Discussion Paper Series
Graduate School of Economics and School of Economics
Meisei University
Discussion Paper Series, No.44
December, 2022
Time-varying safe haven status of major currencies
Ayano Sato
（Aoyama Gakuin University）
Hayato Nakata（Meisei University）
Jay Percy(FD Technologies)

Hodokubo 2-1-1, Hino, Tokyo 191-8506
School of Economics, Meisei University
Phone: +81-(0)42-591-9479 Fax: +81-(0)42-599-3024
URL: https://keizai.meisei-u.ac.jp/econ/
E-mail: keizai@econ.meisei-u.ac.jp

Time-varying safe haven status of major currencies *Ayano Sato †, Hayato Nakata ‡, Jay Percy §Abstract
The main objective of this paper is to investigate the temporal variation of the safe
haven status of the Japanese yen, Swiss franc, British pound and euro relative to the
U.S. dollar. We define a safe haven currency as the currency investors prefer topurchase when exchange rate volatility is high, focusing on a time-varying degree ofrisk aversion for the four currencies. The temporal variation in investor risk aversion,
which is usually unobservable, is simulated explicitly using a rolling GARCH-in-mean
model to analyze the daily exchange rate from January 1, 2000 to November 30, 2018.
At the time of the global financial crisis, the Swiss franc, British pound and euro werelikely safe haven currencies, while the Japanese yen emerged as a safe havensometime later. Moreover, we suggest that investor risk aversion is affected by the
economic policies of each country, and find a strong relation among euro, British poundand Swiss franc investor risk attitudes. More specifically, the euro is often a substitutefor the British pound and a complement for the franc during risk-off markets.
Keywords: GARCH-in-mean model, risk aversion, safe-haven currencies
JEL Classification: F31, G15, G17*

This study was supported by Dr. Koichi Hamada of Yale University, Dr. Takatoshi Itoof Columbia University, and Dr. Yutaka Harada, a former board member of the Bank ofJapan. We are immensely grateful to them for their comments on an earlier version of
the manuscript. Economists Takashi Yoshimatsu, Hiroshi Miyazaki, Hiroki Shimatsu
and Mikihiro Matsuoka provided deep insights that assisted the research. We would liketo show our gratitude to them. We are also grateful for the insightful comments offeredby anonymous peer reviewers. Nakata acknowledges financial support from JSPS, witha Grant-in-Aid for Scientific Research(C) 20K01757.
†
Faculty of Law, Aoyama Gakuin University 4-4-25, Shibuya, Shibuya-ward Tokyo
150-8366, Japan. E-mail address: ayano.sato@aoyamagakuin.jp‡
School of Economics, Meisei University 2-1-1, Hodokubo, Hino-shi Tokyo 191-8506,
Japan. E-mail address: hnakata@econ.meisei-u.ac.jp§
Senior quant consultant, FD Technologies, 20F Shin-Marunouchi Center Building, 16-2 Marunouchi, Chiyoda-ku, Tokyo, 100-0005, Japan. E-mail address
jpercy@firstderivatives.com

1. Introduction
Investors tend to become risk averse when global financial risk increases and suddenly
begin to short risk assets in favor of assets considered to be relatively safe. When
financial markets experience such a risk-off episode, currency investors tend topurchase so-called safe haven currencies, designated hereinafter as SHCs. 1
The conventional view had been that the U.S. dollar is an SHC for investors
during periods of financial uncertainty or turmoil, but in recent years, a subject of
discussion has been that the Japanese yen and other currencies, rather than the U.S.
dollar are regarded as SHCs.2 These discussions became particularly pronounced
around the 2007–08 global financial crisis (GFC). For instance, the yen appreciatedabout 3% against the U.S. dollar during 10 days after BNP Paribas halted withdrawals

1

Habib and Stracca (2012) define a SHC as a currency that functions as a hedge for a reference

portfolio of risky assets conditional on movements in global risk aversion.
2

Kaul and Sapp (2006) assess fund flows into safe havens by examining intraday bid–ask spreads in

the U.S. dollar and euro intra-day spot and forward rate from December 1, 1999 to December 31,
2000.

