Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan, ISBN: 978-1-922069-61-0
Hedging Characteristics of Commodity Investment in the Emerging
Markets
Mitchell Ratner* and Chih-Chieh (Jason) Chiu**
Investors derive the greatest risk reduction benefit from portfolio diversification by
holding assets with a low co-movement. As correlations between stocks worldwide
have been increasing over time, interest in alternative assets such as commodities
is also rising. This study examines the risk reducing properties of commodity
investment from the perspective of 10 emerging markets from January 1991 through
December 2013. Tests of GARCH dynamic conditional correlation coefficients
indicate that commodities are portfolio diversifiers, but do not provide a significant
long-term hedge. In times of extreme stock market volatility commodities provide a
weak safe haven against risk in most countries. During the 1994-95 Mexican peso
crisis, the 1997-98 Asian currency crisis, and the 9/11/01 attack, commodities
demonstrate significant safe haven properties. However, commodities do not offer
significant risk reduction in the 2008 global financial crisis.
JEL Codes: G11, G15
1. Introduction
Imperfect correlation among investments is the foundation of most asset allocation strategies. The
potential benefit of portfolio diversification motivates investors to identify assets that have
relatively low correlation with stocks. As global markets continue to converge, investors continue
to seek out alternative assets to enhance diversification. Commodities are often considered assets
that move with relative independence from stocks. In addition to diversification benefits,
commodity holdings can serve as stand-alone investments. Derivative contracts on commodities,
long the domain of hedgers and speculators, are now easy to purchase as commodity-based
mutual funds, exchange traded funds (ETFs), and exchange traded notes (ETNs).
The purpose of this research is to investigate the hedging properties of broad based commodity
investment on emerging market stock risk. Similar studies use time varying methodology to
investigate individual commodities such as gold and oil, and are largely restricted to developed
financial markets (e.g., U.S., U.K.). This study complements and expands the prior literature by
focusing on the emerging markets. The portfolio benefits of investing in commodities are assessed
in three ways: as a diversifier, as a hedge, and as a safe haven.
*Dr. Mitchell Ratner, Department of Finance and Economics, Rider University, USA. Email: ratner@rider.edu
**Dr. Chih-Chieh (Jason) Chiu, Department of Finance and Economics, Rider University, USA.
Email: cchiu@rider.edu
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Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan, ISBN: 978-1-922069-61-0
Following Baur and Lucey (2010), a diversifier is an asset with positive, but imperfect correlation
with stock prices. A hedge is defined as an asset that is consistently uncorrelated or negatively
correlated to stock price movements. A safe haven is an asset that is consistently uncorrelated or
negatively correlated to stock price movements during times of market turmoil.
There are three main findings presented in this study. First, commodities are a consistent portfolio
diversifier as evidenced by significant but relatively low positive correlations between commodities
and stocks in all market indexes. As such, commodities do not serve as a long-term hedge against
stock risk in any market. Second, in times of extreme stock market volatility, commodities are
generally a weak safe haven in most countries (there are three instances of a strong safe haven).
Third, commodities largely provide a strong safe haven against stock risk in most countries during
the Mexican peso crisis, the Asian currency crisis, and the 9/11/01 period. During the 2008 global
financial crisis, commodities do not provide a safe haven.
The remainder of this article is presented as follows. Section 2 provides a review of the literature on
commodity investment and studies of safe haven assets. Section 3 describes the data in detail, and
the relationship between the commodities and stock market indexes of each country. Section 4
provides the methodology and specification of the models used. Section 5 contains the empirical
findings. Section 6 summarizes and concludes the study.
2. Literature
Factors influencing commodity price movements may include inflation, productivity, supply and
demand, world events (e.g., terrorism), and speculation. Aggarwal and Soenen (1988) suggest
that gold can be used as a defensive asset. Chua et al. (1990) find that gold is useful as a portfolio
investment asset with U.S. equities. The authors find that the correlation between gold and U.S.
equities is increasing over time, and speculate that the benefit of holding gold may diminish if the
correlation continues to increase. Faugère and Van Erlach (2005) find that the inverse
relationship between gold and other assets provides an effective hedge. Hillier et al. (2006) find
that gold and other precious metals have potential diversification benefits, hedging capability, and
enhance the performance of equity portfolios. Geman et al. (2008) conclude that the diversification
benefits of investment in oil futures contracts arise from their negative correlation with equities.
