.0207 0.0671 3.3719 29.7552* 1.497 4.7615* 3.6422 5.3821* 2.2925
0.0204 0.4693 − 0.0451 0.4101 0.0174 0.0004 − 0.1729 0.0453 0.0131 0.7627 0.0974 − 0.327
− 0.0462 − 0.4556 − 0.0188 0.2109 − 0.0053 − 0.0019 − 0.2771 0.0397 0.0300 0.1398 − 0.0627 − 0.3773
Note: “*” indicates coefficient is significant at 5% level of significance. 6
Coefficient
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searches and gold spot prices, indicating increasing investor interest when gold prices are more affordable. Amongst other observations, a bi-directional nonlinear asymmetric causality was also observed between gold search trends and gold prices. Increase in gold search trends causes an increase in the price of gold, whereas a decrease in gold prices causes an increase in gold search trends. This is in line with investor behavior, where demand goes up on price decline and investor interest drives prices. These observations have significant policy implications. Linkages between gold spot prices, exchange rates and the equity markets point to the financialisation of gold. In the Indian economy, gold is not just any other commodity; it is a safe haven asset and a store of value. As gold is India's second largest import, its price changes drive linkages to the exchange rate and the equity markets. Portfolio investors would benefit from including gold in their portfolios. Its negative correlation with the equity market index indicates the diversification benefits it would provide when made part of an equity portfolio. Gold price also have a causal effect on the equity markets and investors would do well to include these linkages in their portfolio design models. Google search trends for gold searches can be used as a predictive input for these models. Central Bankers, especially India's Reserve Bank of India, have the responsibility to maintain stability in the foreign exchange market. This study highlights how Google Search Trends for gold impacts gold prices and the exchange rate. The linkage to exchange rates is driven via linkages to gold, as increase in gold demand would lead to higher imports, thus putting the currency rate under stress of depreciation. This increased demand is captured in the Google Search Trends, which can be used as an early warning mechanism by the central bankers.
global equity and bond returns. J. Financ. Econ. 4 (4), 537–572. Choi, H., Varian, H., 2012. Predicting the present with Google Trends. Econ. Rec. 88 (s1), 2–9. Da, Z., Engelberg, J., Gao, P., 2011. In search of attention. J. Financ. 66 (5), 1461–1499. Da, Z., Engelberg, J., Gao, P., 2014. The sum of all FEARS investor sentiment and asset prices. Rev. Financ. Stud. 28 (1), 1–32. Dickey, D.A., Fuller, W.A., 1979. Distribution of the estimators for autoregressive time series with a unit root. J. Am. Stat. Assoc. 74 (366a), 427–431. Diks, C., Panchenko, V., 2005. A note on the Hiemstra-Jones test for Granger non-causality. Stud. Nonlinear Dyn. Econ. 9 (2). Dimpfl, T., Jank, S., 2016. Can internet search queries help to predict stock market volatility? Eur. Financ. Manag. 22 (2), 171–192. Elliott, G., Rothenberg, T.J., Stock, J.H., 1996. Efficient tests for an autoregressive unit root. Économ.: J. Econom. Soc. 813–836. Engle, R., 2002. Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. J. Bus. Econ. Stat. 20 (3), 339–350. Engle, R., 2009. Anticipating Correlations: A New Paradigm for Risk Management. Princeton University Press, Princeton. Ettredge, M., Gerdes, J., Karuga, G., 2005. Using web-based search data to predict macroeconomic statistics. Commun. ACM 48 (11), 87–92. Granger, C.W., 1969. Investigating causal relations by econometric models and crossspectral methods. Économ.: J. Econom. Soc. 424–438. Guzman, G., 2011. Internet search behavior as an economic forecasting tool: the case of inflation expectations. J. Econ. Social. Meas. 36 (3), 119–167. Heiberger, R.H., 2015. Collective attention and stock prices: evidence from Google Trends data on Standard and Poor's 100. PloS One 10 (8), e0135311. Hiemstra, C., Jones, J.D., 1994. Testing for linear and nonlinear Granger causality in the stock price‐volume relation. J. Financ. 49 (5), 1639–1664. Hristu-Varsakelis, D., Kyrtsou, C., 2007. Evidence for nonlinear asymmetric causality in us inflation, metal, and stock returns. Discret. Dyn. Nat. Soc. 2007. Kristoufek, L., 2013. Can Google Trends search queries contribute to risk diversification? Sci. Rep., 3, Nat. http://dx.doi.org/10.1038/srep02713. Kwiatkowski, D., Phillips, P.C., 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 (1–3), 159–178. Kyrtsou, C., Labys, W.C., 2006. Evidence for chaotic dependence between US inflation and commodity prices. J. Macroecon. 28 (1), 256–266. Muñoz, M.P., Dickey, D.A., 2009. Are electricity prices affected by the US dollar to Euro exchange rate? The Spanish case. Energy Econ. 31 (6), 857–866. Ng, S., Perron, P., 2001. Lag length selection and the construction of unit root tests with good size and power. Econometrica 69 (6), 1519–1554. Phillips, P.C., Perron, P., 1988. Testing for a unit root in time series regression. Biometrika 75 (2), 335–346. Preis, T., Moat, H.S., Stanley, H.E., 2013. Quantifying trading behavior in financial markets using Google Trends. Sci. Rep., 3, Nat. http://dx.doi.org/10.1038/ srep01684. Seabold, S., Coppola, A., 2015. Nowcasting prices using Google trends: an application to Central America. World Bank Policy Research Working Paper 7398. Sheppard, K. (2002). Understanding the dynamics of equity covariance. Manuscript, UCSD. Vlastakis, N., Markellos, R.N., 2012. Information demand and stock market volatility. J. Bank. Financ. 36 (6), 1808–1821. Vozlyublennaia, N., 2014. Investor attention, index performance, and return predictability. J. Bank. Financ. 41, 17–35.
