Does Gold Love Bad News?
Hedging and Safe Haven of Gold against Stocks and Bonds
Samar Ashour* University of Texas at Arlington
[email protected] (682) 521-7675
September 16, 2013
*Corresponding author: Samar Ashour, doctoral student, University of Texas at Arlington, Box 19449, Department of Finance and Real Estate, Arlington, Texas 76019.
1
Does Gold Love Bad News?
Hedging and Safe Haven of Gold Against stocks and Bonds
SAMAR ASHOUR
ABSTRACT
This paper examines the contemporaneous and dynamic relation between gold excess returns and the stocks and corporate bonds excess returns. The results show that gold can be used as a hedge and safe haven against bonds and stocks. This paper is the first to use CAPM and Fama French three factor models to check the relation between gold market and the stock market as well as corporate bond market. Over the entire sample period from 1986 to 2012, there is a significant relation between gold excess returns and market excess returns as well as the corporate bonds excess returns even after controlling for Fama French three risk factors. Additionally, during extreme market conditions such as crashes and crises, there is a negative relation between stock returns and gold returns in crashes times and there is a zero correlation between stock and bonds returns and gold returns in crises times, and consequently gold can be used as a hedge against stocks and bonds in the tension times. The dynamic relation between three markets using VAR and IRF indicates that gold can be used as a safe haven against stocks and bonds.
I. Introduction
Gold is a well-known as a precious metal and store of value for hundreds of decades.
Gold is still the most famous precious metal for investment purposes, since many people transfer
their investments from the stock and corporate bonds to mitigate their losses in these risky
markets during the recession periods especially during crashes and crises. Gold trading and
prices setting are similar to stock trading.
The purpose of this study is to investigate the role of gold as a safe haven or hedge
against losses in financial markets through the equity market and corporate bond market by using
recent data and by applying CAPM and Fama French three factor models to examine the static
relation between stocks returns and gold returns, and using vector autoregressive model (VAR)
and impulse response function (IRF) to investigate the dynamic relation between the three
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markets. In particular, I examine the inter-relationships between three different markets – gold
market, stock market, and bond market over the period from 1986 to 2012 and during the bad
states of the economy and extreme market performance especially in financial crashes such as
1987 Stock Market crash (Black Monday), September eleven crash in 2001, as well as the most
recent crises (Asian crisis 1997-1998 and the global housing crisis (2007-2009). The contribution
of this study is twofold. First, unlike Baur and Lucey (2010) that examine the relation between
three markets using GARCH and using data from 1995 to 2006 and find that gold cannot be
hedge against bonds, I used more recent and comprehensive data and the most important asset
pricing models (CAPM and Fama French three factor models) and I find a significant negative
relation between bonds and gold. Second, unlike the existing literature that examines either the
relation between gold prices and stock prices (e.g., Baur and Lucey, 2010, and Baur and
McDermott, 2011) or between gold prices and exchange rates (e.g., Capie, Mills and Wood,
2005), or even investigate the riskiness of the gold (McCown et.al., 2006), this study attempts to
link many streams of literature by examining the co-movements between the gold returns, stock
returns, and bond yields using different models. Third, this article is the first attempt to use
CAPM and Fama French three factor models to know the static relation between gold, stocks,
and bonds. Moreover, this is the first study to vector autoregressive model (VAR) and impulse
response function (IRF) to examine the market interdependencies and the dynamic relation
between the three classes of assets during bad states of the economy whether in financial crashes
or financial crises. A relatively little research has been done in this area. My argument is that the
dependence structure between the three markets is not constant and it may be different between
normal and calm periods. Most investors realized great losses especially in the last financial
crisis because of the significant drop in the stock prices and the experienced substantial losses
3
due to this phenomenon. This paper tries to provide a suggestion for investors to include gold in
their investment portfolios especially if gold commove against both stocks and bonds. I find that
the negative relation between stocks and gold is stronger and more economically and statistically
significant than the negative relation between bonds and gold. To verify that these results are not
spurious, I used different models to control for Fama French three factor models; SMB which is
the difference between returns on the small firms and the returns on the large firms, HML the
difference between the high book to market returns and low book to market returns.
Additionally, to know the dynamic relation between three markets, I used the lagged returns of
the three markets and run vector autoregressive regression to make sure that the results are not
spurious due to autocorrelation or the endogenity problem and to get the linkage between three
markets assuming every time that each of them is dependent variables and it is lagged variable as
well as the other two markets in their lagged forms are explanatory variables to this market
return. There is an important study in this area by Baur and McDermott (2011) but they restrict
this study to gold and stocks but my paper will investigate the interrelationship between these
three variables. Moreover Baur and Lucy (2010) use index returns data only to represent stocks
and they did not control for any variables and they did not use subsamples to check the
robustness of the safe haven finding. However, my study uses a novel data set in this area of
literature. My study is distinguished by using the value weighted portfolio of all NYSE,
NASDAQ, and AMEX from CRSP as well as using S&P 500 from CRSP after comparing it with
FRED to correct for missing observation. Additionally, Baur and Lucy (2006) uses MSCI bond
index, but I used both AAA bonds and BAA bonds from Moody’s service by using FRED
website. The results provide convincing evidence that gold can be used as a hedge and safe
haven against stocks and bonds.
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II. Literature Review
Gold has many advantages over investment in stocks and bonds or even foreign exchange
rate. Gold is a tangible good, so that when the investor purchases gold yields an object of
tangible value. First, from the consumption side, the gold investor not only purchases gold for
hedging, but also the investor benefits from using it as jewelry so that the utility of gold is higher
because it is considered consumption good . Second, from the production side the producer can
hold gold inventory to hedge himself form the stock out risk. Third, the gold is very liquid
especially in tension times so that the investor can pay higher prices and realizes lower returns
compared to other risky assets However, people flight to less risky investments during bad states
of economy, and consequently, the price of gold after 2009 is substantially increase. This fact
against the famous investment rule: “buy low, sell high”. This striking high prices lead to an
expectation that it will continue to rise forever, but according to the past gold history, this is not
always the scenario.(Davision, 2013)
My work is related to two strands of literature. One stream focuses on examining the gold
prices, Fama and French (1988) study the relation of metal pricing with business cycles and they
find that positive demand chocks during peak periods lower the metal inventories and causes
severe price conversions according to the theory of storage, while the other strand investigates
the relationship between gold prices and stock prices. Baur and Lucey (2010) investigate whether
gold can serve as a safe haven and hedge against stock and/or bonds during bad times. The
results show that there is a flight to quality from stocks to gold during periods of tension, but not
from bonds to gold. Baur and McDermott (2010) provide international evidence to the role of
gold as a hedge and a safe haven against stock prices. In particular, they run multi-country
analysis using data from 53 international stock markets (which include developed and
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developing markets) from March 1997 to March 2009. Alternatively, Capie, Mills, and Wood
(2005) provide evidence on the role of gold as an exchange rate hedge, and they find that there is
a negative relation between gold prices and Sterling-Dollar and Yen-Dollar exchange rates.
This paper is related to the flight-to-quality literature. Gulko (2002) examines whether
the investors transfer their investments from stocks to bonds in the extreme market conditions
(Gonzalo and Olmo (2005)) stated that investors flight to quality (from stocks to bonds). from
the above discussion, it is clear that there is one strand of literature that finds that Gold is a stock
market hedge, while the other stream provides evidence that Gold is not hedge against corporate
bonds. Given the scarcity of literature on the role of gold as a hedging tool in the financial
markets in general, the contribution of this stems from linking the above two strands of literature
by examining the relation between gold prices, bond prices, and stock prices over the entire
sample period and during the bad states of the economy whether in recessionary and the financial
crises sub periods.
Barro and Misra (2013) find that there is a statistically insignificant covariance of change
in the gold’s real rate with consumption and GDP growth rate and they document that the
predicted returns on gold is very close to the risk free rate. Jaffe (1989) justifies why that the
gold return is lower than stocks and bonds by referring to its non-pecuniary benefits. The gold
investor not only purchase gold for hedging but also the investor benefits from using it as jewelry
so that the utility of gold is higher because it is considered consumption good. Moreover, from
the production side the producer can hold gold inventory to hedge himself form the stock out
risk. Finally, the gold is very liquid especially in tension time so that the investor can pay higher
prices and realizes lower returns compared to other risky assets. McCown (2006) study the
degree of the riskiness of gold by using CAPM model and Arbitrage pricing model and find that
6
the gold has a great hedging ability because it has very similar expected return of Treasury bill.