Numerous recent studies examine commodities as an asset class. Akey (2006) demonstrate the
benefits of commodities as both stand-alone investments and as diversifiers. Akey (2006) finds
that active trading in commodities provides superior returns over passive investment. Erb and
Harvey (2006) conclude that individual commodities futures, on average, provide returns that are
not statistically different than zero. However, a portfolio of commodities futures contracts might
offer positive returns and risk reduction properties. Gorton and Rouwenhorst (2006) construct an
equally weighted portfolio of commodity futures from 1959-2004. They discover that a portfolio of
commodities has roughly the same return and risk premium as that of an equity portfolio. The
Gorton and Rouwenhorst (2006) commodity index is negatively correlated with equities during the
test period, which provides evidence that commodities are useful as a hedge.
Ebens et al. (2009) develop an asset allocation strategy and demonstrate that investing in
commodities improves portfolio performance. Conover, et al. (2010) examine the long-term
portfolio benefits of investing in the Goldman Sachs Commodity Index using both tactical and
strategic methodology. They find substantial benefits from commodities investment regardless of
the investment style. Nguyen and Sercu (2011) develop a tactical asset allocation model for
trading commodities based on business cycles and monetary policy. The authors find that
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Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan, ISBN: 978-1-922069-61-0
strategic trading in commodities can be beneficial, but underperforms a buy-and-hold strategy.
Further, the authors add that even the diversification benefits demonstrated by prior studies may
be overstated. The authors conclude that commodities investment should be limited to only a
small part of an investment portfolio. The ongoing interest in commodities by the investment
community, combined with the apparent debate in the literature, justifies further analysis of this
topic.
The interest in identifying effective hedge and safe haven assets is growing for two related
reasons. First, rising correlations among country stock markets over time diminish the advantages
of international portfolio diversification (Eun and Lee, 2010). Second, the contagion literature
shows that there is excessive interdependence between stock markets during the 1987 U.S. stock
market crisis, the 1994 Mexican Peso crisis, and the 1997 Asian crisis, and that global crashes
are preceded by local and regional crashes as a domino effect (Forbes and Rigobon, 2002 and
Markwat et al., 2009).
Empirical studies examine the role of gold as an alternative asset for hedging against stock risk.
Bauer and Lucey (2010) use time-varying betas to demonstrate the usefulness of gold as a hedge
and safe haven against stock risk in the U.S., U.K., and Germany. Using a related methodology,
Bauer and McDermott (2010) identify the hedging and safe haven properties of gold against stock
risk in a sample of developed and emerging countries.
The role of oil as a non-financial commodity is also investigated as a hedge against stock risk due
to its importance to the world economy. Studies generally find a positive relationship between oil
and equity returns, but the nature of causality is varied and uncertain (Apergis and Miller, 2009,
and Park and Ratti, 2008). More recently, Ciner (2013) finds that both gold and oil are not safe
havens against equity risk in the U.S. and U.K., but they do offer some protection against currency
risk.
3. The Data
As investors cannot directly purchase a commodity index, selection of an appropriate proxy, such
as a derivative contract, ETF, ETN, or mutual fund, is required. The two most popular commodity
indexes (serving as ETF/ETN benchmarks) are the Standard & Poor‟s Goldman Sachs
Commodity Index (GSCI) and the Dow Jones-UBS Commodity Index (DJ-UBS). (In May 2009,
UBS GA purchased the commodities business of AIG Inc. The DJ-AIG Commodity Index has
been renamed the Dow Jones-UBS Commodity Index.) As Akey (2006) notes, the DJ-UBS is
considered the benchmark for a diversified group of commodities, while the GSCI is heavily
weighted by its position in energy futures contracts. We document a correlation of 0.90 between
the GSCI and DJ-UBS from 1991-2013. This study employs the DJ-UBS to represent a broad
commodity investment.
The DJ-UBS currently represents futures contracts on 19 physical commodities traded on U.S.
markets, with the exception of aluminum, nickel, and zinc that trade on the London Metals
Exchange. To achieve diversification, related groups of commodities (e.g., energy, livestock, etc.)
are limited to no more than 33% of the index. Additionally, no individual commodity may contribute
less than 2% or more than 15% of the index after reweighting.
The DJ-UBS maintains a long position by selling contracts that will expire and purchasing new
contracts on the same underlying asset (known as “rolling” a futures position). This process
creates a problem with commodities investments called “contango” that results from a price
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Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan, ISBN: 978-1-922069-61-0
differential between the more expensive new contracts purchased and the less expensive older
contracts sold. Since commodity index spot prices consistently overstate returns, this study
utilizes the total returns of the commodity indexes to accurately measure the investor‟s actual
return (comparable to including dividend reinvestment when analyzing stock returns).