1

in August 2007, and more than 20% during the three months following Lehman’s
bankruptcy in September 2008. It appreciated more than 3% in a single day, on May 6,
2010, because of severely heightened Greek default concerns preceding the Greek
government’s passage of austerity measures.
A central question is why was the yen purchased in risk-off episodes,
especially given that the Bank of Japan (BoJ) has flooded the market with yen since
the onset of “Abenomics” at the beginning of 2013? The Japanese yen was expected tolose value against other currencies in response to this monetary policy, and it did infact weaken. However, it has been confirmed frequently that the Japanese yen
appreciated sharply for short periods whenever market volatility peaked. If the yen isan SHC, then an SHC sometimes seems to be unrelated to economic fundamentals
such as domestic and overseas interest rate differentials and the amount of currency incirculation.3

3

Brunnermeier et al. (2008) show that dramatic exchange rate movements induced by

the sudden unwinding of currency carry trade positions coincide with increased global
risk or risk aversion, and suggest strongly that higher volatility leads investors to curb
their carry trade activities because of decreased funding liquidity. Other research on the
carry trade has been reported by Christiansen et al. (2011), McCauley and McGuire
2

Some recent studies have examined which currencies exhibit SHC properties inforeign exchange markets. Fatum and Yamamoto (2016) examine SHC behavior for six
developed country currencies during the GFC period, pre-crisis period, and post-crisis
period. They also assess which currencies are “safest” during the respective periods.
Their results show the Swiss franc as the safest currency during the pre-crisis period
and the Japanese yen during the GFC period as well as in the post-crisis period. In
other words, only the Japanese yen maintained its SHC status after the GFC.4
Masujima（2017）defines two Safe Asset Indices (long term and short term)and using these indices assesses the safe-haven status of several currencies and assets.
From a long-term perspective, only the Japanese yen and the Swiss franc maintained
their safe-haven status for the period 1997-2015. Short-term, however, using rolling
OLS estimation, Masujima finds that the safe-haven status of currencies like theJapanese yen and the Swiss franc is in fact time-varying.
To summarize the findings reported in the literature, first, the leading(2009), and Menkhoff et al. (2012).
4

Fatum and Yamamoto (2016) also test the nonlinearity of a coefficient derived from the VIX. Theyfind that many currencies exhibit nonlinear properties until the GFC period, but these properties
disappear or are resolved after the crisis.

3

candidates for SHC status are the Swiss franc and Japanese yen, followed by the
British pound and then the euro. Second, an SHC exhibits time-variant tendencies
depending on changes in market participants’ risk perception. The CBOE volatility
index (VIX) is often used in many studies as a risk proxy and has been used by
Masujima (2017), Habib and Stracca (2012), De Bock and de Carvalho Filho (2013),
and Fatum and Yamamoto (2016). 5This study was conducted to investigate several notable exchange rate seriesof the Japanese yen/ U.S. dollar (JPY), Swiss Franc/ U.S. dollar (CHF), British pound/
U.S. dollar (GBP) and euro/ U.S. dollar (EUR), to ascertain which can be empiricallycharacterized as SHCs. Also, this study was to identify periods during which theleading SHC candidates function as SHCs. For this study investors are assumed to besensitive to risk (or to have a kind of market sentiment), defined here as the degree ofrisk aversion. That sensitivity is time-variant, resulting in the periodic appreciation of
the SHC during times of risk.6 We seek to identify when the four safe-haven5

VIX is a common abbreviation for the volatility index published by the Chicago Board Options

Exchange (CBOE). It is a crowd-sourced estimate of the volatility of the U.S. S&P 500 Index. Habiband Stracca (2012) use the VIX as a proxy for global risk aversion.
6

Numerous researchers have examined the theoretical and empirical possibility that risk aversion is4

candidates become SHCs by estimating and plotting the degree of time-variant risk
aversion. The degree of risk aversion of investors in FX markets is typicallyunobservable, but we explicitly obtain an observable time-variant coefficient by
exploiting the rolling estimation properties of the GARCH-in-Mean (GARCH-M) model.
Numerous studies have used the GARCH family model to consider the timevariant risk premium in the FX market. For example, Domowitz and Hakkio (1985)
estimate the ARCH-M model for five currencies (British pound, French franc, Deutschemark, Japanese yen, Swiss franc) for 1973–1982. Jiang and Chiang (2000) investigate
whether the foreign exchange risk premium is related to volatility in the FX market
and volatility in equity markets for four currencies (British pound, Canadian dollar,
Deutsche mark, and Japanese yen) for 1973–1990.
As Engle et al. (1987) point out, the ARCH-M model generates a time-varying
risk premium based on economic theory. Following Engle et al. (1987), our study alsouses the GARCH-M model to obtain the degree of risk aversion theoretically, unlike thestudies investigating SHC described above. Although some of these studies emphasizethat the degree of risk aversion might be related to the risk off appreciation oftime-varying, including Campbell and Cochrane (1999), Brunnermeier and Nagel (2008), and Guiso
et al. (2018).