They provide convincing evidence that gold is “zero beta asset” and gold have no market risk.
However, my paper tries to provide convincing evidence that gold has negative beta (i.e.,
comovements against market portfolio) and to extend this to see the relation between gold
market, stock market portfolio and corporate bond market after taking into account all Fama
French three risk factors.
III. Methodology
1. Hedging Hypothesis:
My first hypothesis, therefore, is that gold may be used as a hedge instrument against
stocks and bonds if there is a zero or negative relation between gold returns and stocks and bonds
returns during the entire sample period from 1986 to 2011. According to Baur and Lucey (2010),
gold can be considered as a hedge against bond and stocks when there is a zero or negative
relation between gold and bonds and/or stocks on average during periods of calm and tensions.
1.1.CAPM
Since I am interested in the response of gold prices to fluctuations in stock returns, I run
the following CAPM regression:
( ) )1(,,10,, tftStocktftgold RRRR −+=− ββ
Where )( ,tgoldR the daily return on gold prices is, )( ,& tPSR is the daily return on S&P index, and
)( ,tfR is the risk-free rate. In order to examine the interrelations between bonds, stock and gold
markets, I incorporate the daily gold return )( ,tbondR to the above CAPM equation, as follows:
( ) ( ) )2(,2,,1,, ftbondtftStocktftgold RRRRRR −+−+=− ββα
7
Equation (2) is the central because it examines the contemporaneous relationship between gold,
stocks, and bond in one equation so that as a robustness, I used many measurement for market
excess returns. I used S&P500, Value weighted returns without excluding dividends which is the
same as market excess return from Fama French Library VWR , value weighted returns
including dividends VWRD, equal weighted returns EWR and equal weighted returns including
dividends EWRD. In empirical result section, I will start by displaying the results of this
equation, and then I will present the results of CAPM and Fama French models.
1.2 Fama French Model:
In order to account for the size and value premium, I regress the return on gold on the
three Fama-French factors, as follows:
( ) ( ) ( ) )3(32,,1,, tttftStockjtftgold SMBHMLRRRR βββα ++−+=−
In addition, I also incorporate the bond returns to equation (3), as follows:
Rgold,t − Rf ,t =α +β1 RStock,t( )+β2 HMLt( ) +β3 SMBt( )+β4 Rbond,t( ) (4)
1.2. Vector Autoregression Model (VAR)
The VAR system treats all of the variables in the model as endogenous variables.
Therefore, the dynamic relation between gold market, stock market, and bond market can be
examined by estimating the following three-vector autoregressive (VAR) system:
Rgold,t − Rf ,t =λ0 + αi Rgold,t−1 − Rf ,ti=1
I
∑ + β j RStock,t− j − Rf ,t + ηkRbond,t−k − Rf ,t +k=1
K
∑ εi,t j=1
J
∑ (5)
)6( 1
,1
,,,,,1
1,0,, ∑ ∑∑= =
−−=
− +−+−+−+=−J
jti
K
ktfktbondktfjtgoldjtf
I
itStockitftStock RRRRRRRR εηβαλ
8
)7( 1
,1
,,,,1
,1,0,, ∑ ∑∑= =
−−=
− +−+−+−+=−J
jti
K
ktfktStockktfjtgoldj
I
itftbonditftbond RRRRRRRR εηβαα
1.3. Impulse Response Function (IRF)
The problem with the variance-covariance estimated from the VAR model is that errors
are unlikely to be diagonal. This means that it is difficult to shock one variable while holding
other variables constant. Therefore, I use the impulse response function (IRF) to measure the
response of gold returns to a lagged unit impulse in stock returns, while the bond returns
constant. Conversely, I focus on examining the impact of the impulse bond returns on gold
returns, while holding the S&P index returns constant.
2. Safe Haven Hypothesis:
My second hypothesis is that there should be flight to quality from corporate bonds and
stock market to gold during extreme market conditions. In other words, the safe haven
hypothesis states that US investors in the stock market and bond market view the gold as a hedge
and safe haven to compensate them for losses during stock market crashes. Gold can be
considered as a safe haven rather than hedge against stocks and bonds if there is a zero or
negative relation between gold returns and bonds and stocks during the tension times only. In
order to examine the safe haven hypothesis, I estimate the following regression models:
( ) )7(,_10,_ tfTensiontStocktfTensiontgold RRRR −+=− ββ
( ) ( ) )8(,,_2,,_10,,_ tftTensionbondtftTensionStocktftTensiongold RRRRRR −+−+=− ααα
( ) ( )( ) )9(_
_
3
2,_,1,_
t
t
Tension
TensiontfTensionttStocktfTensiontgold
SMB
HMLRRRR
β
ββα
+
+−+=−
( ) ( )( ) ( ) )10( _4_3
2,_1,,
fTensiontbondTension
TensiontfTensionStocktftgold
RRSMB
HMLRRRR
t
t
−++
+−+=−
ββ
ββα
9
I will also used the VAR and IRF to test the second Hypothesis as follows:
)11( 1
,1
,,_,,_1
,1_0,,_ ∑ ∑∑= =
−−=
− +−+−+−+=−J
jti
K
ktfktTensionbondktfjtTensionStockj
I
itfTensiontgolditftTensiongold RRRRRRRR εηβαλ
)12(1
,1
,_,_,1
1_0,_ ∑ ∑∑= =
−−=
− +−+−+−+=−J
jti
K
ktfkTensiontbondktfjTensiontgoldjtf
I
iTensiontStockitfTensiontStock RRRRRRRR εηβαλ
)13( 1
,1
,_,_1
,1_0,_ ∑ ∑∑= =
−−=
− +−+−+−+=−J
jti
K
ktfkTensiontStockktfjTensiontgoldj
I
itfTensiontbonditfTensiontbond RRRRRRRR εηβαα
Where )( ,_ tTensiongoldR , )( ,_ tTensionStockR , and )( ,_ tTensionbondR are interaction variables of returns and
dummy variable (Tension) that takes value of 1 during the tension time and zero otherwise.
These interaction variables represent daily return on gold, Market index, and bonds during the
crisis period respectively.
IV. Data and Variable Measurement
My sample period is extended from April, 1, 1986 till October 31, 2012. The sample
period will be divided further into different subsamples to cover the periods of four extreme
market conditions (October 1987 Crash, Asian Crisis 1997-1998, September eleven 2001 crash,
and the recent housing crisis 2007-2009). This study uses daily price data from three different
markets - gold, stock and corporate bonds. The gold prices are the gold fixing price 3:0 P.M.
(London time) in London Bullion Market, based on U.S. dollars obtained from London Bullion
Market Association through FRED. Gold prices are expressed in U.S. dollars per troy ounce
from April 3rd, 1986 to December 31st, 2012. I use the log daily returns of the value-weighted
(VWRt) and equal-weighted (EWRt) portfolios obtained from CRSP. The log interest rate
(TBLt) is computed from the one-month Treasury bill which is also obtained from CRSP.
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Additionally, I obtain the daily data of Corporate Aaa and Baa bonds yields in a percentage form
from the Board of Governors of the Federal Reserve System, which is originated in Moody’s
investor service that includes bonds with remaining maturities as close as possible to 30 years
and drops the bonds if the remaining life falls between 20 years, if the bond is susceptible to
redemption or if the ratings change. Baur uses GARCH to estimate the volatility of stocks
included in his sample. This study also calculates the conditional volatility of gold and stocks
using GARCH (1, 1). I use the excess log daily returns of gold, stocks and bonds. The market
excess returns, the Fama French three risk factors, and the returns on the one month Treasury bill
are obtained from Kenneth French website.