The sample period for the study is from January 1991 through December 2013, corresponding to
the creation of the DJ-UBS in January 1991. Local returns represent the perspective of the
emerging markets and avoid the distortion of converting returns into U.S. dollars. Equity market
performance is based on the Datastream Total Stock Market Index for each country. Stock index
prices are the total return indexes including dividends. Selection of the sample countries is based
on the MSCI Emerging Markets Index. MSCI currently identifies 21 countries as emerging
markets. Due to the time period studied and the absence of a sufficient sample size, we select the
10 largest markets in the MSCI index including: Brazil, China, India, Indonesia, Korea, Malaysia,
Mexico, Russia, South Africa, and Taiwan.
Table 1
Descriptive statistics for stock market data and the DJ-UBS
Standard
# of
Mean
Deviation
Min
Max
JarqueMarket
Observ.
(%)
(%)
(%)
(%)
Bera
Brazil
1017
0.375***
3.834
-22.473
25.715
859***
China
1066
0.344**
5.111
-31.095
37.300
2044***
India
1199
0.369***
4.228
-22.812
29.343
1539***
Indonesia
1199
0.311**
4.407
-21.752
31.319
2164***
Korea
1199
0.264**
4.378
-19.948
22.968
466***
Malaysia
1199
0.238**
3.479
-16.159
44.177
4516***
Mexico
1199
0.443***
3.333
-16.209
17.689
478***
Russia
830
0.636**
6.485
-33.243
53.616
4124***
S. Africa
1199
0.346***
2.855
-18.115
13.803
984***
Taiwan
1199
0.187***
3.814
-16.566
16.003
199***
DJ-UBS
1199
0.098*
2.058
-9.936
7.047
211***
Note: *** and ** indicates significance at the 1% and 5% levels, respectively.
Weekly data for all variables in this study are measured from Wednesday-to-Wednesday close. To
achieve stationarity, all indexes are transformed into first-difference form. Daily data suffers from
non-synchronous trading between commodity prices (set in the U.S. time zone) and local
emerging equity markets. Monthly data frequency is too low to capture significant time variation
effects between the variables.
Descriptive statistics for the stock indexes and the DJ-UBS are provided in Table 1. Weekly mean
stock returns are all significantly positive during the sample period. The highest mean appears in
Russia (0.636%) and lowest in Taiwan (0.187%). Standard deviation in stock returns is highest in
Russia (6.485%) and lowest in South Africa (2.855%). By contrast, the mean return of the DJ-UBS
is 0.098% with a standard deviation of 2.058%. The Jarque-Bera test for normality is rejected at
the 1% level for all variables, supporting the use of GARCH analysis. Due to the absence of
available data, observations in Brazil, China, and Russia start later than 1991.
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Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan, ISBN: 978-1-922069-61-0
Fig. 1. Commodity and stock market levels.
stock ------
commodity
Figure 1 provides a graph for each emerging market stock index against the DJ-UBS in nondifferenced form. All stock indexes appear to be positively correlated with the DJ-UBS, rise prior to
the global financial crisis period of 2007-2009, decline during the crisis period, and begin to
experience a noticeable recovery in 2009. In contrast, the DJ-UBS has not recovered its pre-crisis
level compared with the stock indexes. Korea, Malaysia, and most notably Taiwan experience
more significant volatility in the Asian crisis period starting in 1997.
4. Methodology
Dynamic conditional correlation (DCC) is a technique developed by Engle (2002) to examine timevarying correlation. In contrast to a constant correlation model, the time-varying nature of the
technique allows correlations to be positive, negative, or zero. The procedure uses GARCH to
generate time-varying estimates of the conditional co-movement between assets implemented as:
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Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan, ISBN: 978-1-922069-61-0
rt |It-1 N(0 Ht )
Ht t t t
(1)
(2)
where rt is the k x 1 demeaned vector of variables conditional on information I t-1, and is assumed
to be conditionally multivariate normal. Ht is the covariance matrix where t is the k x k timevarying correlation matrix, and Dt is the k x k diagonal matrix of conditional standardized residuals
estimated from the univariate GARCH models. The general form equation of the likelihood
function of the estimator is given by:
-0.5 ∑Tt 1 (klog(2 ) 2log(| t |) log(| t |) ́ t
-1
)
t t
(3)
There are two variable steps in this procedure. The volatility component D t is maximized in the first
step by replacing t with a k x k identity matrix. This results in reducing the log likelihood to the
sums of the log likelihoods of the univariate GARCH equations. The first order univariate GARCH
models are estimated for the DJ-UBS and stock indexes of each country using the Glosten et al.