5

currencies, they rely on an estimated VIX coefficient to gauge risk sensitivity. 7 The
drawback of this approach is that the VIX reflects risks affecting the U.S. stock
market: although the U.S. stock market is sufficiently large to indirectly capture
uncertainty in financial markets worldwide, the VIX might be insufficient when
examining financial market turmoil only local to specific regions.8
The main contributions of this research paper are the following. First, weconclude that, among the four currencies we examine, the value of SHC varies
depending on the period, in line with Masujima (2017). For example, the euro was the
SHC in the early 2000s, but British pound became an SHC at around 2007. Later, theeuro and franc joined the pound as SHCs immediately before the September 2008 GFC.
Also, the Japanese yen briefly demonstrated safe haven properties in the period
immediately thereafter. Furthermore, whereas the euro and franc were still SHCs
when the Greek debt crisis emerged in October 2009, the Swiss franc appeared to lose
its safe haven status following the franc’s flash crash and the China shock in the first7

For example, work reported by Masujima (2017) and Fatum and Yamamoto (2016).

8

Hiraki and Fukunaga (2012) argue that fear over the bond market has remained unlinked to theexchange rate market since August 2011 using a Volatility index for both the bond and exchange rate
market.

6

half of 2015, as did the euro.
Second, we discover that foreign exchange intervention by the Japanese
government around 2004 altered the risk aversion of investors trading Japanese yen.
That helped JPY to follow the government’s intentions for policy. Similarly,
intervention by the Swiss National Bank starting in 2011 altered investors’ attitudes
about risk, but it altered the risk aversion of investors trading the euro or British
pound, not the Swiss franc. In addition to foreign exchange intervention, risk aversionof investors has apparently been affected by the economic policy implemented by eachcountry, including the United States.
Third, our empirical findings suggest a robust relationship among investor
risk attitudes towards the euro, British pound and Swiss franc. Investors likely viewthe euro as an alternative to the British pound and as a complement to the Swiss franc.
The remainder of the paper is organized as explained below. Section 2 presents
a description of the estimation methods applied to obtain the degree of risk aversion
and to analyze its dynamics referencing the JPY, GBP, CHF and EUR. Section 3
presents an evaluation, based on estimation results, of which currency is performing as
the SHC. Section 4 summarizes the conclusions of the paper.

7

2. Estimating the Degree of Risk Aversion
2.1 Methodology

We use foreign exchange rate data and employ the generalized autoregressive
conditionally heteroskedastic process in the GARCH-M model to investigate the timevarying risk aversion of investors. A simple GARCH (1,1)-M is expressed as
𝑟𝑟𝑟𝑟𝑡𝑡 = 𝜇𝜇 + 𝛾𝛾𝜎𝜎𝑡𝑡2 + 𝜖𝜖𝑡𝑡 , 𝜖𝜖𝑡𝑡 |𝛺𝛺𝑡𝑡−1 ~𝑁𝑁(0, 𝜎𝜎𝑡𝑡 )22
𝜎𝜎𝑡𝑡2 = 𝜔𝜔 + 𝛼𝛼𝜖𝜖𝑡𝑡−1+ 𝛽𝛽𝜎𝜎𝑡𝑡−1, 𝜔𝜔 > 0,𝛼𝛼 > 0,(1)𝛽𝛽 > 0(2)

where 𝑟𝑟𝑟𝑟𝑡𝑡 stands for the risk premium or excess return, ϵ𝑡𝑡 expresses a random error,2