References Ajmi, A.N., Montasser, G., Nguyen, D.K., 2013. Testing the relationships between energy consumption and income in G7 countries with nonlinear causality tests. Econ. Model. 35, 126–133. Baker, S.R., Bloom, N., Davis, S.J., 2016. Measuring economic policy uncertainty. Q. J. Econ. 131 (4), 1593–1636. Baur, D.G., Dimpfl, T., 2016. Googling gold and mining bad news. Resour. Policy 50, 306–311. Bijl, L., Kringhaug, G., Molnár, P., Sandvik, E., 2016. Google searches and stock returns. Int. Rev. Financ. Anal. 45, 150–156. Bildirici, M.E., Turkmen, C., 2015. Nonlinear causality between oil and precious metals. Resour. Policy 46, 202–211. Brock, W.A., Scheinkman, J.A., Dechert, W.D., LeBaron, B., 1996. A test for independence based on the correlation dimension. Econom. Rev. 15 (3), 197–235. Cappiello, L., Engle, R.F., Sheppard, K., 2006. Asymmetric dynamics in the correlations of
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Resources Policy journal homepage: www.elsevier.com/locate/resourpol
Does internet search interest for gold move the gold spot, stock and exchange rate markets? A study from India ⁎
Anshul Jain , Pratap Chandra Biswal Management Development Institute Gurgaon, India
A R T I C LE I N FO
A B S T R A C T
Keywords: Google Search Trends Gold Nifty USDINR Exchange rate
India is the largest importer of gold in the world and it is India's second largest import. Gold is treated as a valuable safe haven commodity by investors in India, indicating the extent of its financialisation. This investment demand for gold drives its imports and hence linkages to the exchange rate and equity markets. Movements in the price of gold drives investor interest for the same, which this study aims to capture through Google Search Trends. This study examines the time varying correlation and nonlinear causality amongst Google Search Trends for gold, gold spot price in India, the Indian stock market index Nifty and the USDINR exchange rate. We find presence of bidirectional causality between gold search trends and gold spot price, along with effects on the equity and exchange rate markets. From these results, this study derives important recommendations for both the central bank (Reserve Bank of India) and investors.