In order to test the safe haven hypothesis, I need to focus on periods of bad states of
economy, specifically speaking, during stock market crashes which are the Black Monday crash
in 1987, Asian crises in 1997, the September 11, 2001 attack, and the recent Housing crisis in
2007. Table (1) presents the starting date for each market crash or financial crisis.
Table (1): Market Crashes and Crisis Periods
V. Empirical Results
1. Preliminary Results:
Figure (1) shows the prices of gold from 1986 to 2012. Although the price of gold was
not changed considerably from 1986 to 2002, it starts to increase dramatically after the recent
financial crisis. In addition, figure (2) show that the gold volatility and stock volatility is very
high during the selected subsamples that is why I concentrated on these four critical periods to
investigate if the gold can be used as a safe haven against stocks and bonds or not.
Crises Starting date Market crash (Black Monday) October 17th, 1987
September eleven Crash September 11, 2001 Asian Crisis July 2nd, 1997
Subprime crisis (Housing Bubble) July 1st, 2007
11
Figure (1) Gold prices and stock prices from 1986 to 2012
The gold prices in this paper are the gold fixing 1price 3:0 P.M. (London time) in London Bullion Market, based in U.S. dollars obtained from London Bullion Market Association through FRED. Gold prices are expressed in U.S. dollars per troy ounce from April 3rd, 1986 to December 31st, 2012. S& P500 daily prices and returns from CRSP.
1 The fixings is an open process at which market participants can transact business on the basis o single quoted price. Orders can be changed throughout the proceedings as the price move higher and lower until the price is moved higher and lower until such time as buyers' and sellers' orders are satisfied and the price is said to be 'fixed'. Orders executed at the fixings are conducted as principal-to-principal transactions between the client and the dealer through whom the order is placed.
0
400
800
1200
1600
2000
86 88 90 92 94 96 98 00 02 04 06 08 10 12
GOLDPRICES
(U.S. Do
llars per
Troy on
ce)
200
400
600
800
1000
1200
1400
1600
86 88 90 92 94 96 98 00 02 04 06 08 10 12
LEVEL�OF�THE�S&P�500�INDEX
S&p500
Prices
in U.S.
Dollars
12
Figure (2) Conditional Volatilities (GARCH (1, 1)) of Stocks and Gold
0
400
800
1200
1600
2000
86 88 90 92 94 96 98 00 02 04 06 08 10 12
LEVEL�OF�THE�S&P�500�INDEX Gold Prices
.000
.001
.002
.003
.004
.005
86 88 90 92 94 96 98 00 02 04 06 08 10 12
SP500VOLATILITY
(Percen
t)
.0000
.0002
.0004
.0006
.0008
.0010
86 88 90 92 94 96 98 00 02 04 06 08 10 12
GOLDVOLATILITY
(Percent)
(Percent)
13
Table (2) shows the summary statistics of gold prices, S&P prices, and the bond prices, in
addition to the excess return on gold, bond, and stock. Table (3) presents the correlation matrix
between gold return, bond return, and stock return.
Table (2): Summary Statistics
1986:04:01-2012:10:31(6510obs) Series Mean Standard Deviation Minimum Maximum
GOLDPRICES 542.921 369.44 252.8 1859 S&PPRICES 863.059 418.77 223.92 1565.150
tftgold RR ,, − 0.010 1.02 -7.85 7.68 VWRD-TBL 0.025 1.15 -17.16 9.52 VWR-TBL 0.016 1.15 -17.15 9.53
EWRD-TBL 0.064 0.94 -10.41 9.52 EWR-TBL 0.057 0.94 -10.42 6.90 SP-TBL 0.018 1.20 -20.5 10.79
AAA-TBL 6.95 1.62 3.22 11.06 BAA-TBL 7.95 1.60 4.45 12.04
Table (3): The Correlation Matrix Gold Return, Bond, Stock Returns
1986:04:01-2012:10:31(6510 Obs.)
Series tftgold RR ,, − VWR-TBL
VWRD-TBL EWR-TBL EWERD-TBL SP-TBL AAA-
TBL BAA-TBL
tftgold RR ,, − 1.00 (0.00)
-0.0236 (0.05)
-0.0236 (0.05)
0.004 (0.74)
0.004 (0.75)
-0.0417 (0.00)
-0.04 (0.00)
-0.03 (0.00)
VWR-TBL 1.00 (0.00)
0.99 (0.00)
0.89 (0.00)
0.89 (0.00)
0.98 (0.00)
-0.007 (0.58)
-0.01 (0.47)
VWRD-TBL 1.00 (0.00)
0.89 0.00)
0.89 (0.00)
0.98 (0.00)
-0.005 (0.66)
-0.007 (0.56)
EWR-TBL 1.00 (0.00)
0.89 (0.00)
0.84 (0.00)
-0.004 (-0.76)
-0.0035 (0.77)
EWRD-TBL 1.00 0.83 -0.004 -0.003
.000
.001
.002
.003
.004
.005
86 88 90 92 94 96 98 00 02 04 06 08 10 12
S&P Volatiltiy Gold Volatility
14
(0.00) (0.00) (0.72) (0.75)
SP-TBL 1.00 (0.00)
-0.005 (0.66)
-0.005 (0.66)
AAA-TBL 1.00 (0.00)
0.96 (0.00)
BAA-TBL 1.00 (0.00)
From the correlation matrix, it is obvious that there is a negative correlation
between gold excess returns and stocks and bonds’ excess returns. Additionally there is
insignificant correlation between stocks and bonds excess returns and consequently, both of them
can be included in the same equation without the presence of the problem of multicollinearity.
2. The Empirical Results of testing the Hedging Hypothesis:
2.1. The Contemporaneous relation between gold, stocks, and bonds.
In this section, I examine the first study hypotheses. I investigate whether gold can be
used as a hedge against stocks and bonds or not. If I find significant negative coefficient of the
stocks and bonds over the entire sample period, this means that investors can depend on gold
investment to hedge themselves from the losses in the stock and bond markets.
Table (4) reports the results of regressing the gold excess returns on the stock
returns (S&P500 excess returns, mean excess value weighted returns VWR, and the mean excess
equal weighted returns) and the corporate bond yields (Aaa, and Baa) during the entire sample
period (1986 - 2012). These results provide convincing evidence that there is a negative relation
between the stock returns and the gold prices returns. In other words, if the value weighted
returns of all NYSE, AMEX, NASDAQ portfolio drops on average by 1%, the gold returns move
to the opposite direction by 0.02% and can be used as a hedge against any surprising falls in the
stock prices. This relation is economically and statistically significant. The negative relation
between S&P500 and gold returns are more dramatic and statistically significant because any
15
drop in S&P500 returns by 1% can be partially compensated by increase in the gold returns by
0.4% the t statistics and the P-values show the probabilities of accepting the null hypothesis. The
negative relation between corporate bonds yields and stocks are more statistically but less
economically significant. We can imply from these results that gold can be used as a hedge
against drop in the stock returns. This result is Consistent with Baur and Lucey (2006)) and Baur
and McDermott (2009). Additionally there is a statistically evidence that gold can be used as
hedge against the drop in the corporate bond yields (Inconsistent with Baur and Lucey (2006)).
My results can add to the existing literature by finding significant negative relation between gold
and bonds by using more comprehensive and current dataset for Aaa and Baa corporate bonds.
Table (4) Hedging predictability of gold over the period from 1986 to 2012
Table (4) reports the relation between gold returns with stocks returns and bonds yields. The dependent variables is the gold excess return tftgold RR ,, − mean excess value-weighted and equal-weighted portfolios, VWR-TBL EWR-TBL excluding dividends and the mean excess value weighted portfolios including dividends VWRD-TBL EWRD-TBL; and S&P 500 mean excess rerun are all proxy to the stock returns. AAA-TBL and BAA-TBL yields are the bonds returns. All rates are represented in annualized percentage points.. The first number in the third column refers to the t-statistic of the test, and the second number refers to p value under the null hypothesis that the gold cannot be used as a hedge against drop in the stock and bond returns.
The next part use many models to test whether this relation is robust across many models
especially after controlling for Fama-French three factor model.