(1993) model allowing for asymmetries:
ht c0 a1
2
t-1
b1 ht-1 d1
2
I
t-1 t-1
(4)
where ht is the conditional variance, d1 is the asymmetry term, and It-1=1 if t<0, otherwise I=0. The
correlation component t is maximized in the second step:
t
(1- - )̅
t-1 ́ t-1
(5)
t-1
where the values of the DCC parameters  and  are provided. If  and  are zero then
reduces to ̅, which would indicate that the constant correlation model is appropriate.
t
Following the GARCH estimation the time varying correlations t are extracted from model (5) into
a separate time series for each country. t are regressed on dummy variables representing market
turmoil to test the DJ-UBS as a hedge, safe haven, or diversifier against stock risk:
t
0
1
(rstock q10 )
2
(rstock q5 )
3
(rstock q1 )
(6)
where D represent dummy variables that capture extreme movements in the underlying stock
index at the 10%, 5%, and 1% quantiles of the most negative stock returns. The DJ-UBS is a
weak hedge if 0 is insignificantly different than zero, a strong hedge if 0 is significantly negative,
or a diversifier if 0 is significantly positive. The DJ-UBS is a weak safe haven if the 1, 2, or 3
coefficients are insignificantly different than zero or a strong safe haven if they are significantly
negative. Significantly positive 1, 2, or 3 coefficients indicate that the DJ-UBS is not a safe haven
during periods of extreme stock volatility.
To examine the DJ-UBS as a hedge, safe haven, or diversifier against stock risk during a period of
crisis, a modified version of a dummy variable regression model used by Baur and McDermott
(2010) is empirically tested as:
t
0
1
( exico)
2
(Asia)
3
( /11)
4
(Global)
(7)
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Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan, ISBN: 978-1-922069-61-0
where a dummy variable is set to one (for 20 trading observations) following the start of four
periods of crisis: Mexican peso crisis (12/19/1994), Asian currency crisis (10/20/97), 9/11 attack,
and the global financial crisis (9/10/08). The DJ-UBS is a weak hedge if 0 is insignificantly
different than zero a strong hedge if 0 is significantly negative or a diversifier if 0 is significantly
positive. The DJ-UBS is a weak safe haven in specific crisis periods if the 1, 2, 3, 4 coefficients
are insignificantly different than zero or a strong safe haven if they are significantly negative.
Significantly positive 1, 2, 3, 4 coefficients indicate that the DJ-UBS is not a safe haven during
the specific crisis period.
5. Empirical results
Estimation results of the GARCH models for the DJ-UBS and stock indexes are provided in Table
2. The univariate GARCH parameters (c0, a1, b1, d1) show that most coefficients are statistically
significant at the 1% or 5% levels across most countries. The significant positive asymmetry term
d1 implies that declines in stock indexes are followed by a reduction in volatility. The high degree
of significance among the GARCH parameters indicates both time variation and dependence in
the variance, which supports the use of GARCH-DCC models.
The estimates of the CC parameters and are statistically significant and their sum is close to
one for most countries. This indicates that the volatility process is stable and that there is a high
degree of persistence. All of the coefficients are significant at the 1% level, further supporting
the use of a time varying model as opposed to a constant correlation model.
The time series of the dynamic conditional correlation coefficients ( t) between the DJ-UBS and
stock indexes for each country are extracted from the GARCH model and graphed in Figure 2.
The signs of the correlation coefficients are mostly positive for all countries. In a static sense this
indicates that increases in DJ-UBS are associated with increases in stock indexes. This
relationship indicates that commodities are more likely a portfolio diversifier rather than a hedge.
However, the magnitude and variability of the coefficients is unique to each country. Relatively
brief periods of negative correlation coefficients are observed in all countries except Russia. The
most consistently negative DCC coefficients occur in Taiwan. Negative coefficients imply a
portfolio hedge and possible safe haven. Lastly, correlation coefficients appear to be generally
highest in all countries during the global financial crisis period. As such, the DJ-UBS does not
appear to be an effective hedge or diversifying asset during the global crisis.
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Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan, ISBN: 978-1-922069-61-0
Table 2
Univariate GARCH estimates for stock index returns and the Dow Jones UBS
commodity index.
