TRADING RULE PROFITS AND FOREIGN EXCHANGE MARKET

INTERVENTION IN EMERGING ECONOMIES

Leticia Garcia

A moving average trading rule is applied to 28 exchange rates in the post Bretton Woods period. 14 of these currencies are from developed countries and 14 from emerging ones. The trading rule produces significant excess return for 27 exchange rates. It is shown that the profit possibilities from using the trading rule are higher when trading currencies from developed countries compared with the emerging. The trading rule returns from developed country currencies existed even after several transaction costs are subtracted. Returns using emerging country currencies are almost vanish subtracting transaction costs. Explanations for the currency returns are found in the leaning against the wind central bank intervention.

2

It is well known that after the Bretton Woods agreement ended, many exchange rate

regimes in developed countries went from fixed to floating, generating more variability in the

nominal price of the domestic currency in terms of a foreign currency. In some periods this

variability increased in such a way that the monetary authorities of some countries decided to

avoid these sharply fluctuations with different degrees of market intervention. For example,

Federal Reserve “intervention operations generally have been carried out under the broad rubric

of countering disorderly market conditions” [Federal Reserve Bank of New York (1998)].

The rationale behind central bank intervention in the foreign exchange market (forex

from now on) is to “lean against the wind”. It implies buying own currency when it is

depreciating and selling when it is appreciating, to smooth market fluctuations that can have

serious effects on international trade and flows of capital.

But, why does the exchange rate sometimes change sharply? In a free market this is a

supply-demand phenomenon. Thus, changes in the demand and/or supply of foreign currency

will affect the equilibrium exchange rate. Demand and supply changes are the result of decisions

taken by buyers and sellers of foreign currency who do so in order to finance international trade,

invest abroad or speculate on currency price fluctuations.

Then, some of the market participants who move supply and demand in the forex with

floating regimes are traders seeking excess returns from speculation. The possibility to make

money buying and selling foreign currency is sometimes rejected in the economic literature,

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finding that forex is efficient and uncovered interest parity holds. However, some recent works

suggest otherwise, that excess returns can be generated by trading foreign currencies1.

Some of this work explores the use of technical trading rules that provide signals

regarding when to buy or sell the asset (foreign currency in this context). Even though these

trading rules are considered by many academics as atheoretical techniques that will not work in

practice, the literature has documented particular trading rules generating substantial excess

returns in the forex.

Profits provide incentives for additional participants to enter the market, that possibly

move the floating exchange in a direction and by a magnitude that produces destabilizing effects

in the international sector of the country. Central bank may prefer to smooth these variations via

a leaning against the wind strategy.

The chain of causality implies that profits in forex produce more participants that cause

the exchange rate to fluctuate; the central bank wants to mitigate these fluctuations with market

intervention. Nevertheless, the causality may also go in the opposite direction meaning that more

intervention sends signals to the market participants that profit opportunities are available. Some

believe that central banks stimulate speculation creating disturbing movements in the exchange

rate, instead of smoothing the fluctuations2.

The contribution of this paper is to look for links between profits from trading rules in

the forex and central bank intervention, using not only developed countries (the only countries

considered in the related literature) but also incorporating emerging markets. The inclusion of

1 For a complete review of the literature in forex see Taylor (1995). 2 The seminal paper in this regard is Taylor (1982).

4

these economies is motivated by the fact that their influence has been increasing in international

markets in the last twenty years.

Some emerging markets are characterized by a highly variable exchange rate. Does this

imply that the possibilities of profits by trading currencies are larger than those arising in

developed economies? Does central bank intervention help to explain these profit opportunities,

if they exist? Are there economically significant returns to be earned in developed and emerging

markets by using trading rules? Are there differences related to the type of economy?

The plan for the rest of the paper is to accomplish the following. First, to review the

current literature that addresses similar questions. Second, to propose a methodology for

exploring the returns from using trading rules in the forex and for testing the link between these

returns and central bank intervention. Third, to present and discuss the results.

LITERATURE REVIEW

In this section of the paper some relevant literature for the topic is described. The

review focuses on the following issues: a) Effects of central bank intervention on the forex. b)

Profitability of trading rules in currency markets. c) The relationship between central bank

intervention and trading rule profits. d) Forex and emerging economies.

Central bank intervention in the forex

In the literature of the eighties and early nineties an unusual degree of consensus is found

among economists, they state that intervention by central banks in the forex had a very small and

transitory effect on the exchange rate. Thus, it did not reach the stated objective of the central

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bank to smooth sharp variations3. During the nineties there is a return to consider that

intervention matters and the studies exploring the effects are done using new data and

econometric techniques. There are two possible channels trough which intervention can

influence the exchange rate: the portfolio and the expectation channels.