1986:04:01-2012:10:31 (6510 Obs.) tftgold RR ,, − t-statistic
VWR-TBL -0.0212**
-1.93
(0.054)
VWRD-TBL -0.0212** -1.93 (0.054)
EWR-TBL 0.0041 0.31 (0.76)
EWRD-TBL 0.0040 0.30 (0.76)
SP500-TBL -0.04*** -3.38 (0.00)
AAA-TBL -0.00024***
-3.06 (0.00)
BAA-TBL -0.00021*** -2.72 (0.00)
16
2.2.CAPM and Fama-French Results:
Table (5) reports the relation between the gold returns and stocks and bonds returns.
By using CAPM model to determine if gold has zero beta or negative beta with the market
portfolio. Gold can be a strong hedge because its returns commove against the market return.
After adding the bonds to the CAPM model to determine the relation between bonds returns and
gold returns, we find very significant negative relation between bond returns and gold returns.
Even after controlling for Fama French risk factors, the negative relation between market
excess returns and gold remains robust. However, it is obvious that there is a positive relation
between the between small companies returns and the large companies returns. This implies that
small stocks and gold may move
with the same direction.
However, even after controlling
for this positive relation, the
market excess returns as well as
bonds excess returns commove
against gold returns.
Table (5) CAPM and Fama French Results [the entire sample
period] Table (5) documents the relation between the gold returns with the other two markets for the entire sample period using four models. CAPM model, CAPM model including excess bonds returns in the right-hand side, Fama French three factor models, and Fama French Model including bonds excess returns in the right hand side. The GOLDR-TBL is the gold excess return. VWR-TBL is the excess value-weighted return, and S&P 500 is the excess rerun on S&P index return. AAA-TBL and BAA-TBL yields are the bonds returns. All rates are represented in annualized percentage points. t-statistics are in ().
1986:04:01-2012:10:31(6510 Obs.) Series MKTRF
1-VWR-TBL 2-S&P500-TBL
SMB HML BONDS AAA-TBL BAA-TBL
CAPM -0.0209* (-1.91)
-0.035***
17
3- The Empirical Results of testing the Safe Haven Hypothesis:
3.1. The Contemporaneous relation between gold, stocks, and bonds ( During tension
times) Although the gold proves that it can used as a hedge tool against stocks and bonds risks,
This does not mean that it can be used as a safe have. That is why I interact the stocks and bonds
returns with the tension times- dummy variable to test whether that gold can be used as a safe
haven in the crises and crash times whenever the stocks and bonds prices decreases dramatically.
Table (6) Reports the results of regressing the gold returns in the time of crisis on all
interaction terms that represent that stock and bonds returns in the extreme market conditions.
Results show that gold can be used as a safe haven against stocks. Negative relation between two
variables that ranges from (-0.063) using value weighted return portfolio and -0.077 using S&P
500 as a proxy for stock returns. There is no significant relation between bond retunes and the
gold returns in the tension periods. These results indicate that gold can be used as a safe haven
against stock but not against bonds.
Table (6) The contemporaneous relation between gold, stock and bonds in the tension times
(-3.37) CAPM+Bonds -0.0212**
(-1.93) -0.04** (-3.38)
-0.00024*** (-3.06)
-0.00021*** (-2.72)
FF -0.017* (-1.65)
-0.027** (-2.45)
0.07*** (3.83)
0.08*** (3.33)
-0.000922 (-0.04) 0.003 (0.15)
FF+Bonds -0.017* (-1.65) -0.027* (-2.74)
.08*** (3.8)
-0.07*** (3.27)
0.003 (0.16)
-0.00065 (-0.03)
-0.0002*** (-2.97)
-0.00021*** (-2.97)
18
(Safe haven hypothesis) Table (6) reports the relation between gold returns with stocks returns and bonds yields. The dependent variables is the gold excess returns such as crises or crashes RGOLD_Tension. Mean excess value-weighted and equal-weighted portfolios, VWR-TBL EWR-TBL excluding dividends and the mean excess value weighted portfolios including dividends VWRD-TBL EWRD-TBL; and S&P 500 mean excess rerun are all proxy to the stock returns during tension times. AAA-TBL and BAA-TBL yields are the bonds returns during tension times. All rates are represented in annualized percentage points.. The first number in the third column refers to the t-statistic of the test, and the second number refers to p value under the null hypothesis that the gold cannot be used as a safe haven against drop in the stock and bond returns .
3.2. CAPM and Fama French results:
Table (7) The relationship between gold returns and the sock and bonds returns during
extreme market conditions for the whole sample period from 1986:04:1-2012:10:31 using CAPM and Fama French models (Safe haven hypothesis)
Table (7) documents the relation between the gold returns with the other two markets during extreme market conditions using four models. CAPM model, CAPM model including excess bonds returns in the right-hand side, Fama French three factor models, and Fama French Model including bonds excess returns in the right hand side. The dependent variables is the gold excess return RGOLD_Tension.. The first row of each model documents mean excess value-weighted and equal-weighted portfolios, VWR-TBL which is the same as (MKTRF) mean excess value weighted portfolios excluding; and the second row of each model represents S&P 500 mean excess rerun are all proxy to the stock returns during tension times. AAA-TBL and BAA-TBL yields are the bonds returns during tension times. All rates are represented in annualized percentage points.. t-statistic are in ().
1986:04:01-2012:10:31 RGold_Tension tfR ,− t-statistic
VWR-TBL -0.063*** -3.27 0.00
VWRD-TBL -0.0627*** -3.27 0.00
EWR-TBL -0.058** -2.41 0.02
EWRD-TBL -0.059** -2.43 0.02
SP500-TBL -0.077*** -4.16 0.00
AAA-TBL -0.00044 -1.30 0.2
BAA-TBL -0.000433 -1.10 0.26
1986:04:01-2012:10:31(1440bs.) Series MKTRF
1-VWR-TBL 2-S&P-TBL
SMB HML BONDS AAA BAA
CAPM -0.062***
19
4- Robustness Tests:
4.1.Subsamples
As a check for robustness, I will analyze the relation between the change in the gold
prices with the stock and bond returns in each crisis separately to make sure if the gold can be
used as a safe haven against stocks and corporate bonds returns. I will analyze the relation
between gold prices with the other risky assets in the 20 days after the crash or crisis start.
Moreover, I will analyze the nature of this relation over the entire Asian crisis, and subprime
crisis period. I included here the most important crashes and crises that experienced dramatic
decrease in the stock and bond returns. For the black Monday crash and September eleven crash,
I tested the relation in the twenty days after the start of crashes. I tested the model for Asian and
subprime crises using 20 days after the crisis start and on overall crises period. The flowing four
tables show all the coefficients of stock returns and corporate bonds yields in each specific
subsample.
Table (8) The contemporaneous relation between gold, stock and bonds
(-3.22) -0.08*** (-4.14)
CAPM+Bonds -0.063*** (3.27)
-0.077*** (04.16)
-0.00044 (-1.3)
-0.00043 (-1.1)
FF -0.077*** (-3.43)
-0.08606*** (-3.88)
0.098** (2.58)
0.0708* (1.75)
-0.09** (-2.27)
-0.0937** (-2.41)
FF+Bonds -0.0774*** (-3.44)
-0.086*** (-3.89)
0.098 (2.57) 0.07* (1.73)
-0.09** (-2.38)
-0.089** (-2.25)
-0.0004 (-1.19) -0.0004 (-1.01)
20
in the Black Monday crash Table (8) reports the relation between gold returns with stocks returns and bonds yields during the black Monday crash. The dependent variables is the gold excess return tftgold RR ,, − mean excess value-weighted and equal-weighted portfolios, VWR-TBL EWR-TBL excluding dividends and the mean excess value weighted portfolios including dividends VWRD-TBL EWRD-TBL; and S&P 500 mean excess rerun are all proxy to the stock returns. AAA-TBL and BAA-TBL yields are the bonds returns. All rates are represented in annualized percentage points.. The first number in the third column refers to the t-statistic of the test, and the second number refers to p value under the null hypothesis that the gold cannot be used as a safe haven against drop in the stock and bond returns .The displays the number of observations and R2.
Table (8) reports the relation of the gold with the stock and bonds returns to test whether
the gold can be used as a safe haven in the extreme market condition like Black Monday crash .