Brazil
China
India
Indonesia
Korea
c0,commodity
0.000**
0.000**
0.000**
0.000**
0.000**
a1,commodity 0.062***
0.062***
0.062***
0.062***
0.062***
b1commodity
0.940***
0.940***
0.940***
0.940***
0.940***
d1commodity -0.015
-0.015
-0.015
-0.015
-0.015
c0stock
0.000***
0.000***
0.000***
0.000***
0.000
a1stock
0.076***
0.101***
0.128***
0.050***
0.073***
b1stock
0.842***
0.850***
0.870***
0.864***
0.869***
d1stock
0.095***
0.057**
-0.021
0.133***
0.092***
0.842***
0.850***
0.870*
0.864***
0.869**
0.095***
0.057*** -0.021***
0.133***
0.092***
Malaysia
Mexico
Russia
S. Africa Taiwan
c0,commodity
0.000**
0.000**
0.000**
0.000**
0.000**
a1,commodity 0.062***
0.062***
0.062***
0.062***
0.062***
b1commodity
0.940***
0.940***
0.940***
0.940***
0.940***
d1commodity -0.015
-0.015
-0.015
-0.015
-0.015
c0stock
0.000***
0.000***
0.000**
0.000***
0.000***
a1stock
0.086***
0.061***
0.056*
0.023***
0.058***
b1stock
0.880***
0.880***
0.912***
0.871***
0.901***
d1stock
0.067***
0.096***
0.050**
0.136***
0.053***
0.880**
0.880***
0.912***
0.871***
0.901***
0.067***
0.096***
0.050***
0.136***
0.053***
Note: ***,**,* indicates significance at the 1%, 5%, and 10% levels, respectively.
Models: ht c0 a1 2t-1 b1 ht-1 d1 2t-1 It-1 and t (1- - )̅
t-1 ́ t-1
t-1
Two models test the DJ-UBS as a hedge and safe haven against stock risk based on a similar
methodology used by Baur and McDermott (2010). The first model examines the hedging and
safe haven characteristics of the DJ-UBS during periods of extreme stock market volatility. The
second model focuses on the hedging and safe haven properties of the DJ-UBS during periods of
crisis.
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Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan, ISBN: 978-1-922069-61-0
Fig. 2. GARCH-DCC estimates for commodities and stocks.
Table 3 shows the estimates of regressions from model (6). The CC coefficients t are regressed
on a constant and three dummy variables representing levels of extreme stock volatility quantiles
of 10%, 5%, and 1% of the most negative stock returns in each country‟s stock index. The “hedge”
column represents the model constant 0, which shows a positive relationship between the DJUBS and stock index of each country with significance at the 1% level. A significantly positive
value indicates that the DJ-UBS is a diversifier asset to the stock index, but not a hedge. While
significant diversification properties are demonstrated across all countries, the benefits of the DJ-
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Proceedings of 3rd Global Business and Finance Research Conference
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UBS vary among markets. A smaller coefficient value provides the greatest benefit, i.e., the DJUBS provides a larger benefit in Taiwan (0.129) compared with a lower benefit in Russia (0.344).
The stock quantile regression coefficients ( 1 2 3) represent the safe haven characteristics of
the DJ-UBS against extreme negative stock risk. Negative and significant coefficients indicate that
the DJ-UBS is a strong safe haven within the 5% stock quantile in Brazil (-0.105), and in the 10%
quantile for Russia (-0.052) and Taiwan (-0.057). The insignificant regression coefficients for all
remaining variables indicate that the DJ-UBS serves as a weak safe haven – the DJ-UBS reduces
risk during periods of extreme volatility in the stock market.
Table 3
The DJ-UBS as a hedge and safe haven against stock risk during extreme
stock volatility.
Stock Quantile
Hedge
10%
5%
1%
Market
( 0)
( 1)
( 2)
( 3)
Brazil
0.281***
0.020
-0.105***
-0.011
China
0.227***
0.010
0.025
0.009
India
0.144***
-0.005
-0.005
-0.044
Indonesia
0.147***
-0.015
0.028
-0.018
Korea
0.166***
-0.029
-0.042
-0.010
Malaysia
0.157***
-0.016
0.022
0.050
Mexico
0.187***
-0.026
0.007
-0.015
Russia
0.344***
-0.052**
0.044
0.038
S. Africa
0.276***
-0.006
0.015
0.027
Taiwan
0.129***
-0.057**
-0.003
0.018
Note: ***,**,* indicates significance at the 1%, 5%, and 10% levels, respectively.