σ denotes the conditional variance of ϵ𝑡𝑡 at time t, Ω𝑡𝑡

t−1

signifies the information attime t-1, and 𝜇𝜇, 𝛾𝛾 , 𝜔𝜔, 𝛼𝛼, and 𝛽𝛽 are parameters. 𝜔𝜔, 𝛼𝛼, and 𝛽𝛽 must be non-negative toensure positive volatility. Moreover, the measure of shock persistence 𝛼𝛼 + 𝛽𝛽 must be

less than unity for stability to hold (stability condition of volatility clustering). Also, 𝛾𝛾
in equation (1) is interpreted as investors’ sensitivity to risk or the coefficient ofrelative risk aversion. Engle et al. (1987) show that the sign and magnitude of 𝛾𝛾

depends on the utility function of investors in asset markets. If 𝛾𝛾 = 0, then investors
are indifferent to volatility (risk) 𝜎𝜎𝑡𝑡2 . Also, if 𝛾𝛾 > 0, then higher volatility will lead

investors to demand a higher risk premium. 𝜇𝜇 in equation (1) is interpreted as the8constant and average risk premium.
After producing evidence that an investor’s degree of risk aversion is timevariant, we next analyze whether the currencies presented above are indeed SHCs andelucidate when the degree of risk aversion of a particular currency changes. Therefore,
we apply a rolling estimation to the above GARCH-M and obtain the estimated 𝛾𝛾 inequation (1). 9 The left-hand side in equation (1), the foreign exchange risk premium𝑟𝑟𝑟𝑟𝑡𝑡 in this study, is defined as 10

𝑟𝑟𝑟𝑟𝑡𝑡 = 𝐸𝐸[𝑆𝑆𝑡𝑡+1 |Ω ] − 𝐹𝐹𝑡𝑡𝑡𝑡

(3)

where 𝑆𝑆𝑡𝑡+1 represents the logarithm of the spot exchange rate at time t+1, 𝐹𝐹t stands
for the logarithm of the time t forward exchange rate for delivery at time t+1, and𝐸𝐸[∙|Ω ] denotes the expectation operator on all information available at time t. Also,𝑡𝑡

𝑆𝑆𝑡𝑡+1 and 𝐹𝐹t are expressed as units of the currency per U.S. dollar.

Equation (3) shows a positive risk premium, meaning that investors enter into

forward rate contracts at a lower forward rate than the expected spot rate at time t,whereas a negative risk premium indicates that investors enter into forward rate

Chou et al. (1992) and Cotter and Hanly (2010) are among the researchers who usedrolling estimation to estimate the ARCH-M or GARCH-M’s time-varying parameter.
10 The definition of the foreign exchange risk premium is provided by Engle (2016), Fama (1984),
9

Canova and Marrinan (1993), etc.

9contracts at a higher forward rate than the expected spot rate at time t.
In general, the expected rate of return for a risky asset is higher than that fora safe asset if investors are assumed to be risk averse. In the FX market, trading the
U.S. dollar at the forward rate is interpreted as trading the safe asset. Trading the U.S.
dollar at the spot rate in the future is interpreted as trading the risky asset fordomestic investors. Accordingly, if investors in the FX market are assumed to be risk
averse and rational 11, then 𝑟𝑟𝑝𝑝𝑡𝑡 > 0 indicates that more investors are trading the U.S.

dollar at a lower forward price against the local currency than expected. However,when 𝑟𝑟𝑝𝑝𝑡𝑡 < 0 is observed, more investors are trading the local currency at a lower
forward price in terms of the U.S. dollar than expected.

The sign of risk aversion 𝛾𝛾 affects the sign of the risk premium. If 𝛾𝛾 > 0, thenincreased risk 𝜎𝜎𝑡𝑡2 results in 𝑟𝑟𝑝𝑝𝑡𝑡 > 0. The local currency is regarded as the SHC.

Investors consequently purchase the local currency to temporarily flee the U.S. dollar
because of losses arising from holding the U.S. dollar when the FX market isundergoing a risk-off episode. Similarly, if 𝛾𝛾 < 0, then the U.S. dollar is regarded as theSHC, and if 𝛾𝛾 = 0, investors are indifferent to the currency versus the U.S. dollar.
11

We postulate that there exist few risk-lover investors.