The coefficient of the value weighted return in the 20 trading days after the beginning of the
crash is -0.150 and this estimate is statistically and economically significant. There is no
significant relation between the equal weighted return and the gold returns. The coefficient of
S&p500 is very close to the VWR (-0.1512) and there is negative relation between the corporate
bond yields and the gold returns but this relation is insignificant. Table (9) shows that gold
cannot be used as a safe haven against stocks and bonds for Asian crisis.
Table (9) The contemporaneous relation between gold, stock and bonds
In the Asian crisis Table (9) reports the relation between gold returns with stocks returns and bonds yields during the Asian crisis. The dependent variables is the gold excess return tftgold RR ,, − mean excess value-weighted and equal-weighted portfolios, VWR-TBL EWR-TBL excluding dividends and the mean excess value weighted portfolios including
1986:04:01-1986:5:01 (20 Obs.) R2=42% tftgold RR ,, − t-statistic
VWR-TBL -0.150*** -2.67 0.02
VWRD-TBL -0.147*** -2.57 (0.02)
EWR-TBL -0.042 -0.48 (0.64)
EWRD-TBL -0.146 -2.56 (0.02)
SP500-TBL -0.1512*** -3.47 (0.00)
AAA-TBL -0.003 -0.34 (0.74)
BAA-TBL -0.0047 -0.49 (0.63)
21
dividends VWRD-TBL EWRD-TBL; and S&P 500 mean excess rerun are all proxy to the stock returns. AAA-TBL and BAA-TBL yields are the bonds returns. All rates are represented in annualized percentage points.. The first number in the third column refers to the t-statistic of the test, and the second number refers to p value under the null hypothesis that the gold cannot be used as a safe haven against drop in the stock and bond returns .The displays the number of observations and R2.
Table (10) The relation contemporaneous between gold, stock and bonds in the September 11
Crash Table (10) reports the relation between gold returns with stocks returns and bonds yields during September 11 crash. The dependent variables is the gold excess return tftgold RR ,, − mean excess value-weighted and equal-weighted portfolios, VWR-TBL EWR-TBL excluding dividends and the mean excess value weighted portfolios including dividends VWRD-TBL EWRD-TBL; and S&P 500 mean excess rerun are all proxy to the stock returns. AAA-TBL and BAA-TBL yields are the bonds returns. All rates are represented in annualized percentage points.. The first number in the third column refers to the t-statistic of the test, and the second number refers to p value under the null hypothesis that the gold cannot be used as a hedge against drop in the stock and bond returns .The displays the number of observations and R2.
1997:07:02-to1997:08:02 (20 obs.) 20days trading after the starting of
Asian crisis R2=0
1997:01-1998:09(405obs.) The Asian crisis period
R2=0
tftgold RR ,, − t statistic tftgold RR ,, −
t-statistic
VWR-TBL 0.17 0.48 (0.63)
0.055 1.62 (0.11)
VWRD-TBL 0.17 0.48 (0.63)
0.045 1.6 (0.11)
EWR-TBL 0.82 0.48 (0.64)
0.115 2.49 (0.013)
EWRD-TBL 0.81 0.89 (0.39)
0.113 2.33 (0.015)
SP500-TBL 0.17 0.58 (0.57)
0.047 1.49 (0.135)
AAA-TBL -0.02 -0.78 (0.44)
-0.001 -0.96 (0.33)
BAA-TBL -0.02 -0.72 (0.48)
0.002 -1.04 (0.301)
22
Table (10) documents the estimated coefficient from regressing the gold returns on the
stocks returns and corporate bonds yields for twenty days after the starting of the September
eleven crash from. The estimated coefficients indicate that there is a highly significant negative
relation between stocks returns and gold returns in this crash indicating that the gold can be used
as a safe haven in this critical period. However the bonds returns coefficient implies no relation
with the gold returns. Moreover, Table (11) reports the relation between the gold prices change
and the changes in the stock and bond prices in 20 days after the crisis start, during the peak of
the crisis and during the overall subprime crisis period. There is no significant relation between
the gold and stocks in the early stage of the subprime crisis except between S&P500 excess
returns and the gold excess returns. However there is a significant relation between gold prices
change and the stock returns over the peak period of the crisis. The results remain robust in
respective of the ability of the gold to play a role of safe haven against corporate bonds in the
extreme market periods.
Table (11)
The contemporaneous relation between gold, stock and bonds in the subprime crisis Table (11) reports the relation between gold returns with stocks returns and bonds yields during the 20days period after the starting of the subprime crisis, the relation between three markets from the starting of the crisis to the peak of the financial crisis, and for the entire crisis period . The dependent variables is the gold excess return
2001:09:11-2001:09:30 (20 Obs.) R2=50.9
tftgold RR ,, − t-statistic (P value)
VWR-TBL -0.73*** -2.80 (0.02)
VWRD-TBL -0.73*** -2.75 (0.03)
EWR-TBL -0.62** -1.91 (0.09)
EWRD-TBL -0.63** -1.89 (0.09)
SP500-TBL -0.73*** -2.82 (0.02)
AAA-TBL -0.05 -0.99 (0.34)
BAA-TBL -0.05 -1.03 (0.33)
23
tftgold RR ,, − mean excess value-weighted and equal-weighted portfolios, VWR-TBL, EWR-TBL excluding dividends and the mean excess value weighted portfolios including dividends VWRD-TBL EWRD-TBL; and S&P 500 mean excess rerun are all proxy to the stock returns. AAA-TBL and BAA-TBL yields are the bonds returns. All rates are represented in annualized percentage points.. The first number in the third column refers to the t- statistic of the test, and the second number refers to p value under the null hypothesis that the gold cannot be used as a safe haven against drop in the stock and bond returns .The displays the number of observations and R2.
2007:07:01-2007:08:01(20 Obs.) 20days trading after the start of
Subprime crisis R2=11.5%
2007:7:18-2008:10:10(304 Obs.) during the peak Subprime
crisis R2=1.5%
2007:7:18-2009:04:02(529 Obs.) The entire period of the Subprime
crisis R2=2%
tftgold RR ,, − t-statistic (p value) tftgold RR ,, − t-statistic
(p value) tftgold RR ,, − t-statistic (p value)
VWR-TBL
0.194 1.46 (0.18)
-0.09* -1.60 (0.17)
0.003 0.11 (0.91)
VWRD-TBL
0.193 1.45 (0.18)
-0.10* -1.66* (0.17)
0.003 0.11 (0.90)
EWR-TBL
0.26 1.54 (0.16)
-0.08 -1.65* (0.134)
0.009 0.26 (0.79)
EWRD-TBL
0.26 1.55 (0.16)
-0.09 -1.63 (0.161)
0.009 0.26 (0.79)
SP500-TBL
0.17 -1.93* (0.05)
-0.10** -1.93* (0.05)
-0.016 -0.47 (0.63)
AAA-TBL 0.014 -0.8 (0.28)
-0.001 -0.19 (0.84)
-0.002 -0.20 (0.90)
BAA-TBL -0.012 -0.7 (0.30)
-0.002 -0.95 (0.34)
-0.003 -0.85 (0.28)
3.2.CAPM and Fama French Results ( Subsamples)
This section displays the results of the safe haven hypothesis using four different models
during four different stock market crashes. The gold can be used as a safe haven against stocks
even after controlling for FF three risk factors. Additionally, gold can be used as a safe haven
against bonds risk because there is zero correlation between two markets.