In the portfolio channel it is assumed that investors diversify their holdings among

domestic and foreign assets (including bonds) as a function of risk and the expected rate of

return. This theory explains how sterilized intervention affect the exchange rate. For example, a

purchase of German mark by the Federal Reserve Bank of New York is accompanied by

selling the appropriate amount of US bonds that maintain unchanged the monetary base. The net

effect of these transactions would be to increase the relative supply of US bonds versus German

bonds held by the public and to raise the dollar price of mark because investors must be

compensated with a higher expected returns to hold the relatively more numerous US bonds. To

produce a higher expected returns, the mark price of the US bonds must fall immediately. That

is the dollar price of mark must rise.4

The expectations channel studies how public information about central bank intervention

in support of its currency (at the moment when the intervention is actually performed, or when

plans to intervene in the future are announced), may under certain conditions, cause speculators

to expect an increase in the price of that currency in the future. Speculators will react to that

information buying the currency today, thus changing the exchange rate today.

3 See Henderson (1984) or Obstfeld (1990) 4 The exchange rate reaction to an increase in the relative supply of foreign assets will be reduced if there is a simultaneous increase in their expected rate of return that induces a corresponding increase in demand.

6

Dominguez and Frankel (1993) and Ghosh (1992) among others used the portfolio

channel approach to study the effects of central bank intervention. They analyzed the US dollar,

Deutsche mark -DM- and Swiss franc -SF-, and the corresponding daily intervention data and

found for some currencies an effect of central bank intervention on exchange rates in the second

half of the eighties.

The expectations channel was explored by Bonser-Neal and Tanner (1996) and Baillie

and Osterberg (1997), among others. In the latter paper, for example, DM and Japanese yen -

JY- where the currencies considered together with daily actual intervention data. They found

evidence that intervention has increased rather than reduced exchange rate volatility. It is

important to note that for many years the GM and the JY were the only two currencies in which

the US has conducted intervention operations [Federal Reserve Bank of New York (1998)].

Profitability of trading rules

Trading rules sold as easy ways to make money have a bad reputation among academic

economists. Nevertheless, they have been used to study the predictability of the stock market5

and, with more success, the predictability of the forex. Early studies as Dooley and Shaffer

(1983) and Sweeny (1986) found that the use of simple filter rules in the forex exploited the

forecastability of the exchange rate to produce excess returns.

More recently, Levich and Thomas (1993) explored the significance of technical trading

rule profits in the forex, using a bootstrap approach for comparison. Their methodology

employs a filter rule for generating buy and sell signals. A “z percent” filter rule leads to a

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strategy for speculating in the spot forex. Whenever the price of the foreign currency rises by z

percent above its most recent trough (buy signal), take a long position in that foreign currency.

On the other hand, whenever the price falls z percent below its most recent peak (sell signal)

take a short position.

After applying the filter rule for different values of z to 3800 daily observations for five

different currencies (British Pound -BP-, Canadian Dollar -CD-, DM, JY and SF) different

series of excess returns were calculated. The experiment performed to explore the significance

of these profits is that the returns calculated applying the filter trading rules to the actual

exchange rate series are compared to the bootstrapped returns. This means that returns are also

calculated applying the same filter rules to exchange rates generated by drawing numerous

random samples with replacement from the original set. Those scrambled series of exchange

rates retain the statistical properties of the original series but they lose the time series properties.

The purpose of the experiment is to see if the trading rules applied to these random walk

reconstructions of exchange rates perform as well as the returns from the actual time series.

What the authors found with the bootstrapped experiment is that the profitability from

simple technical models that was documented on data from the 1970's has continued in the 80's.

In addition, they found that the profitability of these technical rules is highly significant in

comparison to the empirical distribution of profits generated by thousands of bootstrapped

simulations. However, they did not attempt to explain the causes of these profits.

The immediate critique that this technique produces is regarding the size of z. there is no

objective way that traders can choose ex-ante the optimal size for the trading rule.

5 See for example Brock, Lakonishok and LeBaron (1991)

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Trading rules and intervention

Attempts to explain the excess returns are made by LeBaron (1999) and Szakmary and

Mathur (1997). These works look at a possible explanation for some of the predictability found

in the forex. One hypothesis is that central bank intervention introduces noticeable trends in the

evolution of exchange rates and creates opportunities for private market participants to profit

from speculating against the central bank. In this regard, the research coincides with the central

bank intervention affecting exchange rates via the expectation channel mentioned above.