1. Introduction Investors’ will always search for quicker and inexpensive sources of information, be it macro level or micro level of information. Due to wide spread internet connectivity through modern broadband connections, information gathering process has changed dramatically over the years. Moreover, internet search engines have made information gathering process much more simpler and quicker. Today, Google Search has emerged as an important source of information, especially for uninformed and retail investors. As the volume of Google searches act as proxy for the effort of uninformed investors’ attempt to gain information (Da et al., 2011), data is generated from every interaction with Google search engine through computer, mobile, watches and other electronic gadgets. Now the real question is: are investors, researchers and policy makers able to use this search volume data for decision making? Extracting information using Google search engine is one thing but drawing useful inferences using search engine volume data requires understanding about dynamic interactions among Google search volume data with various macroeconomic and financial variables. Google search volume data have immense potential for making useful decisions for consumers, investors and policy makers. Numerous papers have already been published exploring the use of Google search trend data predicting and forecasting economic and financial indicators including stock return (Ettredge et al., 2005; Guzman, 2011; Choi and Varian, 2012). However, literature on understanding dynamic interactions between Google search queries and precious metals, which are
⁎
emerging as investment class, is limited and is growing (Baur and Dimpfl, 2016). In this paper, we have made an attempt to understand the dynamic interactions among Google trend data on gold and a select macroeconomics and financial market indicators such as stock return, exchange rate and gold price return in India. We find Google Search Trends for gold to have significant asymmetric causal impact on gold prices, along with the stock market index and the currency exchange rate. Gold prices are also observed to have causal relations with the stock market index. These findings have important implications for policy makers. The remainder of the paper is structured as follows. Section 2 presents a review of the existing literature. Data and Methodology are discussed in Section 3. Section 4 presents and discusses the empirical findings. We conclude in Section 5. 2. Review of literature There exists vast literature on print media coverage and linkages to economic and financial variables (Baker et. al, 2016) but literature on internet based search is still in infancy and growing. Literature on relationship between internet based search and various economic and financial market indicators could be divided into two categories. First, forecasting and nowcasting of economic and financial market indicators based on search queries (Ettredge et al., 2005; Guzman, 2011; Choi and Varian, 2012). Second, contemporaneous and lagged relationship analysis of financial market variables such as returns, volatility, volume
Corresponding author. E-mail addresses: [email protected], [email protected] (A. Jain), [email protected] (P.C. Biswal).
https://doi.org/10.1016/j.resourpol.2018.04.016 Received 31 December 2017; Received in revised form 20 April 2018; Accepted 30 April 2018 0301-4207/ © 2018 Elsevier Ltd. All rights reserved.
Please cite this article as: Jain, A., Resources Policy (2018), https://doi.org/10.1016/j.resourpol.2018.04.016
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Google Trends for gold related internet searches. Google Trend data for search indicates the relative intensity of search volumes over a given period of time and is indicative of broad interest in search term. To ensure that the data collected is relevant to our purpose, various filters were imposed on the Google Trends. The first filter was to remove searches where non-commodity connotations of gold were in use, for example those related to the color gold or to the gold medal awarded to competition winners. The second filter was to consider searches only from the country of India. This was done to ensure that non-Indian trends do not throw up spurious results. The financial variables considered are the spot gold prices in India, the value of the benchmark Nifty 50 stock index and the US Dollar – Indian Rupee exchange rate. The MCXGOLD (mcxgold) index disseminated by the Multi Commodity Exchange (MCX) has been taken as the proxy for spot gold prices in India. MCX collects data for gold prices from the three primary gold trading centers in India (Ahmedabad, New Delhi and Mumbai) and uses a proprietary method to arrive at a spot value for gold in India. The Nifty 50 (nifty) stock index is a free float market capitalization weighted index constituted of 50 stocks trading on the National Stock Exchange (NSE), from 23 sectors of the Indian economy. The NSE is one of the two large stock exchanges in India and accounts for more than 85% of the trading volume in the country. The stocks of this index represent more than 65% of the market cap of the NSE exchange, making the Nifty 50 index a good representative of the Indian capital markets. Most of India's exports and imports are denominated in the US Dollar and hence the USDINR(usdinr) rate is the most relevant foreign exchange rate for this economy. The MCX initiated dissemination of the MCXGOLD index from the 21st of October 2005. Google trends data is available on a weekly frequency over larger periods of time. These constraints resulted in the span of the data being financial data was collected from the Bloomberg service for a period of 23rd October 2005 to the 8th of January 2017. The frequency of the data is weekly. Google Trends data (gtrend) was collected from its website in spans of four years, which was then stitched together. Financial data was collected from the Bloomberg service. The purpose of this study is to examine the linkages between Google Trend data for gold searches, Gold prices in India, the Indian stock market and the USDINR exchange rate. The Brock-Dechert-Scheinkman (BDS, 1996) Test for nonlinear serial dependence was used to test for the presence of nonlinear structure in the series under study. All series exhibited nonlinear serial dependence, indicating that further explorations required nonlinear methodologies. Towards this end, the ADCC-GARCH (Cappiello et. al, 2006) framework has been used to observe the time varying correlation amongst the variables under study. The Non-linear Symmetric and Asymmetric Causality Tests (Kyrtsou and Labys, 2006) have been used to study the causal linkages amongst the variables under study.