Table (12) CAPM and Fama French Results for Black Monday Crash
Black Monday (Market crash) (20bs.) Series MKTRF SMB HML BONDS
24
Table (13)
CAPM and Fama French Results: Asian Crisis
Table (14) CAPM and Fama French Results: September 11, 2001 Attack
1-VWR-TBL 2-S&P-TBL
AAA BAA
CAPM -0.140*** (-2.76) -0.146 (-3.67)
CAPM+Bonds -0.150*** (-2.67) -0.151 (-3.47)
-0.003 (-0.34) -0.005) (-0.49)
FF -0.155** (-2.04) -0.152 (-2.18)
0.17* (1.75) 0.103 (0.91)
-0.36 (-1.29) -0.36
(-1.34)
FF+Bonds -0.141* (-1.65) -0.142 (-1.69)
0.18* (1.69) 0.19 (0.9)
-0.34 (-1.1) -.34
(1.17)
0.002 (0.32) 0.003 (0.28)
Asian crisis months (405)obs.) Series MKTRF
1-VWR-TBL 2-S&P-TBL
SMB HML BONDS AAA BAA
CAPM 0.056 (1.62) 0.047 (1.49)
CAPM+Bonds FF 0.148**
(2.34) 0.152** (2.37)
0.09 (1.3) 0.140 (1.66)
0.190 (1.59) 0.204 (1.65)
FF+Bonds 0.164** (2.57)
0.170*8 (2.61)
0.117 (1.56) 0.166* (1.73)
0.223 (1.83) 0.126 (1.9)
-0.001 (-1.4) -.001
(-1.45)
25
Table (15)
CAPM and Fama French Results: Subprime Crisis
September 11 crash (20bs.) Series MKTRF
1-VWR-TBL 2-S&P-TBL
SMB HML BONDS AAA BAA
CAPM -0.70*** (-2.76)
-0.68*** (-2.68)
CAPM+Bonds -0.73*** (-2.8)
-0.73*** (-2.82)
-0.05 (-0.99) -0.05
(-1.03) FF -0.764***
(-4.46) -0.746***
(-4.24)
1.15 (1.81) 1.001 (1.56)
0.106 (0.15) 0.073 (0.10)
FF+Bonds -0.765*** (-4.13)
-0.764*** (-3.91)
1.15 (1.74) 1.93
(2.69)
0.110 (0.14) 0.07
(0.09)
-0.0003 (-0.01) -0.0001 (-0.00)
Panel (A): Subprime crisis first 20 trading days (20bs.) Series MKTRF
1-VWR-TBL 2-S&P-TBL
SMB HML BONDS AAA BAA
CAPM -0.194 (1.46) 0.173 (1.39)
CAPM+Bonds 0.194 (1.45) 0.17
(1.37)
-0.014 (0.69) -0.012 (-0.33)
FF 0.183 (1.24) 0.16
(1.12)
-0.172 (-0.54) -0.15
(-0.45)
-0.006 (-0.01) -0.002 (-0.01)
FF+Bonds 0.160 (1.07) 0.135 (0.94)
-0.29 (-0.85) -0.27 -0.77)
-0.13 (-0.26) -0.128 (-0.26)
0.02 (0.95) -0.01
(-0.16)
Panel (B): Subprime crisis from July 2007 to 0ct 2008 (304bs.)
26
4.2 The dynamic relation between gold, stocks, and bonds.
4.2.1 VAR Results:
It is important to examine the lagged impact of stock returns and corporate bond yields
on the gold returns. I will present the results of using VAR model that takes into consideration
the lagged impact of the gold lagged returns as well as the stock return and bond lagged returns,
Series MKTRF 1-VWR-TBL 2-S&P-TBL
SMB HML BONDS AAA BAA
CAPM -0.075 (-1.38) -0.10* (-1.93)
CAPM+Bonds -0.09* (-1.65) -0.10* (-1.93)
-0.001 (-0.19) -0.95
(-0.34) FF -0.063
(-1.02) -0.10
(-1.58)
0.210 (1.53) 0.19
(1.38)
-0.001 (-0.01)
0.03 (0.29)
-0.001 (-0.25) -0.001 (-0.25)
FF+Bonds -0.063 (-1.03) -0.097 (-1.6)
0.21 (1.53) 0.19
(1.38)
-001 (-0.01) 0.013 (0.12)
-0.002 (-0.25) 0.002
(-1.07))
Panel (C): Subprime crisis whole period from July 2007 to April 2009 (528bs.) Series MKTRF
1-VWR-TBL 2-S&P-TBL
SMB HML BONDS AAA BAA
CAPM 0.00318 (0.09) -0.016 (-0.47)
CAPM+Bonds 0.003 (0.09) -0.016 (-0.48)
-0.005*** (-2.01) -0.001 (-1.00)
FF 0.044 (1.07)
0.01 (0.09)
-0.15* (-1.69)
FF+Bonds 0.04 (1.00)
0.09 (0.98)
-0.14** (-1.98)
-0.001 (-1.08)
27
because in most cases the negative stock returns are followed by positive stocks. This change in
the direction of stocks returns changes the relation between stocks and gold completely. In most
cases, the negative stock returns at time t motivate investors to investors to purchase gold at time
t+1, then the gold prices may be affected and the returns on gold may drop at time t+1. The merit
of using VAR is that it considers all the variables in the model as endogenous variables so that
we can know the effect of all variables in the lagged form on the dependent variable at time t.
The following tables show that there is no dynamic relation between gold, stocks and bonds. As I
mensioned before, If the gold is zero or negatively correlated with stocks and bonds during
extreme market periods, then it will be considered as a safe haven against these two markets.
Table (16) presents the VAR estimates of the dynamic relation between gold, stocks, and
bonds for the entire sample period during all calm and tension states of economy. VAR accounts
of the lagged impact of the stocks returns and corporate bonds yields on the returns on gold.
I include the lagged returns up to 3 periods to account for the time series effect and know how
the stocks and bonds returns in t-1, t-2, t-3 affect the returns of gold today. The relation between
three markets is totally different from the contemporaneous relation between the three markets.
The gold returns on time t is negatively affected by the gold returns. The significant negative
relation between stocks and gold’s returns is no longer exists. However, there is a significant
negative relation between the corporate bond yields in t-1 and the gold returns at time t.
Tables (16) panel (b) reports the VAR estimates of the dynamic relation between gold
returns, corporate bond yields, and equity return during two market crashes (Black Monday, and
September 11), and two global financial crises (Asian crisis and subprime crisis), respectively.
Table (17) documents the VAR estimates of the three markets during market crash 1987
and September 11 crash in 2001. During the Market crash, the gold prices is negatively affected
28
by the stock returns on t-1 and t-3 however, there is a positive relation between gold returns at
time t and stocks returns at time t-2. This result implies that gold was considered a safe haven
against risky change in corporate bond yields during September 2001 crash.
Table (16) VAR Estimates of the relationship between gold, stocks and bonds returns (Entire
Sample Period) The table (16) presents the VAR parameter estimates of testing the dynamic relationship between gold
excess return tftgold RR ,, − , tftStock RR ,, − the stock returns using SP500. ftbond RR −, yields as estimated by
equations (1), (2), and (3) during the overall and tension periods (1986-2012). t-statistics are in [ ]. All variables are treaded as endogenous state variables All rates are represented in annualized percentage points.. The first number in the third column refers to the t-statistic of the test, and the second number refers to p value under the null hypothesis that the gold can not be used as a hedge against drop in the stock and bond returns.