LeBaron (1999) applies moving average trading rules to daily and weekly exchange rate

and interest rate data from Japan and Germany. Next, he removes from the exchange rate series

the days in which the Federal Reserve intervened. LeBaron uses the intervention values

provided by the Federal Reserve Bank.

Moving average -MA- trading rules consist of calculating

∑−

=−=

1

0,,

1 M

iitktk P

Mma (1)

where mak,t is the moving average for country k in time t, Pk,t is the number of dollars per unit of

foreign currency and M is the length of the moving average6.

sk,t is defined as a buy or sell signal,

<−≥

=currencyforeignsellmaPif

currencyforeignbuymaPifs

tktk

tktktk :1

:1

,,

,,, (2)

6 For the daily data M=152 and for the weekly data M=30. Le Baron (1998) mentions that traders commonly use those moving average lengths and that trading rule profitability is not overly sensitive to the actual length of the moving average.

9

Using the previous strategy, returns are calculated assuming two different possibilities.

One is that the investors buy and sell currency in the spot market, holding the non-interest-

bearing asset. The other is that investors buy and sell short-term bonds from the foreign country

with the currency trade.

In the first case the returns -xk,t- for the MA trading rule are calculated as

xk,t = sk,t (pk,t+1 - pk,t) (3)

where pk,t is the log of Pk,t.

For the case with interest rate, the returns -x*k,t- are

x*k,t = sk,t (pk,t+1 - pk,t - (log(1 + r*

k,t)) - log (1 + rk,t))) (4)

rk,t and rt* are the short-term interest rate for the k country and for the United States in time t

respectively.

Using the moving average strategy, equations (1) and (2), returns are calculated. For

example, the mean of the annualized daily returns for the JY from equation (4) is 10.4% and for

the DM is 8.58%. After demonstrating significant forecastability from a simple MA trading rule

for two foreign exchange series, LeBaron removes from the data the intervention days and

repeats the experiment. The returns drooped substantially, for the JY the mean goes to 4.42%

and for the DM to 2.08%. Thus, this predictability puzzle is reduced when the days in which the

Fed actively intervened were eliminated.

Szakmary and Mathur (1997) consider the same research question and a similar

methodology. They employ a MA trading rule that implies establishing or maintaining a long

position in a currency if the short term MA is equal to or greater than the long-term MA;

establish or maintain a short-position if the short-term MA is less than the long term MA. The

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short-term MA length, I, and the long-term MA length, J, are selected from a grid with I=1,

2,…, 9 and J = 10, 15, 20, 25 and 30. These grid values are arbitrary and no optimization

attempt is made. Even though the authors mention that all the 45 different strategies results are

reported, to calculate the returns for the following procedures they used the strategy that

produced the highest returns. Thus, the previously mentioned critique to the filter rule

approaches holds in this research because it is impossible for the traders to know ex-ante the

best length of the short-term and long-term MA.

The ensuing steps were to calculate returns and to estimate regressions using as

explanatory variable a proxy for central bank intervention. They used monthly change in central

bank international reserves as reported in International Financial Statistics. The currencies

examined in this paper are GM, JY, BP, SF and CD. The results showed that traders utilizing

MA rules could produce significant transaction-cost-adjusted trading profits in four of the five

currencies examined. A link between trading rule profits and central bank intervention was also

found which means that the coefficient of the intervention variable was statistically different from

zero in the corresponding regressions.

It is important to note two differences between LeBaron (1999) and Szakmary and

Mathur (1997). One is the trading rule employed, LeBaron chooses only the size of the MA

while Szakmary and Mathur chooses among several values for the long and short MA the ones

that produced the highest returns. The other is the way to introduce intervention, LeBaron uses

precise information from the Fed and he can only analyze the two currencies in which the Fed

intervenes; Szakmary and Mathur uses a more available and less precise variable. The

similarities are, that both found significant and positive returns, that intervention played some role

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in explaining these returns, and it did not differ when only spot exchange rate data were used,

compared to also considering the interest rate differential.7

Emerging markets

The last topic in this review concerns currency markets from emerging countries. Most

papers analyzing international financial markets are dedicated to developed economies.

Moreover, in the discussion of forex the literature studies three, five or at the most seven

developed country currencies. The explanation for this lack of studies is the difficulty of finding

reliable data from emerging economies. In the last five years some work started filling this gap,

for example, Harvey (1995) (analyzing stock markets) or McKinnon and Pill (1999) (currency

markets).