Model: t 0 1 (rstock q10 ) 2 (rstock q5 ) 3 (rstock q1 )
The second model tests the DJ-UBS as a hedge and safe haven during the periods of crisis based
on model (7). The results presented in Table 4 show a positive relationship between the DJ-UBS
and the stock indexes in the model constant 0 with significance at the 1% level. This suggests
that the DJ-UBS is a strong diversifier against stock risk as the correlations are relatively low. The
benefit of the DJ-UBS as a safe haven asset appears to change over time. During the Mexican
peso crisis, significantly negative coefficients indicate that the DJ-UBS is a strong safe haven in all
countries at the 1% level, except for South Africa (insignificant 1 coefficients suggest that the DJUBS is a weak safe haven in South Africa).
During the Asian currency crisis, the DJ-UBS provides a strong safe haven in Brazil, India, Korea,
Mexico, South Africa, and Taiwan, and a weak safe haven in China, Indonesia, and Malaysia. A
significantly positive coefficient shows that the DJ-UBS is not a safe haven in Russia. Following
the 9/11 attack in New York, the DJ-UBS is a strong safe haven in all countries with two
exceptions: it is only a weak safe haven in South Africa, and is not a safe haven in India (due to
the significantly positive coefficient).
The results during the global financial crisis indicate that the DJ-UBS does not provide a safe
haven in any country except Korea, where it is a weak safe haven. We speculate that the failure of
the DJ-UBS to provide a safe haven is likely due to the catastrophic nature (systemic risk) of this
event reflected in most financial marketplaces (e.g., stocks, bonds, derivatives, commercial paper,
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Proceedings of 3rd Global Business and Finance Research Conference
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etc.). Systemic risk is the risk of financial system failure that could lead to disruptions or even
collapse of the real economy.
Table 4
The DJ-UBS as a hedge and safe haven against stock risk during crises.
Crisis Period
Hedge
Mexico
Asia
9/11
Global
Market
( 0)
( 1)
( 2)
( 3)
( 4)
Brazil
0.284***
-0.183***
-0.196***
-0.139***
0.284***
China
0.238***
-0.117***
-0.062
-0.369***
0.238***
India
0.147***
-0.115***
-0.085***
0.078**
0.147***
Indonesia
0.147***
-0.110***
0.010
-0.150***
0.147***
Korea
0.171***
-0.140***
-0.252***
-0.217***
0.171
Malaysia
0.160***
-0.114***
0.033
-0.167***
0.160***
Mexico
0.190***
-0.171***
-0.168***
-0.083**
0.190***
Russia
0.344***
NA
0.155*
-0.247***
0.344***
S. Africa
0.277***
-0.041
-0.120***
-0.023
0.277***
Taiwan
0.129***
-0.205***
-0.111***
-0.081**
0.129*
Note: ***,**,* indicates significance at the 1%, 5%, and 10% levels, respectively.
Model: t 0 1 ( exico) 2 (Asia) 3 ( /11) 4 (Global)
6. Summary and Conclusion
Due to the increasing attention on investment in alternative assets, this paper explores the risk
reducing benefits of commodities from the perspective of emerging markets investors. While the
literature documents the use of gold and other specific commodities as hedging instruments
against stock risk, there is little attention given to the hedging role of broad based commodity
investment, especially from the perspective of the emerging markets. Specifically, this study adds
to the literature by evaluating the hedging characteristics of the Dow Jones-UBS commodity index
against stock market risk in 10 emerging markets from 1991 through 2013.
Empirical analysis utilizes GARCH dynamic conditional correlation (DCC) to examine the time
varying relationship between the DJ-UBS and stock market indexes. The major findings are as
follows: First, during periods of extreme negative stock market volatility, the DJ-UBS demonstrates
strong diversification characteristics against stock risk, but it is not an effective hedge in any
country. Second, regression analysis shows that the DJ-UBS is largely a weak safe haven against
stock risk during times of extreme negative stock volatility. Third, the DJ-UBS is generally a strong
safe haven during the Mexican peso crisis, the Asian currency crisis, and the period following the
9/11/2001 attack. However, similar to many other assets, the DJ-UBS does not provide a safe
haven during the recent global financial crisis.
11
Proceedings of 3rd Global Business and Finance Research Conference
9 - 10 October 2014, Howard Civil Service International House, Taipei, Taiwan, ISBN: 978-1-922069-61-0
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