10

2.2 Data

For the SHCs used for this study, we use daily exchange rate closing-price data for the
New York market, as reported by Bloomberg. The data are three-month forward andspot data for the JPY, CHF, GBP and EUR (local currencies pairs with the dollar) fromJanuary 1, 2000 through November 30, 2018.
First, we generate a series of risk premiums at time t in equation (3) from the
spot rate at time t+ three months and the 3 month-forward rates at time t for the four
currencies.12 Table 1 presents results of the descriptive statistics, a Ljung–Box test,
and a Jarque–Bera test of the generated 𝑟𝑟𝑟𝑟𝑡𝑡 . As presented in Table 1, normality is

rejected at the 1 percent significance level by the Jarque–Bera test. The existence ofserial correlation is also suggested strongly by results obtained from the Ljung–Box(10) for all four currencies. This finding suggests that a risk premium can exist for all
four currencies and that the risk premium is time-variant. 13 Therefore, application of

Forward and spot exchange rate data used herein are only for business days. They
exclude weekends and national holidays. Accordingly, when the settlement date for thethree-month ahead forward data from time “t” is not on a business day, there is nocorresponding three-month ahead spot rate. In this case, we use data for the precedingbusiness day and match it with the spot rate to calculate the risk premium.

12

13

In addition, the kurtosis for the series of risk premiums for all three currencies is three or higher,

indicating the likelihood that volatility is changing every day. Chan et al. (2013) also reported

11the GARCH-M model is suitable for the sample.
#------------------------------------------------Insert Table 1 about here#------------------------------------------------Second, we estimate equations (1) and (2) (based on Quasi-Maximum
Likelihood estimation) using the above generated 𝑟𝑟𝑟𝑟𝑡𝑡 . The sample is then rolledforward by the end of each month starting from January 31, 2003 while keeping thewindow length of 804 business days unchanged.14 We obtain 188 rolling estimates foreach currency, with 𝜔𝜔� , 𝛼𝛼� and 𝛽𝛽̂ , all satisfying the required sign conditions. Stability

conditions of volatility clustering 𝛼𝛼� + 𝛽𝛽̂ for JPY, CHF, GBP and EUR are presented in
Figure 1. Dates used for the graphs in this study, including those shown in Figure 1,

are intermediate dates for the estimation window. For example, when January 2002 is
displayed at the bottom of the graph, the estimation window is the 804 business days

positive excess kurtosis for major currency pairs versus the U.S. dollar.
14

For our analysis we use a window length containing 804 samples. GARCH estimations havedemonstrated that a larger number of samples produce more stable results. For the window length,
we compared the results obtained when we have 543 (approximately 2 years) and 804 samples(approximately 3 years). The findings indicate that the stability condition 𝛼𝛼 + 𝛽𝛽 < 1 was better
satisfied when we used 804 samples.

12

before and after that date. As Figure 1 shows, the stability condition for all currencies
holds throughout the entire sample period, supporting our view that GARCH-M
estimation fits the data well.
#------------------------------------------------Insert Figure 1&2 about here#------------------------------------------------The estimated volatility 𝜎𝜎�𝑡𝑡2 of the three currencies in the foreign exchange

market is shown in Figure 2. Estimated volatility is the rolling average for theestimated 𝜎𝜎�𝑡𝑡2 resulting from the rolling estimation. In Figure 2, we also show the

simple monthly average values for the VIX as well as the rolling average for the VIXthat matches the rolling GARCH-M window.15 Our analysis reveals that a rise in therolling average for the VIX starts from the first half of 2007 and that it accelerates inthe second half of 2010. The September 2008 GFC, which disrupted financial markets
severely, is wedged between these two periods. However, the estimated volatility of the
JPY starts to rise sharply from the middle of 2005, followed by a similar movement in

15

Sample values for the VIX were calculated from VIX daily closing prices at the following URL:

https://www.cboe.com/tradable_products/vix/vix_historical_data/13

the other two currencies from the first half of 2007. Moreover, although volatility for all
three currencies peaks significantly around 2009, CHF volatility remains high until the
middle of 2011. From the middle of 2011, JPY volatility rises again.
Our model reveals that the two volatility parameters moved similarly until thelead up to the GFC (September 2008) but deviate slightly thereafter. Specifically, we
confirm that the rolling average of the VIX is flat but the estimated volatility of the
JPY rises from the first half of 2015 (Chinese stock market flash crash).
These findings indicate that, despite the average rolling 804 business day
estimated volatility window, the simulation adequately captures the VIX averagemonthly movements. The results also imply that idiosyncratic geopolitical risks, suchas the Chinese stock market flash crash, have a much greater effect on the JPY rate
than on either the EUR or the CHF. Although the original purpose of this study was
identification of which currency acts as an SHC when the “global financial market” isrisk-off, exactly when the global financial market is risk-off has in fact remained
unclear. No composite global economic index captures these episodes. Many recent
studies have sought to use the VIX to capture these episodes. However, it is apparent
that the estimated volatility from our model can capture both U.S. stock market