and liquidity with internet search trend (Da et al., 2011; Vlastakis and Markellos, 2012; Da et al., 2014). All macroeconomic based studies using internet search queries established that ‘Google Trends’ data are related to macroeconomics series such as unemployment, inflation and sales growth of companies and they improve forecasting performance of the models (Ettredge et al., 2005; Guzman, 2011; Choi and Varian, 2012; Seabold and Coppola, 2015). Relationship between Google Trends series and financial market indicators in literature is widely evident (Da et al., 2011; Vlastakis and Markellos, 2012; Da et al., 2011; Bijl et al., 2016). Preis et al. (2013) and Heiberger (2015) find that patterns in Google Trends data could be useful for developing trading strategies in financial markets. Building on the above studies, Vozlyublennaia (2014) and Baur and Dimpfl (2016) used Google Trends data as proxy for investor's attention to financial markets indicators and analyzed their relationship with prices of commodities such as oil, gold and other precious metals. They found a positive relationship of gold price volatility and search queries and a strong asymmetric effect on negative gold price changes on search queries. Further, Da et al. (2014) developed an index of Google Trends data as a proxy for the investor sentiment that helped explaining mispricing and volatility of stocks and transfer of wealth from mutual funds to bond funds. In a slightly different fashion, Kristoufek (2013) develops portfolios with inversely proportional weights to Google search query volumes and prove that they display lower volatility than equally weighted portfolios. This finding has been extended by Vlastakis and Markellos (2012) and Dimpfl and Jank (2016) who established a relationship between stock market volatility with Google Trends data. Prior research, though limited, largely concentrated on examining bivariate linkages between ‘Google Trend’ data and financial market indicators or macroeconomic series. However, we did not find any study looking at dynamic linkages between ‘Google Trend’ and gold price in a multivariate set up. A multivariate framework is important to verify whether the information contained in Google Search Trends for gold is subsumed by other real economy variables or whether the information is unique. If the multivariate tests indicate that Google Trends do not have unique information, then they can be ignored from a policy perspective. On the other hand, if Google Trend is found to contain unique information, then it is an important indicator for policy makers. This study makes an attempt to examine dynamic interactions between ‘Google Trend’ data on gold search and gold price in India along with two other relevant financial market indicators which are the stock index price and exchange rate. This study has got enough significance for India as it is the largest importer of gold in the world. Gold is also India's second largest imported commodity after crude oil. Fluctuations in gold prices could have significant impact on exchange rate and stock market. It is also observed that when stock market in India is on bullrun, foreign investment flow increases leading to an appreciation of exchange rate. On the other hand during bearish trend in Indian stock market, investors shift their investment destination towards gold which is emerging as an investment asset class in India and considered to a safe heaven. Hence, it is important that investors, policy makers and portfolio managers do understand and appreciate the dynamic linkages between ‘Google Trend’ on gold search and gold price along with exchange rate and stock market. It could be altogether possible that relationship between google trend and gold price might impact the dynamic linkages among gold price, exchange rate and stock price. This paper aims to examine the dynamic contemporaneous linkages among Google Trend, gold price, stock market and exchange rate in India using ADCC-GARCH model. We also test lead-lag relationship among the above variables by applying non-linear non-causality tests.
3.1. Asymmetric generalised dynamic conditional correlation - GARCH models The DCC-GARCH model is used to examine the time varying correlations between two or more series and was developed by Engle (2002). Its generalised extension was developed by Sheppard (2002), which was extended by Cappiello, Engle and Sheppard (2006) to develop the AGDCC specification (also referred to as ADCC). This specification is able to capture the asymmetric impact of returns on correlation. A Vector Autoregression (VAR) model is fit to the series and its residuals are standardised by dividing them by their corresponding GARCH conditional standard deviation. Engle (2009) described this process as "De-GARCHing". The DCC model then uses these standardised residuals to estimate the Dynamic Conditional Correlations. The GARCH(p,q) model is estimated using Maximum Likelihood Estimation (MLE) methods. It can be represented by the following equations where yt is a residual from one of the VAR equations:
3. Data and methodology This study utilizes a mix of non-financial and financial data. The variable being studied, outside of the realm of finance literature, is the 2
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yt = θ0 + ϵt
(1)
ϵt ~N (0,σt2)
(2)
log(σt2) = α 0 +
p
Yt = α21
q
∑ j=1 βj log(σt2−j) + ∑i =1 αi ϵt2−i
(3)
ϵt σt
(4)
The Generalised DCC process is then defined (Sheppard, 2002) by Qt as:
Qt = (Q − A′Q A − B′Q B − G′ N G) + A′ϵt −1ϵ′t −1A + B′Qt −1 B + G′nt −1 nt′−1 G 1
SF =
(5) 1
R = diag {Qt }− 2 Qt diag {Qt }− 2
(7)
Y Xt − τ1 − δ21 Xt −1 + α22 t −ct 22 − δ22 Yt −1 + μt 1+Xtc−1τ1 1+Yt − τ 2
(8)
Where, αij and δij are the parameters to be estimated and the residuals are normally distributed. τi are integer delays and ci are constants to be determined prior to estimation by maximizing the likelihood of the model. For this study, delays of one to five and constant exponents of one to two were tested for each model fitted with a pair of variables. Most models had maximum likelihood using a delay of one and constant exponent of two. The test is carried out in two steps. In the first step, the unconstrained model is estimated using OLS. To test for Y causing X, in the second step a constrained model with α12=0 is estimated. The KyrtsouLabys test statistic can be derived from the SSRs of the constrained and unconstrained models and it follows an F distribution. If the test statistic is higher than the critical value, then we can reject the null hypothesis of Y not causing X. The test statistic is as follows:
ϵt is the standardised residual from removing the mean from the VAR residual series. The log of its volatility is modelled in the last equation as a function of its own lagged values and lagged standardised residuals. The β’s represent the persistence of volatility and α’s the GARCH effect. For the purpose of this research, GARCH (1,1) has been used to standardise the residuals. The standardised residual from all VAR equations, ϵi, t , is further standardised with respect to its standard deviation, σi, t as follows: si, t =
Xt − τ1 Y − δ11 Xt −1 + α12 t − τc 22 − δ12 Yt −1 + εt 1+Xtc−1τ1 1+Yt − τ 2
Xt = α11
(SC − SU )/nrestr ~Fn , N − nfree −1 SU /(N − nfree −1) restr
(9)
Where, nfree is the number of free parameters in the model and nrestr-1 is the number of parameters set to zero while testing the constrained model. This study explores the symmetric as well as the asymmetric causal relationship. The symmetric causal relationship indicates the direction of causality amongst the variables but does not indicate the type or size of effect. For this study, the asymmetric test is defined to test the effect of positive or negative changes in the causal variable on the dependent variable. An increase (or decrease) in the causal variable might cause increase or decrease in the dependent variable, which the Asymmetric test will help us ascertain. To test whether nonnegative returns in the series Y cause the series X, an observation (Xi, Yi) is included for regression only if Y(t-τ2) ≥ 0. The test is then run in similar way as defined before. Testing the reverse causality employs the same method with the order of series reversed. Hristu-Varsakelis and Kyrtsou (2007) highlight that asymmetric causality testing "sharpens" the common symmetric causality test. It yields further insights into the impact of the causal variable on the dependent variable.
(6)
where A, B, and G are diagonal parameter matrixes. For the ADCC specification (Cappiello et al., 2006) A = [aij] = [√a], B = [bij] = [√b], G = [gij] = [√g], where the three matrices A, B and G only consist of one element a, b and g respectively. R is the time varying correlation amongst the variables under study and can be plotted against time. The parameters a, b and g are restricted to be positive and to have a total less than one. Dependence on only these parameters is one of the strengths and weaknesses of this model. Irrespective of number of variables, only these three parameters need to be estimated, making it more likely to reach the optimal solution. Contrary to this, the restriction on all the variables to be following the dynamic process defined by these three common parameters is a restrictive condition. When the standardised residuals from two variables rise or fall together, then they will push the correlation up. This elevated level will gradually decrease back to the average level with the passage of time due to complete absorption of information. When the residuals move in different directions, they will pull the correlation down, which will move up with the passage of time. The speed of this process is controlled by the parameters a and b. Parameter g is responsible for the asymmetric effect of positive and negative returns on the correlation. The ADCC-GARCH model will be used to study the time varying correlations amongst the four variables considered in this study. The correlations thus obtained will shed light on the time varying contemporaneous relationships amongst the variables.
4. Results and discussion Table 1 presents the descriptive statistics for the four series (gtrend, mcxgold, nifty and usdinr) under study and the log difference form (dl) of the three financial series. Chart 1 presents the graphs of the series in levels to help visualize the data. It can be observed that the google trends series (gtrend) has the maximum skewness, maximum kurtosis and also relative difference between its maximum and minimum is higher as compared to other series. These deviations in behavior from the other series point to it not being a financial series. We would like to highlight the period of April 2013 in Chart 1. In this period, Google Search Trends for gold spiked by a factor of 4, gold spot was on the decline, stock markets were volatile and currency was sharply depreciating. In the next 6 months, gold prices had a sharp reversal, Google Search Trends for gold came down to normal ranges and the depreciation of currency was halted. This indicates possible causal relations amongst the variables under study, which are explored later. The ADCC-GARCH methodology takes as input the residuals from a Vector Autoregressive (VAR) model. To appropriately fit the VAR model, the series were examined for the presence of unit root using five different tests for robustness. The tests used were Augmented Dickey Fuller test (Dickey and Fuller, 1979), Phillip Perron test (Phillips and
3.2. Kyrtsou-Labys non linear symmetric and asymmetric causality test Granger's linear test for non-causality (Granger, 1969) is one of the most widely used to study lead lag relationships between variables. Hiemstra and Jones (1994) proposed a nonlinear version of the Granger test for non-causality, but recent research by Diks and Panchenko (2005) has raised issues with the power of the test in large samples. Kyrtsou and Labys (2006) and Hristu-Varsakelis and Kyrtsou (2007) have proposed a nonlinear symmetric and asymmetric test for noncausality by replacing the Vector Autoregression structure of the Granger test with a Mackey Glass model to capture the nonlinear relationships. This test does not suffer from power issues with large sample sizes and has been used by Muñoz and Dickey (2009), Ajmi et. al. (2013) and Bildirici and Turkmen (2015) amongst many others. For a bivariate case with two variables Xt and Yt the model used in this study is as follows: 3
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Table 1 Descriptive Statistics.