Panel (a) Hedging: Whole sample Panel (b) Safe haven: tension periods
tftgold RR ,, −
tftStock RR ,, −
ftbond RR −,
tf
tTensiongold
RR
,
,_
−
tf
TensiontStock
RR
,
_
−
tfktTensionbond RR ,,_ −−
tftgold RR ,, − (-1) -0.02145* [-1.72595]
0.004369 [ 0.30016]
0.095803 [ 1.56331] tftTensiongold RR ,,_ −
(-1) -0.049864* [ -1.88178]
-0.04741 [-1.36582]
0.081546 [ 0.38296]
tftgold RR ,, − (-2) 0.002437 [ 0.19610]
0.017371 [ 1.19344]
0.253926*** [ 4.14328] tftTensiongold RR ,,_ −
(-2) 0.036676 [ 1.38070]
0.02456 [ 0.70576]
0.069584 [ 0.32598]
tftgold RR ,, − (-3) 0.00715 [ 0.57519]
-0.00343 [-0.23552]
-0.05689 [-0.92835] tftTensiongold RR ,,_ −
(-3) 0.01819
[ 0.68423] 0.005277 [ 0.15151]
-0.00899 [-0.04208]
tftStock RR ,, − (-1) 0.01538 [ 1.44501]
-0.04253*** [-3.41256]
-0.21338*** [-4.06604]
tfjtTensionStock RR ,,_ −−
(-1) -0.00993
[-0.49130] -0.03052
[-1.15219] -0.25554
[-1.57281]
tftStock RR ,, − (-2) 0.012796 [ 1.20119]
-0.03936 [-3.15520]
-0.17447*** [-3.32168]
tfjtTensionStock RR ,,_ −−
(-2) 0.011218 [ 0.58957]
-0.07536*** [-3.02311]
-0.47649*** [-3.11638]
tftStock RR ,, − (-3) -0.00249 [-0.23425]
-0.00683 [-0.54962]
-0.04185 [-0.79950]
tfjtTensionStock RR ,,_ −−
(-3) -0.00942
[-0.49521] -0.03838
[-1.53946] -0.09256
[-0.60529]
ftbond RR −, (-1) -0.00445* [-1.76021]
-0.00738** [-2.49182]
1.006033*** [ 80.7121] tfktTensionbond RR ,,_ −− (-1)
-0.00029 [-0.08893]
-0.01121*** [-2.60064]
0.99108*** [ 37.4927]
ftbond RR −, (-2) 0.002632 [ 0.73325]
0.005579 [ 1.32753]
-0.01776 [-1.00377] tfktTensionbond RR ,,_ −− (-2)
-0.00243 [-0.52782]
0.010225* [ 1.69438]
0.018094 [ 0.48881]
ftbond RR −, (-3) 0.001577 [ 0.62425]
0.001767 [ 0.59743]
0.011583 [ 0.93024] tfktTensionbond RR ,,_ −− (-3)
0.002305 [ 0.70918]
0.001435 [ 0.33692]
-0.02276 [-0.87129]
29
Table (17): VAR Estimates - The Black Monday crash and September 11 crash
The table (5) presents the VAR parameter estimates of testing the dynamic relationship between gold excess return
tftgold RR ,, − and the stock returns ( tftStock RR ,, − and ftbond RR −, yields as estimated by equations (1), (2), and (3) during the crashes periods. t-statistics are in [ ]. All variables are treaded as endogenous state variables All rates are represented in annualized percentage points.. The first number in the third column refers to the t-statistic of the test, and the second number refers to p value under the null hypothesis that the gold cannot be used as a hedge against drop in the stock and bond returns.
Table (18) documents the VAR estimates of the three markets during Asian crisis and
recent global financial crisis. During the Asian crisis, the gold prices are negatively affected by
the stock returns and bonds returns on t-1, t-2 and t-3. Overall, there is no significant relation
between gold and stock and bonds returns during crisis, and consequently the gold can be
considered as a safe haven even after taking into consideration the dynamic effect between the
three markets and taking the lagged returns effect into consideration.
Market crash September 11 crash
tftgold RR ,, − tftStock RR ,, − ftbond RR −,
tftgold RR ,, − tftStock RR ,, −
ftbond RR −,
tftgold RR ,, − (-1) -0.56050* [-1.65265]
0.782132 [ 0.75630]
-0.40901 [-0.16115] tftgold RR ,, − (-1) -0.61198***
[-2.22290] 0.246491 [ 0.26600]
-0.40485 [-0.22411]
tftgold RR ,, − (-2) 0.299549 [ 0.89060]
0.0946 [ 0.09238]
1.327673 [ 0.52829] tftgold RR ,, − (-2) 0.088735
[ 0.52113] 0.064334 [ 0.11225]
-0.71861 [-0.64318]
tftgold RR ,, − (-3) -0.0251 [-0.08772]
0.9066 [ 1.04077]
-0.99369 [-0.46482] tftgold RR ,, − (-3) 0.224813*
[ 1.67116] -0.50771
[-1.12124] 0.581162 [ 0.65839]
tftStock RR ,, − (-1) -0.02913 [-0.34511]
0.029154 [ 0.11345]
0.045259 [ 0.07177] tftStock RR ,, − (-1) -0.21926
[-1.54438] -0.10937
[-0.22887] 0.211626 [ 0.22717]
tftStock RR ,, − (-2) 0.007059 [ 0.11815]
0.013251 [ 0.07285]
-0.09991 [-0.22380] tftStock RR ,, − (-2) 0.110339
[ 0.90481] -0.18414
[-0.44862] -1.70318** [-2.12852]
tftStock RR ,, − (-3) -0.0523 [-1.05583]
-0.15757 [-1.04492]
-0.25517 [-0.68949] tftStock RR ,, − (-3) 0.26257
[ 1.43222] -0.39819
[-0.64527] -0.19638
[-0.16324]
ftbond RR −, (-1) -0.01514 [-0.26560]
-0.08953 [-0.51598]
0.706857 [ 1.65990] ftbond RR −, (-1) 0.109051*
[ 1.69984] -0.25493
[-1.18054] 0.872131** [ 2.07180]
ftbond RR −, (-2) -0.06943 [-1.33731]
0.434136*** [ 2.74658]
0.056314 [ 0.14517] ftbond RR −, (-2) -0.18129***
[-2.45382] 0.20804
[ 0.83656] 0.010872 [ 0.02243]
ftbond RR −, (-3) 0.076238*** [ 2.67925]
-0.34316*** [-3.96102]
0.025139 [ 0.11824] ftbond RR −, (-3) -0.201***
[ 3.38039] -0.1921
[-0.02375] 0.019
[-0.36882]
30
Table (18) VAR Estimates – the Asian Crisis and the Subprime Crisis
The table (5) presents the VAR parameter estimates of testing the dynamic relationship between gold excess return
tftgold RR ,, − and the stock returns tftStock RR ,, − and bond yields ftbond RR −, as estimated by equations (1), (2), and (3) during the crisis periods. t-statistics are in [ ]. All variables are treaded as endogenous state variables All rates are represented in annualized percentage points.. The first number in the third column refers to the t-statistic of the test, and the second number refers to p value under the null hypothesis that the gold cannot be used as a hedge against drop in the stock and bond returns.
4.2.2 IRF Results:
Using Impulse response Function technique (IRF), figure (3) shows the response of gold
returns to shocks in the stock returns and corporate bonds yields during October 1987 crash as
well as the September 11, 2001 attack. The figure shows that gold prices at time t commove
against the bonds and stocks returns shocks on the day t-1. However, gold prices at time t
commoves with the bonds and stock prices at time t-2 and t-3 and then for the rest of the trading
Asian Crisis Subprime crisis tftgold RR ,, − tftStock RR ,, −
ftbond RR −, tftgold RR ,, − tftStock RR ,, −
ftbond RR −,
tftgold RR ,, − (-1) -0.72572*** [-2.60719]
-0.27909 [-1.38309]
0.465419 [ 0.42818] tftgold RR ,, − (-1) 0.003391
[ 0.01013] -0.10722
[-0.21735] 1.140999 [ 0.90048]
tftgold RR ,, − (-2) -0.34287 [-1.12238]
0.490459** [ 2.21473]
-0.7963 [-0.66752] tftgold RR ,, − (-2) -0.36065
[-1.13930] 0.559027 [ 1.19831]
1.744518 [ 1.45586]
tftgold RR ,, − (-3) 0.205145 [ 0.60956]
-0.08416 [-0.34496]
0.781419 [ 0.59459]*** tftgold RR ,, − (-3) 0.569437*
[ 1.60197] 0.184668 [ 0.35252]
0.456178 [ 0.33902]
tftStock RR ,, − (-1) -0.92496** [-1.71022]
0.383291 [ 0.97761]
-2.89824 [-1.37228]** tftStock RR ,, − (-1) 0.022728
[ 0.06555] -0.93467** [-1.82919]
-1.47157 [-1.12121]
tftStock RR ,, − (-2) -0.25674 [-0.68374]
-0.57123** [-2.09850]
2.362404 [ 1.61112] tftStock RR ,, − (-2) -0.09614
[-0.27987] -0.79291
[-1.56625] -1.79291
[-1.37881]
tftStock RR ,, − (-3) -0.57455 [-1.38730]
-0.1115 [-0.37139]
0.046798 [ 0.02894] tftStock RR ,, − (-3) -0.06123
[-0.26745] -0.22186
[-0.65755] -1.01045
[-1.16591]
ftbond RR −, (-1) 0.045765 [ 0.48004]
0.082825 [ 1.19843]
0.812182** [ 2.18161] ftbond RR −, (-1) -0.07289
[-0.59271] 0.087591 [ 0.48330]
0.908285** [ 1.95113]
ftbond RR −, (-2) -0.02586 [-0.18569]
-0.12275 [-1.21570]
0.230201 [ 0.42325] ftbond RR −, (-2) -0.05186
[-0.41233] 0.012473 [ 0.06729]
0.233786 [ 0.49101]
ftbond RR −, (-3) -0.1007 [-0.93279]
0.043458 [ 0.55533]
-0.08808 [-0.20895] ftbond RR −, (-3) 0.037615
[ 0.35311] -0.1357
[-0.86438] -0.19981
[-0.49551]
31
month (20 days) after the crash the chocks in the stocks and bonds do not affect the gold returns.