14

idiosyncratic risk and financial risk that is not idiosyncratic to the US stock market. 16

3. Empirical Findings
3.1 Japanese Yen

Figure 3 (1) depicts the investor risk appetite for JPY. The dates referenced here areanalogous to those used in the graph, but we caution readers to note that the date forthe estimation period is the intermediate of 804 business days.
#------------------------------------------------Insert Figure 3(1) about here#------------------------------------------------From the start of the sample period to immediately before the GFC in�
September 2008, γ is either negative or zero at approximately the 5% significancelevel. The JPY value shows a tendency to fall when volatility is high (that is, when the
US dollar is deemed to be the SHC). For example, γ� becomes significant and negativearound the second half of 2003, but the monthly spot rate sharply appreciates. This16

The strong correlation with US financial markets is not surprising given that the exchange rates

used for our study are given in relation to the U.S. dollar.

15

appreciation can be interpreted by the lower estimated volatility of the JPY in thesame period of Figure 2. In other words, the JPY trend has been determined by its
fundamentals because this period is risk-on.
Around the first half of 2004, γ� turns markedly negative. The estimatedvolatility also remains low, but the observed spot rate decreases slightly. This period
included large-scale foreign exchange intervention by the Japanese government to sell
the JPY to disrupt the yen’s rapid appreciation 17. Our analysis indicates that changes
in the value of the yen during this period follow the policy aims pursued by theJapanese government’s intervention, which also simultaneously induced γ� to decrease.
The negative γ� causes investors to attempt to purchase U.S. dollars and to short theyen in risk-off circumstances. The intervention in this period is interesting in that it
altered the attitudes of investors in the FX market as well 18.
During the GFC, even though the JPY appreciated sharply, γ� is notstatistically significantly different from zero: its 95% confidence interval band is wide.
Masujima (2017) also insist that the JPY is not an SHC for this period and this is

17

The Japanese government’s heavy intervention to curb the Japanese yen’s rise continued fromJanuary 2003 through March 2004.
18

Ito (2005) presents a discussion of intervention by the Japanese government during this period.

16

consistent with our results. A review of financial policy data during this period reveals
that, apart from Japan, six Western central banks lowered their policy interest rates
around October 8, 2008 (immediately after the GFC), and that the FRB began easing
operations in November of that year 19. Consequently, JPY appreciation during thisperiod was likely not attributable to the yen’s status as an SHC, but was instead driven
by changes in fundamentals such as widening interest rate differentials and thedecrease in the growth rate of Japan’s monetary base relative to that of the U.S.
The Japanese yen emerged clearly as an SHC from around the first half of2009 to roughly the middle of that year. Accordingly, the JPY appreciated. It is possible
that, because currency investors regarded the BoJ’s monetary easing stance as passive,the consensus that “the Japanese yen is a safe haven” spread gradually throughout
financial markets. This could account for the change in investor risk aversion in our
simulation.
One other interesting period was from the first half of 2012 to the end of 2014
(excluding the first half of 2013), a period when investor risk aversion remained

19

The BoJ action lagged the European and the U.S central banks. Although the BoJ lowered thepolicy rate by 0.2% (not 0.25%) from 0.5% to 0.3% on October 31, 2010, this was still small
compared to actions taken by the European and U.S. central banks.

17significant and positive. Immediately before investor risk aversion turns positive (thatis, when the yen becomes an SHC), credit rating companies including Standard andPoor’s (S&P) downgraded their ratings of U.S. sovereign debt. Also, the Swiss National
Bank (SNB) began to intervene to sell its currency in unlimited quantities (details
later). Both events likely induced investors to prefer the Japanese yen. During thisperiod, there were often sudden bouts of JPY appreciation. Some researchers haveattributed this phenomenon to Japanese yen repatriation by carry traders. Our results
are also interpreted as suggesting a strong likelihood of Japanese yen repatriation
during this period because investor risk aversion remains significant and positive. 20
This period overlaps with both the third round of quantitative monetaryeasing (QE3) by the FRB and the policy stance change of the BoJ. 21 The BoJ had
started to step up its massive monetary easing policy from the first half of 2013. The
JPY spot rate is roughly flat during this period, except for the first half of 2013,
because both the U.S. and Japan implemented monetary easing policies. In fact, the20

Chantapacpedong et al. (2017) and Chuffart and Dell’Eva (2020) investigate quantitative easing

and the carry trade in Japan during this period.
21

The FRB implemented the QE3 from September 2012 to October 2014 aimed at exercising low

inflation.