Matheussen and Satchell (1998) examined the possibility of using rules in trading stocks

in emerging markets based on mean-variance analysis. They introduced high transaction costs

because this is a characteristic of these markets. Using mean-variance optimization as a trading

rule for investors, they found significant profits even though high transaction costs were used.

25 emerging economies were considered using monthly stock indexes from 1990 to 1996.

My paper uses a moving average trading rule with monthly exchange rate data from 28

countries, 14 of these are considered emerging economies. It explores the profitability of buying

and selling foreign currency from the point of view of the US trader. It is not a portfolio analysis

because every foreign currency is considered in isolation. It assumes that investors buy and sell

7 LeBaron (1999) used returns with interest rate differential and without according to equations 3 and 4. Szakmary and Mathur (1997) used spot and futures data, the latter eliminating the need for an interest rate

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short-term bonds from the foreign country with the currency trade. Next, this paper tries to find

an explanation for the profits analyzing central bank intervention. A comparison between

emerging and developed economies in the search for profits and their reasons are emphasized.

METHODOLOGY

The following experiments have a twofold purpose. First, to explore the possibility of

making profits using trading rules in forex using not only developed country currencies but also

incorporating in the sample emerging economies. Second, to look for differences in the profit

possibilities when analyzing a wider set of countries and to check if these depend on the level of

financial development of the country. Third, to explore the links between profits and central

bank intervention that was found previously using a small number of countries, in a larger

context.

MA trading rule in developed and emerging currency markets

In the first part, the simple MA trading rule used by LeBaron (1999) equations (1) to

(4) is applied to 28 exchange rate series (14 developed countries and 14 emerging markets).

With monthly spot exchange rate data, returns are calculated considering the interest rate

differential (difference between domestic and foreign interest rate8) in order to calculate excess

returns from holding a foreign short-term security (equation 4). Alternatively, the returns are also

differential.

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calculated according to investors who buy and sell currency in the spot market, holding the non-

interest-bearing asset (equation 3). The latter returns are calculated for robustness. Using

monthly data some profit possibilities from highly variable exchange rates will be missed but

monthly returns are necessary because this is the span of the changes in international reserve

data. Monthly data also avoids excessive transaction cost.

The length of the MA is a debatable matter because there is no opportunity to know ex-

ante which horizon would be more profitable. Instead of using several trading rules and

reporting only the one that produces the highest returns, we use a MA of size 6 which is the

length for monthly data commonly used by traders [LeBaron (1998)].

The time period covers the post Bretton Woods period -1973 to 2000-. The sample is

also evenly divided into two subperiods, one from 1973 to 1986 and the other from 1987 to

2000. This corresponds to the idea that during the first half of the period the role of emerging

markets in the world economy was substantially different as from that in the second in which the

flows of goods and capitals to and from those economies increased dramatically. Considering

the 1990-1999 decade, the world average growth per year of international flows was around

5%, only Asia and Latin America were above that, with 7.7 and 7.9% respectively (World

Bank, 2001).

Transaction costs may make a difference in developed and emerging markets.

Subtracting the cost every time a buy or sell transaction is performed will incorporate different

8 The domestic interest rate is the 3-month T-bill rate; the foreign interest rate is the corresponding short-term risk free bond for each country.

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levels of transaction costs. Accounting for them implies calculating the returns of the trading rules

subtracting the appropriate transaction cost every time the signal sk,t changes sign.

The statistical significance of the trading rule profits is examined, firstly, via the standard

t-test. Secondly, the bootstrap approach used by Levich and Thomas (1993) is utilized.

Intervention: leaning against the wind

The intervention variable used is calculated with the change in international reserves of

the central bank. This is a proxy because intervention in practice is usually secret, “most

monetary authorities chose to intervene secretly” (Neely, 2001). The lack of actual intervention

data has been a major handicap to the literature (Edison, 1993). Changes in reserves may not

correspond to intervention for several reasons. On one hand, reserves can be used for

transactions other than intervention; on the other hand, intervention can de disguised

deliberately so that it will not appear in reserve changes (Neely, 2000). Thus, using change in

reserves as intervention implies some costs; nevertheless, it allows incorporating more countries

to the sample because the change in reserves is provided monthly to the IMF. The change in

international reserves needs to be corrected by some measure of the total transactions in this

currency market economy, nominal GDP is used to express the change in reserves as a

percentage of the total volume of transactions.