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis Jarque-Bera Probability
gtrend
mcxgold
nifty
usdinr
dlmcxgold
dlnifty
dlusdinr
53.69140 51.75238 433.3333 24.00000 26.33710 6.179040 79.36051 146,100.8 0.000000
20,707.30 22,120.00 32,207.00 6840.000 8231.873 − 0.245875 1.473690 62.78604 0.000000
5602.958 5385.025 8937.750 2316.050 1702.993 0.254055 2.220978 21.12166 0.000026
52.18224 48.99250 68.63250 39.28000 8.854458 0.406180 1.757906 53.78327 0.000000
0.002428 0.00178 0.091252 − 0.11006 0.024135 − 0.21492 5.294013 132.7769 0.000000
0.002079 0.003852 0.143568 − 0.17376 0.031411 − 0.46466 6.434183 308.5201 0.000000
0.000702 9.61E− 05 0.044811 − 0.04691 0.010155 0.185951 5.190202 120.2978 0.000000
Chart 1. Visual Representation of Series under Study.
It can be seen that all three coefficients (a, b and g) are highly significant, indicating that the model is a good fit. The low value of "a" and the high value of "b" indicate that the correlation process is resistant to shocks and reverts to the mean quickly. This indicates that the time varying correlations amongst the variables should be stable without many outliers. Chart 2 presents the graphical output of the time varying correlations from the ADCC-GARCH model. From Chart 2, it is observed that gtrend has a low negative correlation with both gold and nifty for most of the period under study. This negative correlation indicates that increases/declines in the Google Search interest for gold corresponds with the decline/increase in spot prices of gold and the benchmark Nifty index. The presence of moving correlation indicates that google trends for gold does not capture information only about gold prices, rather is a source of other information and uncertainty too. Stable high negative correlation between the exchange rate and Nifty index indicates the importance of exchange rate for the Indian economy. India is amongst the largest importers of crude oil and gold in the world, which are traded in US Dollar values globally. Depreciation in India's currency against US Dollar leads to increased inflation, thus affecting the stock markets and the benchmark Nifty index. Gold prices
Perron, 1988) and KPSS test (Kwiatkowski et al., 1992), ERS test (Elliott et al., 1996) and Ng-Perron test (Ng et al., 2001). Lag length for unit root tests are chosen by Schwarz Information Criteria (SIC). Presence of a significant test statistic in the ADF, PP, ERS and Ng-Perron test indicates absence of unit root whereas it indicates presence of unit root in the case of the KPSS test. Table 2 presents the results. All the five tests concur on the presence-absence of a unit root in all cases. On the level series, gtrend is the only series which is stationary. The other three series are non stationary and have a unit root. Log-differencing these series, as indicated by the “dl” prefix, removes the unit root. These differenced variables are then fitted to a VAR model. The optimal lag length is estimated using the AIC criterion and is found to be four. GARCH and EGARCH models were used to de-GARCH the residuals. It was observed that the GARCH model fit was better than the EGARCH model fit and hence the GARCH model was used henceforth.1 An ADCC model was then fit on the standardized residuals from the GARCH model. The following table highlights the model parameters. (Table 3)
1 Estimates for the VAR model and the de-GARCHing step have not been included due to paucity of space. They are available with the authors and can be shared on request.