18

�
JPY depreciates sharply; γ becomes nonsignificant during the first half of 2013. This
period is consistent with the start of the Abenomics, which suggests that Abenomics
strongly affected market sentiment initially.

3.2 Swiss franc, British pound, and Euro

Figure 3 (2) shows the degree of investor risk aversion in relation to the CHF. From
immediately before the GFC, including Greek’s debt crisis (October, 2011), until thefirst half of 2011, the degree of investor risk aversion was positive at the 5%
significance level. 22 As the figure shows, with the exception of a brief period
immediately after the Greek debt crisis, the observed spot rate continued to appreciate
until an announcement by the SNB to buy foreign currencies in unlimited quantities in
September 2011. 23 This trend in the spot rate coincided with a positive degree of

22

Another important event that coincides with a positive risk aversion value in the first half of 2002

is the WorldCom accounting scandal in the United States.
23

The Greek debt crisis emerged after the GFC. The spillover effects led to increased buying of the

Swiss franc. The SNB intervened in the FX market to stop the appreciation of the Swiss franc at thistime, and began “unlimited” foreign exchange buying (the SNB set an exchange rate target of 1.2
francs to the euro) from September 2011. However, the SNB announced suddenly that it would end
the scheme in January 2015. The announcement caused the exchange rate of the franc against the19

investor risk aversion along with high volatility from August 2008. The CHF spot rate
depreciated sharply immediately after the beginning of unlimited intervention by the
SNB. Thereafter, it depreciated slightly or remained largely flat. This modest
depreciation might be attributable to the fact that the degree of investor risk aversion
remained positive, although partially not significant.
#------------------------------------------------Insert Figure 3(2) to 3(4) about here#------------------------------------------------The noticeable spike around the last half of 2013 might have been caused by
the European Central Bank’s (ECB) forward guidance, which signaled the ECB’s
intention to lower key interest rates. It also implies a strong link between the euro andfranc against the dollar.
To summarize, whereas the Swiss franc was interpreted as an SHC from thestart of the GFC and mostly until the Swiss Franc Shock in the first half of 2015, thecurrency plausibly lost its position as an SHC thereafter.24 Other researchers haveeuro to fall as much as 40% in only 20 min after the announcement (“Swiss Franc Shock”).
24

The SNB introduced a negative interest rate policy. The policy likely has a stronger effect on the

degree of investor risk aversion than unlimited intervention to sell Swiss franc.

20

pointed out that while the franc remained a favorite among carry traders after theGFC (similar to yen), it fell out of favor after the Swiss Franc Shock.25 Those
conclusions are supportive of our results.
Figure 3 (3) shows the observed spot rate of the GBP and its estimated degreeof risk aversion. For the GBP, the degree of investor risk aversion is positive at the 5%
significance level from the second half of 2007 through the second half of 2009.
Actually, the British pound might have lost its safe haven status for approximately
three years, starting from the first half of 2010, after markets grasped the implicationsof the Greek debt crisis. Thereafter, the pound regains its status intermittently from
September 2012, which coincides with the start of QE3 by the FRB, through the end of
the sample period.
The observed spot rate for the GBP decreases sharply after the GFC, although
the degree of investor risk aversion is significantly positive and its volatility is high(Fig. 2). It is somewhat inconsistent with the degree of risk aversion. However, theSwiss franc and euro (described later) were also simultaneously SHCs and likely more
attractive than the pound.

25

Tomio and Vallet (2020) present a relevant discussion.