The change in reserves variable is defined as

1,

1,,,

−

−−=∆

tk

tktktk NGDP

resresinres (5)

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where �inresk,t is the change in reserves of the foreign central bank k during month t, resk,t are

the US dollar reserves holdings of central bank k at the end of month t, and NGDPk,t is the

nominal GDP of country k in time t expressed in US dollars. Further, leaning against the wind

intervention -LAWI-, is defined as, �inresk,t when appreciation of the foreign currency is

accompanied by an increase in dollar reserves, when depreciation is accompanied by a

decrease in dollar reserves, and LAWI is zero otherwise. The definition of LAWI coincides, in

part, with Szakmary and Mathur (1997), however here the change in reserves is defined

differently. Thus, LAWI is constructed as,

LAWIk,t = �inresk,t if {(Pk,t - Pk,t-1) > 0 and (resk,t - resk,t-1) > 0} (6)

= �inresk,t if {(Pk,t - Pk,t-1) < 0 and (resk,t - resk,t-1) < 0} (7)

= 0 otherwise (8)

where LAWIk,t is the leaning against the wind intervention performed by the country k central

bank in month t.

If the central bank reserves change follows a different rule, this is considered non-

leaning against the wind intervention -NLAW- and it is defined as

NLAWk,t = �inresk,t - LAWIk, t (9)

The following regression is estimated using the returns from equation (4) as a dependent

variable. The regression is estimated for each of the 28 currencies.

x*k,t = βk,0 + βk,1LAWIk,t + εk,t (10)

with all the variables as previously defined and εk,t the error term.

DESCRIPTION OF THE FINDINGS

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The monthly returns from the moving average trading rule

One of the purposes of this paper is to explore the performance of a moving average

trading rule in the forex market. Monthly data was used for 28 countries, 14 developed and 14

emerging. The countries chosen are those that had either a market rate, exchange rate

determined largely by market forces, or a managed floating system during the considered time.

They were divided into developed and emerging based on Matheussen and Satchell (1998).

Monthly returns that would be generated following the rule were calculated for two different

situations: with and without interest rate, according to equations (3) and (4). The complete

sample goes from January 1973 to December 2000, and it is also divided into two equal

subperiods, subsample 1 uses data from January 1973 to December 1986 and subsample 2

uses data from January 1987 to December 2000. Here transactions costs and bid-ask spreads

are ignored.

28 different series of monthly returns are calculated using equation (4). The mean return

is negative in only one case. The value of the t-statistic allows rejecting the hypothesis that the

mean return is equal to zero in 27 of the cases at the .05 significance level. Because the t-test

may not be appropriate due to the non-normality of the exchange rate series used to calculate

returns, the bootstrapped experiment done by Levich and Thomas (1993) and others was

performed. It was found that the probability of 5000 simulated random walks generating returns

as large as that in the actual data is less than 3.5% for 26 out of the 28 countries.

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In table 1 these monthly returns are presented. From Australia to United Kingdom

(UK) the countries are considered developed, from Brazil to Thailand, the countries are

emerging.

TABLE 1 MONTHLY RETURNS USING THE MA TRADING RULE

(M=6) COMPLETE SAMPLE

SUBSAMPLE 1 : 197301-198612 SUBSAMPLE 2 : 198701-200012

Complete Subsample1 Subsample2 Australia 0.0111

(9.78) 0.0106 (6.08)

0.0103 (7.14)

Belgium 0.0149 (11.40)

0.0154 (8.28)

0.0148 (8.26)

Canada 0.0055 (10.92)

0.0049 (7.17)

0.0059 (7.65)

Denmark 0.0147 (11.37)

0.0147 (7.86)

0.0152 (8.65)

France 0.0140 (10.91)

0.0135 (7.22)

0.0145 (8.37)

Germany 0.0148 (11.11)

0.0151 (7.78)

0.0150 (8.40)

Ireland 0.0138 (10.68)

0.0149 (8.23)

0.0135 (7.27)

Italy 0.0131 (10.41)

0.0122 (7.01)

0.0144 (7.75)

Japan 0.0159 (11.46)

0.0154 (8.15)

0.0163 (7.77)

Netherlands 0.0149 (11.21)

0.0150 (7.99)

0.0149 (8.12)

New Zealand 0.0049 (9.61)

0.0119 (5.98)

0.0012 (6.99)

Spain 0.0137 (10.76)

0.0126 (7.23)

0.0148 (7.74)

Switzerland 0.0169 (11.32)

0.0172 (7.92)

0.0172 (8.66)

UK 0.0139 (11.15)

0.0143 (8.75)

0.0124 (6.66)