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Table 2 Unit Root tests. Variable
ADF Test
PP Test
KPSS Test
ERS Test
Ng-Perron Test
Order of Integration
gtrend mcxgold nifty usdinr dlmcxgold dlnifty dlusdinr
− 12.3247 * − 1.0151 − 2.5471 − 2.5421 − 11.5564 * − 14.8198 * − 14.7932 *
− 13.0145 * − 1.2594 − 2.5887 − 2.3701 − 22.6070 * − 23.5509 * − 21.1697 *
0.1031 0.5130 * 0.2041 * 0.4516 * 0.0474 0.0596 0.0555
− 12.2469 * − 1.4854 − 2.3761 − 1.3998 − 20.3779 * − 4.3830 * − 21.1482 *
− 190.313 * − 5.2347 − 11.1230 − 4.2515 − 283.620 * − 22.8765 − 286.930
I(0) I(1) I(1) I(1) I(0) I(0) I(0)
Note: “*” indicates test statistic significant at 5% level of significance. MZa test statistic value reported for Ng-Perron test.
and the exchange rate have a small but significant positive relation, and gold prices and the index have a negative relation. This indicates the importance of gold as an inflation-hedge safe harbor asset. Investors in India tend to move investments out of the stock markets and into gold, especially during times of high inflation and stock market declines. The time varying correlations motivate us to explore the effects of causal linkages amongst the variables. Tests for causality can be conducted using both traditional linear (Granger) or more contemporary nonlinear (Krytsou-Labys) methodologies. Choice of either depends on the nature of the data, for which purpose the Brock-DechertScheinkman test (Brock et al., 1996) for nonlinear serial dependence
Table 3 Parameters from the ADCC-GARCH model. Parameter
Coefficient
t-stat
a b g Akaike Information Criterion (AIC) Hannan Quinn Information Criterion (HQIC)
0.015841 ** 0.934058 *** 0.003539 *** − 9.0382 − 8.9386
2.4361 55.0732 6.5726
Note: Numbers in parenthesis indicate the t-stat of the coefficient. "*","**" and "***" denote significance at the 10%, 5% and 1% level of significance respectively.
Chart 2. Time Varying Correlations amongst the variables under study.
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The last three pairs do not involve the google trends, and hence are not of primary interest. In line with existing literature, it is observed from the results of pair four, that increase/decrease in gold prices causes decline/increase in the benchmark Nifty index. This relationship can be used to take positions in gold to hedge against stock market volatility. This further reinforces the common belief in gold as a safe haven for retail investors and emergence of gold as an asset class. Pair five indicates that fall in gold prices cause an appreciation in the exchange rate and depreciation in the exchange rate causes an increase in gold prices. This is in line with the market structure; fall in gold demand causes fall in gold prices and eases the import burden, which allows the currency to appreciate. Depreciating currency causes the prices of imported gold to increase. Policy makers in the central bank, whose primary goal is to manage currency volatility, thus need to keep watch on gold prices and google search trends of gold. It is observed from the last pair that the Nifty index has an effect on the exchange rate but not vice versa, which is in line with existing literature. The ADCC test indicates strong negative causality between the Nifty index and the currency rate; yet correlation does not imply presence of causality. Hence the weak causality observed from Nifty index to currency is as expected. Results from both time varying correlation and causal tests point towards similar conclusions. There is an effect of search interest for gold on the prices of gold in the spot markets in India. Both prices and google trends cause each other. Both the gold spot and google trends for gold searches have causal relations with the benchmark Nifty index and the USDINR exchange rate. As India imports most of its crude oil, denominated in USD rates, any movements in the USDINR rate has an effect on the inflation in the economy. These linkages have far reaching implications, both from a portfolio managers and a central bankers perspective.
Table 4 BDS test statistic. Embedding Dimension (m) Series
2
3
4
5
6
gtrend dlmcxgold dlnifty dlusdinr
0.1350 * 0.0228 * 0.0219 * 0.0221 *
0.2226 * 0.0356 * 0.0446 * 0.0414 *
0.2747 * 0.0470 * 0.0641 * 0.0545 *
0.3018 * 0.0550 * 0.0748 * 0.0625 *
0.3119 * 0.0581 * 0.0792 * 0.0631 *
Note: “*” indicates significant test statistic at the 5% level of significance.