21

In addition, the spot rate for the GBP starts to decrease dramatically frommid-2014 after it appeared that the Brexit referendum would pass in the UK. Resultsof our analyses indicate the pound is an SHC at this time, but the GBP depreciates
because its volatility is low.
It is noteworthy that once the SNB began intervening in the exchange rate
market, the GBP’s degree of investor risk aversion turned positive and statisticallysignificant for a few months. This phenomenon is evidence that the SNB intervention
affects the GBP.
Figure 3 (4) shows risk aversion effects for the EUR. The degree of investor
risk aversion is positive at the 5% significance level from the beginning of the sample
period (January 2000) to the first half of 2004, and from the GFC until the second halfof 2015 (China shock). The exception is in 2011: the period of the protracted Greek debt
crisis.
When the euro was first introduced as the official currency for the EU in
January 1999, it was initially the single currency for 11 countries, but was later
adopted by 10 new countries in 2004 with their migration to the EU. Our analyses
show that the euro was possibly an SHC until the first half of 2004. It was highly
trusted among global investors. Although the euro lost its SHC status from the last22

half of 2004, only the U.S. dollar became an SHC as an alternative to the euro until the
GFC because neither the yen, franc, nor the pound showed signs indicating SHC
status. In addition, our analyses suggest that the euro regained its SHC status in
September 2011 with the start of the SNB’s unlimited foreign currency market
intervention policy to sell the Swiss franc and buy the euro. This strengthens theconjecture that the SNB’s unlimited intervention policy, perhaps unsurprisingly,
affected EUR investor risk aversion.
In the first half of 2016 after the China shock, the strong possibility exists that
Brexit disrupted Eurozone stability. The euro became an SHC for a short period
immediately leading up to Brexit. But thereafter, both the euro and Swiss franc seemed
to lose their SHC status. Immediately after Brexit, the pound rose again. In short, ourresults imply that the euro is a substitute for the British pound and a complement for
the Swiss franc.

4. Conclusion
This research exploits the GARCH-M rolling estimation method to develop atheoretical degree of risk aversion coefficient that enables us to investigate whether
risk aversion is time-variant. The coefficient is then used to ascertain whether the four23

currencies discussed herein are SHCs, and, if they are indeed SHCs, during which
periods they exhibit safe haven characteristics. We conclude that the euro was clearly
an SHC in the early 2000s, as was the British pound around the Paribas Shock in
August 2007. The British pound, Swiss franc, and euro were SHCs at the time of the
GFC in September 2008. The Japanese yen emerged slightly later in the first half of2009 as an SHC. Additionally, the findings indicate that the Swiss franc and euro werepossibly SHCs when Greece’s debt problem first became public in October 2009. The
SHC subsequently varied among the four currencies, but all of the currencies gradually
stopped displaying SHC characteristics from the second half of 2015. Stated succinctly,
different currencies are used as SHCs in different periods.
The core of this study is not to discover why one particular currency is adopted
as an SHC. Undertaking such a challenge in previous studies uses factors that
stimulate changes in the degree of investor risk aversion 26. Such determinants are

26

Habib and Stracca (2012) investigate the fundamental qualities of SHC. Renaldo and Söderlind(2010) find that JPY, CHF, EUR and GBP tend to appreciate when U.S. stock prices decrease andU.S. bond prices and FX volatility increases. Additionally, Botman et al. (2013) find that neither
capital inflows nor monetary policy expectations explain the JPY’s safe haven behavior. However,they insist that changes in market participants’ risk perceptions trigger derivatives trading. De Bockand de Carvalho Filho (2013) suggest that economic factors related to currency appreciation in risk-

24

usually observable economic fundamentals, such as the current account balance or net
foreign asset position (ratio of GDP). However, our results raise the possibility that thedegree of investor risk aversion is related strongly to unobservable factors that
influence market sentiment, including those stemming from monetary policy
announcements and political events. Therefore, future research should look forunobservable ones, as well as looking for observable economic fundamentals.

off episodes are the current account, net foreign asset position, and capital controls.

25References
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Table 1 Descriptive Statistic, Ljung-Box Test, and Jarque-Bera Test
JPY

GBP

CHF

Number of observations

4869

4865

4868

EURO
4868

Mean

0.006

0.001

-0.003-0.001

Standard deviation

0.047

0.049

0.051

Minimum

0.051-0.176-0.179-0.190-0.145

Maximum

0.172

0.233

0.226

0.227

Skewness

0.056

0.788

-0.022

0.386

Kurtosis

3.367

6.154

3.227

3.373

LB (10)41208 ***

41364 ***

38779 ***

41261 ***

JB

29.909 *** 2519.5 ***10.885 *** 149.28 ***

LB (10)：Ljung-Box test statistic with 10 lags, JB：Jarque-Bera test statistic, ＊＊＊，＊＊，*: for the 1%, 5% and 10% significant level, respectively.

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