Brazil -0.0839 (-2.52)

0.0096 (3.94)

-0.1674 (-1.83)

Chile 0.0129 (2.18)

0.0236 (2.10)

0.0030 (1.96)

Greece 0.0106 (8.63)

0.0103 (6.18)

0.0108 (6.01)

Hong Kong 0.0004 (2.56)

0.0002 (1.69)

0.0002 (1.51)

India 0.0077 (8.63)

0.0090 (8.52)

0.0065 (3.56)

Indonesia 0.0084 (2.37)

0.0102 (3.18)

0.0092 (1.25)

Korea 0.0071 0.0061 0.0090

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(4.64) (4.90) (2.87) Mexico 0.0059

(1.49) 0.0138 (1.99)

-0.0028 (-0.71)

Philippines 0.0070 (5.07)

0.0069 (3.39)

0.0093 (4.44)

Portugal 0.0131 (9.51)

0.0130 (6.23)

0.0132 (7.30)

Singapore 0.0061 (8.92)

0.0062 (6.53)

0.0063 (5.88)

S. Africa 0.0116 (7.07)

0.0137 (5.14)

0.0081 (4.47)

Sri Lanka 0.0066 (3.75)

0.0121 (3.87)

0.0006 (0.68)

Thailand 0.0056 (4.16)

0.0030 (2.91)

0.0092 (3.34)

Table 2

STATISTICS OF THE ANNUALIZED MONTHLY RETURNS

1973-2000 1973-1986 1987-2000 Mean 0.0819 0.1275 -0.0722 Sda 0.4003 0.0612 0.5604 Max 1.0176 0.2839 0.1586

Emerging Countries

Min -1.0068 0.0367 -2.0100 Mean 0.1621 0.1615 0.1624 Sda 0.0328 0.0360 0.0365 Max 0.2028 0.2075 0.2075

Developed Countries

Min 0.0660 0.0590 0.0709 Mean 0.1220 0.1451 0.0451 Sda 0.2817 0.0517 0.4076 Max 1.0176 0.2839 0.2075

All countries

Min -1.0068 0.0367 -2.0100 a The standard deviation is calculated with cross-section data to compare the variability among sets of countries.

Some comparisons can be made using the statistics of the monthly returns. First, we can

compare the emerging and developed economies. The average return from the emerging

countries is lower than the average return from developed countries. The variability

(measured by the standard deviation) is always higher for the former countries. Monthly returns

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can go from 1.32% to -16% for the 1987-2000 period in the emerging economies, for the

developed countries the returns for the same subperiod vary from 1.72% to 0.59%.

Second, the complete period can be contrasted with the two subsamples. Considering

all the countries, the average annual mean return drops from 14.51% in the first period to

4.51% in the second. This difference is due to the emerging countries because the monthly

return goes from 12.75% in the 73-86 period to -7.22% in the 87-00. On the other hand, the

same figure for the developed country currencies goes from 16.15% in the 73-86 period to

16.24% in the 87-00.

Comparing the exchange rate variability in the emerging country currencies with the

developed ones, it can be found that the first is much higher than the second considering the

complete period. That is, the average standard deviation of the exchange rate series for the

developed economies is 0.12 and for emerging countries it is 3.17. This last number excludes

the variability of the Brazilian currency, including it, the average variability goes to a 11 digit

number. If the excess return possibilities from trading currencies following a rule would be based

on the variability of the exchange rate, we would expect much higher profits from emerging

currencies. However, our results show the opposite, profit opportunities are higher trading

developed country currencies.

The case of the Brazilian currency is unique. The variability of the exchange rate is a 12

digit number. It is the only country in the sample that has a negative mean return for the

complete period. Brazil is the only case in which the mean return of the trading rule is negative (-

0.0839) for the 73-00 period. In the first part of the period the mean return is positive and small

(0.0096), in the second part, it is negative and large (-0.1674). This may be due to the

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characteristics of the Brazilian economy in the second half of the eighties in which Brazil faced a

strong debt crisis.

The negative 7.22% of annualized monthly return for the emerging currencies in the

second half of the period is seriously influenced by the Brazilian currency, and so it is the fact

that the overall performance of the trading rule produce returns in the emerging countries that

are half of the returns in the developed ones. Excluding Brazil from the emerging countries

sample the mean of the annualized monthly returns in the 87-00 period rises to 7.67% , while

the mean for all the countries increases to 12.11%. The difference between developed-emerging

currency returns does not change substantially through time: the developed currency returns

double the emerging.