(BDS test) was used. Table 4 presents the results from the BDS test. The test statistic is significant at all dimensions for all the series under study, indicating that the null of absence of nonlinear serial dependence is rejected. As indicated by the BDS test, all the series under study have nonlinear serial dependence. Nonlinear Causality tests by Kyrtsou and Labys (2006) were chosen to explore the causal linkages. Both symmetric and asymmetric tests were conducted. Test for symmetric non linear causality only tests for the presence of causality, but asymmetric tests in this context provide information regarding return-direction causality. Results are presented in Table 5. The first two causal tests are on the google trend – gold spot pair. Symmetric causality exists from gold spot to the google trend, but not vice versa. The Asymmetric tests indicate bidirectional causality. As the “P” statistic is significant in this pair when google trend is the causal variable, it indicates that increase in google trend causes gold spot prices. Looking at the positive sign of the corresponding coefficient, it is observed that increase in google trends causes an increase in gold spot prices. With gold spot as the causal variable, it is observed that fall in gold spot causes an increase in google trends. Thus there is a bidirectional causality between gold spot and google search trends for gold. The causality from google search for gold to gold prices can be seen to be driven from retail investor interest in purchasing gold. Fall in gold prices causes an increase in google search trend for gold, as gold is considered a safe haven investment in India and retail investors are always on the lookout for short term downtrends in gold prices, so that they can buy gold at cheaper than the usual rates. In the second pair of tests, it is observed that increased search interest for gold causes a decline in the Nifty index. No direct inference from the same can be drawn, but it is postulated that the relation is observed due to the intervening effect of gold spot prices and its relationship to both the variables. Similarly in the third pair it is observed that increase in search interest for gold causes depreciation in the USDINR exchange rate.
5. Conclusion Individual investors in India consider gold as a safe haven commodity. Change in gold spot prices drives investor interest, which this study has captured through Google Search Trends for gold. India is the largest importer of gold in the world and at the same time gold is India's second largest imported commodity. Thus gold prices are expected to have an effect on the exchange rate and equity markets, which has been observed in this study. This study examines a data span of approximately eleven years and explores time varying correlation and nonlinear causality amongst Google Search Trends for gold, gold spot prices in India, the level of the benchmark Indian equity index (Nifty) and the US dollar – Indian Rupee rate. It was observed that there exists stable negative correlation between the Google Search Trends for gold
Table 5 Symmetric and asymmetric nonlinear causality test statistics and coefficients. Asymmetric
Pair Number 1 1 2 2 3 3 4 4 5 5 6 6
dlgtrend - > dlmcxgold dlmcxgold - > dlgtrend dlgtrend - > dlnifty dlnifty - > dlgtrend dlgtrend - > dlusdinr dlusdinr - > dlgtrend dlmcxgold - > dlnifty dlnifty - > dlmcxgold dlmcxgold - > dlusdinr dlusdinr - > dlmcxgold dlnifty - > dlusdinr dlusdinr - > dlnifty
Symmetric
Test Statistic
Test Statistic
P
N
P
N
1.7556 4.9334* 0.8005 0.7225 0.7243 0.0973 16.1705* 0.0328 12.4257* 6.4206* 0.6177 1.92
22.5409* 1.2718 7.9685* 2.0836 6.6214* 3.3496 7.5131* 2.1434 2.6894 23.4133* 14.6613* 2.253
1.969 8.3294* 0.2505 0.0207 0.0671 3.3719 29.7552* 1.497 4.7615* 3.6422 5.3821* 2.2925
0.0204 0.4693 − 0.0451 0.4101 0.0174 0.0004 − 0.1729 0.0453 0.0131 0.7627 0.0974 − 0.327
− 0.0462 − 0.4556 − 0.0188 0.2109 − 0.0053 − 0.0019 − 0.2771 0.0397 0.0300 0.1398 − 0.0627 − 0.3773
Note: “*” indicates coefficient is significant at 5% level of significance. 6
Coefficient
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A. Jain, P.C. Biswal
searches and gold spot prices, indicating increasing investor interest when gold prices are more affordable. Amongst other observations, a bi-directional nonlinear asymmetric causality was also observed between gold search trends and gold prices. Increase in gold search trends causes an increase in the price of gold, whereas a decrease in gold prices causes an increase in gold search trends. This is in line with investor behavior, where demand goes up on price decline and investor interest drives prices. These observations have significant policy implications. Linkages between gold spot prices, exchange rates and the equity markets point to the financialisation of gold. In the Indian economy, gold is not just any other commodity; it is a safe haven asset and a store of value. As gold is India's second largest import, its price changes drive linkages to the exchange rate and the equity markets. Portfolio investors would benefit from including gold in their portfolios. Its negative correlation with the equity market index indicates the diversification benefits it would provide when made part of an equity portfolio. Gold price also have a causal effect on the equity markets and investors would do well to include these linkages in their portfolio design models. Google search trends for gold searches can be used as a predictive input for these models. Central Bankers, especially India's Reserve Bank of India, have the responsibility to maintain stability in the foreign exchange market. This study highlights how Google Search Trends for gold impacts gold prices and the exchange rate. The linkage to exchange rates is driven via linkages to gold, as increase in gold demand would lead to higher imports, thus putting the currency rate under stress of depreciation. This increased demand is captured in the Google Search Trends, which can be used as an early warning mechanism by the central bankers.
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