During September 11 crash, the response of gold is relatively higher to chocks in bonds holding
stocks returns constant. The results show that gold move against chocks in bonds returns. This
figure along with the table (17) proves that there is a significant negative relation between gold
returns and corporate bonds yields chocks during the September 11 crash.
Figure (4) shows the results of the impulse response function during the 20 days period
after the start of the Asian crisis and the entire crisis period. It is obvious that there is negative or
zero relation between the gold and the stock market but there is a positive relation between gold
prices and the stock prices over the Asian crisis period. Figure (5) presents the IRF results during
the subprime crisis, and the figure shows that there is a negative but insignificant relation
between gold prices at time t and all the lagged returns of the stock and bonds returns except the
stock returns at t-1 and the bonds returns at t-3.
32
Figure (3)
Impulse Response Function for the Market Crashes Response Functions to gold, stock, and corporate bonds chocks. Calculations based on the VAR of table (11). Each chock changes the other variable of interest holding the others constant. GOLDEXR refers to the gold excess returns. EXSP refers to the excess returns on S&P used as a proxy of market index and EXxaa corporate bonds represent the excess returns on bonds. Panel (a) refers to IRF during the 20 days after the start of the Black Monday and panel (b) displays the IRF during 20 days after the starting of September 11 crash.
Panel (a): Market Crash 1987 and Panel (b): September 11
-.004
-.002
.000
.002
.004
.006
.008
.010
2 4 6 8 10 12 14 16 18 20
GOLDEXR EXSP EXAAA
Response of GOLDEXR to CholeskyOne S.D. Innovations
-.010
-.005
.000
.005
.010
.015
.020
.025
2 4 6 8 10 12 14 16 18 20
GOLDEXR EXSP EXAAA
Response of EXSP to CholeskyOne S.D. Innovations
.00
.01
.02
.03
.04
.05
2 4 6 8 10 12 14 16 18 20
GOLDEXR EXSP EXAAA
Response of EXAAA to CholeskyOne S.D. Innovations
-.006
-.004
-.002
.000
.002
.004
.006
.008
2 4 6 8 10 12 14 16 18 20
GOLDEXR EXSP EXAAA
Response of GOLDEXR to CholeskyOne S.D. Innovations
-.015
-.010
-.005
.000
.005
.010
.015
2 4 6 8 10 12 14 16 18 20
GOLDEXR EXSP EXAAA
Response of EXSP to CholeskyOne S.D. Innovations
-.04
-.03
-.02
-.01
.00
.01
.02
.03
.04
2 4 6 8 10 12 14 16 18 20
GOLDEXR EXSP EXAAA
Response of EXAAA to CholeskyOne S.D. Innovations
33
Figure (4) Impulse Response Function for the Asian Crisis
Response Functions to gold, stock, and corporate bonds chocks. Calculations based on the VAR of table (12). Each chock changes the other variable of interest holding the others constant. GOLDEXR refers to the gold excess returns. EXSP refers to the excess returns on S&P used as a proxy of market index and EXxaa corporate bonds represent the excess returns on bonds. Panel (a) refers to the IRF during twenty days after the start of the crisis. Panel (b) refers to the IRF for the entire crisis period
Panel (a) The beginning of Asian crisis: Panel (b) Asian Crisis peak:
-.006
-.004
-.002
.000
.002
.004
.006
.008
.010
2 4 6 8 10 12 14 16 18 20
GOLDEXR EXSP EXAAA
Response of GOLDEXR to CholeskyOne S.D. Innovations
-.006
-.004
-.002
.000
.002
.004
.006
.008
2 4 6 8 10 12 14 16 18 20
GOLDEXR EXSP EXAAA
Response of EXSP to CholeskyOne S.D. Innovations
-.01
.00
.01
.02
.03
.04
2 4 6 8 10 12 14 16 18 20
GOLDEXR EXSP EXAAA
Response of EXAAA to CholeskyOne S.D. Innovations
-.02
-.01
.00
.01
.02
.03
2 4 6 8 10 12 14 16 18 20
GOLDEXR EXSP EXAAA
Response of GOLDEXR to CholeskyOne S.D. Innovations
-.04
-.03
-.02
-.01
.00
.01
.02
.03
.04
2 4 6 8 10 12 14 16 18 20
GOLDEXR EXSP EXAAA
Response of EXSP to CholeskyOne S.D. Innovations
-.06
-.04
-.02
.00
.02
.04
.06
.08
.10
2 4 6 8 10 12 14 16 18 20
GOLDEXR EXSP EXAAA
Response of EXAAA to CholeskyOne S.D. Innovations
34
Figure (5) Impulse Response Function for the Subprime Crisis
Response Functions to gold, stock, and corporate bonds chocks. Calculations based on the VAR of table (12). Each chock changes the other variable of interest holding the others constant. GOLDEXR refers to the gold excess returns. EXSP refers to the excess returns on S&P used as a proxy of market index and EXAAA corporate bonds represent the excess returns on bonds. Panel (a) refers to the IRF during twenty days after the start of the crisis. Panel (b) refers to the IRF for the whole crisis period.
Subprime start Panel (a) Subprime peak Panel (b)
-.004
-.002
.000
.002
.004
.006
.008
.010
.012
2 4 6 8 10 12 14 16 18 20
GOLDEXR EXSP EXAAA
Response of GOLDEXR to CholeskyOne S.D. Innovations
-.015
-.010
-.005
.000
.005
.010
.015
.020
2 4 6 8 10 12 14 16 18 20
GOLDEXR EXSP EXAAA
Response of EXSP to CholeskyOne S.D. Innovations
.01
.02
.03
.04
.05
.06
2 4 6 8 10 12 14 16 18 20
GOLDEXR EXSP EXAAA
Response of EXAAA to CholeskyOne S.D. Innovations
-.02
-.01
.00
.01
.02
.03
2 4 6 8 10 12 14 16 18 20
GOLDEXR EXSP EXAAA
Response of GOLDEXR to CholeskyOne S.D. Innovations
-.04
-.03
-.02
-.01
.00
.01
.02
.03
.04
2 4 6 8 10 12 14 16 18 20
GOLDEXR EXSP EXAAA
Response of EXSP to CholeskyOne S.D. Innovations
-.06
-.04
-.02
.00
.02
.04
.06
.08
.10
2 4 6 8 10 12 14 16 18 20
GOLDEXR EXSP EXAAA
Response of EXAAA to CholeskyOne S.D. Innovations
35
VI. Conclusions
This paper examines the hedging and safe haven properties of stocks using daily data
over the period 1986 to 2012 using many different market indexes for stocks and corporate
bonds. I find that gold can be used as a hedge and safe haven during the calm and crashes
periods. During Asian crisis, gold cannot be used a safe haven against stock after controlling for
Fama French three factor models but can be used as a safe haven against bonds. The dynamic
relation between three markets take different style using VAR and IRF that shows in most cases
that gold has zero relation with the lagged stock returns and lagged corporate bonds yields, and
consequently can be a very effective safe haven against these markets fluctuations. The
durability for safe haven and hedging ability of stocks during recent years need further research
especially after price Jumping of gold in 2010. I think gold may lose its advantages if the trend
of the gold prices remains upward. If all investors flight from stocks and bonds to gold and
realized its advantages, the price of gold turned to be higher than stock and bonds prices and
consequently may lose its hedging and safe haven characteristics. This expectation depends on
the investor behaviors and this issue is left for future research.
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