In short, using moving average trading rules significant profit opportunities trading

currencies existed during the post Bretton Woods time. These profit opportunities were better in

developed markets. Emerging markets faced greater instability in their currencies and financial

markets that did not translate in a more profitable use of trading rules. It was the opposite, this

instability decreased the profit opportunities especially in the second half of the period in which

many of those countries experienced turbulence in their economies, and they also increased their

contribution to international flows of capital and goods.

Computing different returns

Several experiments were performed to explore the consistency of the moving average

returns. First of all, we change the signal followed by the investor. In the original set up the

currency trader is using a zero cost strategy of borrowing in one currency to go long in the

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other. It means to follow the signal from equation (2). This can be changed to a trading process

that starts with one US dollar investment and follows the signal

<≥

=nothingdomaPif

currencyforeignbuymaPifs

tktk

tktktk :0

:1

,,

,,, (11)

Second, the returns were compared to a buy and hold strategy. Third, the returns were

calculated assuming perfect foresight, in the sense that the trader knew ex-ante the change in the

exchange rate. Finally, using the original set up returns were calculated from the point of view of

a Japanese trader, to see if the previous results were only based in the performance of the

dollar/foreign currency exchange rate or they might be achieved using a different exchange rate.

9

TABLE 3 MONTHLY RETURNS FROM DIFFERENT SCENARIOS

1973-2000 Returns

following equation (11) signal

Returns from Buy and Hold strategy

Returns from perfect foresight

Returns from JY/foreign currency perspective

Australia 0.005553 (7.93)

-6.7E-05 (-1.36)

0.015996 (17.04)

0.015846 (10.08)

Belgium 0.007996 (7.96)

0.001071 (0.68)

0.021844 (23.16)

0.013472 (11.01)

Canada 0.002929 (8.12)

0.000284 (0.47)

0.008463 (23.49)

0.016563 (11.35)

Denmark 0.008507 (8.59)

0.002225 (1.44)

0.0214 (22.32)

0.013174 (10.68)

9 The returns were also calculated for the no interest rate differential case, that is, using equation 3, the results were similar to the original set up considering the interest rate differential.

22

France 0.007536 (7.69)

0.00105 (0.69)

0.020742 (22.15)

0.014879 (12.45)

Germany 0.007503 (7.06)

0.000143 (0.09)

0.022088 (22.93)

0.014715 (12.10)

Ireland 0.0035 (4.32)

-0.00689 (-4.68)

0.02087 (22.03)

0.016592 (12.32)

Italy 0.007053 (8.45)

0.000953 (0.65)

0.01948 (20.22)

0.014264 (10.25)

Japan 0.008575 (7.00)

0.001169 (0.70)

0.022055 (20.15)

0.015985 (11.46)

Netherlands 0.007882 (7.64)

0.000842 (0.53)

0.022077 (23.23)

0.014861 (12.10)

New Zealand 0.002504 (7.41)

-0.00094 (-0.66)

0.016285 (15.32)

0.010192 (10.19)

Spain 0.006994 (7.99)

0.000235 (0.15)

0.019183 (18.65)

0.015386 (10.43)

Switzerland 0.008375 (6.51)

-0.00021 (-0.11)

0.024531 (22.10)

0.015507 (12.60)

UK 0.007367 (7.77)

0.000795 (0.53)

0.019906 (20.88)

0.015801 (11.25)

Brazil 0.026142 (2.21)

0.13619 (4.23)

-0.06471 (-1.93)

-0.07159 (-2.15)

Chile 0.001678 (4.22)

-0.00959 (-1.61)

0.025325 (4.33)

0.025696 (4.39)

Greece 0.005051 (7.38)

-0.00056 (-0.40)

0.016103 (15.79)

0.013344 (8.91)

Hong Kong 0.000604 (4.66)

0.000781 (0.60)

0.000604 (13.74)

0.0172 (7.19)

India 0.002462 (5.02)

-0.00283 (-2.63)

0.010885 (12.18)

0.013533 (8.98)

Indonesia 0.002538 (2.11)

-0.00341 (-0.94)

0.013522 (3.86)

0.019259 (5.45)

Korea 0.004705 (8.79)

0.002236 (1.40)

0.009469 (6.29)

0.016587 (8.58)

Mexico 0.002998 (7.88)

8.42E-05 (0.02)

0.01974 (5.20)

0.018504 (4.36)

Philippines 0.003875 (7.38)

0.000735 (0.51)

0.010969 (8.46)

0.017293 (8.28)

Portugal 0.005651 (7.22)

-0.00185 (-1.17)

0.020085 (18.60)

0.01463 (10.37)

Singapore 0.003054 (6.02)

-8.5E-05 (-0.10)

0.009571 (17.21)

0.013058 (11.69)

S. Africa 0.004235 (4.49)

-0.00321 (-1.81)

0.016776 (11.16)

0.014683 (7.70)

Sri Lanka 0.00146 (3.01)

-0.00371 (-2.06)

0.009605 (5.59)

0.01848 (8.47)

Thailand 0.003357 (5.47)

0.001083 (0.78)

0.008457 (6.49)

0.015154 (8.98)

23

TABLE 4 MEANS OF THE ANNUALIZED MONTHLY RETURNS FROM DIFFERENT

SCENARIOS

Returns

following equation (11) signal

Returns from Buy and Hold strategy

Returns from perfect foresight

Returns from JY/foreign currency perspective

Emerging 0.058123 -0.01876* 0.091197 0.124999 Developed 0.081664 0.000566* 0.235646 0.181916 All 0.069893 -0.00874* 0.163422 0.153457

*These numbers exclude Brazil

On the first column of tables 3 and 4 we can see that the returns when using a modified

rule are still present. The t-statistics show that they are significant and the differences between

developed and emerging currencies hold. If we think about buying the foreign currency when a

buy signal appears and do nothing otherwise, the returns from the currency trading are cut by

half compared with the original rule.

However, the buy and hold strategy shows returns not significantly different from zero

for 23 currencies according to the t-statistic values. In four cases when the returns are

significant, they are negative. Brazil is again a special case because returns from buy and hold

strategy are significant, positive, and large.

The perfect foresight returns are calculated assuming that the traders could know in

advance the change in the exchange rate. All the mean returns, but Brazil, are significant and

higher than the ones coming from the other strategies. The difference between emerging and

developed is still present.

In the last column of tables 3 and 4 the returns were calculated from the point of view of

a Japanese trader. This was done to check if the returns are due to the US dollar/foreign

24

currency exchange rate. Assuming a Japanese trader who uses the moving average trading rule

buying and selling foreign currency during the time 1973-2000, positive and significant excess

returns were also obtained that are higher than the ones based on US dollar.

Calculating Sharpe ratios

In order to have some measure of the return corrected by the risk produced with the

trading rule strategy, Sharpe ratios were calculated using one-year periods, that is, the sum of

the returns that would be generated using the rule in one year.

Sharpe ratios10 for the zero cost strategy of borrowing in one currency to go long in the

other vary from 2.25 to -0.66 with an average of 1.54. For the developed countries the Sharpe

ratios fluctuate around 2, with an average of 2.14. The maximum is 2.25 for Japan and the

minimum is 1.8 for New Zealand. For the emerging economies Sharpe ratios show more

variability, its average is 0.93, with a maximum of 1.87 for Portugal and a minimum of -0.66 for

Brazil. Here the case of the Brazilian currency appears one more time, pulling down

considerably the Sharpe ratios for the emerging economies.

A close comparison with Lebaron (1999) results can be made using the Sharpe ratios.

He uses a moving average trading rule for DM and JY with daily and weekly data. Sharpe

ratios using one-year periods move between 0.66 and 0.96. The Sharpe ratios found here for

25

the same currencies and monthly data are 1.48 for the DM and 1.61 for the Japanese yen. His

Sharpe ratios are 0.689 for the DM daily and 1.033 for the JY daily. The ratio of the returns

corrected by risk is higher using monthly data, which may imply that the monthly investment was

more profitable than daily. He shows an annual mean return for the DM of 7% for the daily

investment and 7.91% for the weekly data; for the JY, 9.73% daily and 10.02% weekly. He

uses data from 1979 to 1992. With the monthly data the annual mean returns for the DM is

17% and 19% for the JY.

Our results can be also compared with more typical Sharpe ratios and annualized

returns. For example, buy and hold strategies on aggregate US stock portfolios produce Sharpe

ratios between 0.3 and 0.4 (Hodrick, 1987). In Brock, Lakonishok and LeBaron (1992)

annualized returns of 12% where obtained using trading rules with daily data US stocks for over

90 years when no transaction costs are included.

Including transactions costs

Profit possibilities existed in the forex market using trading rules during the 1973-2000

period. But, were these exploitable? Or is it the case that the transaction costs and bid-ask

spreads involved in the actual trade eliminate the profit making opportunities. In this part of the

paper we take into account these issues.

Following the trading rule for the currency trade there are three different sources of cost

for reducing the actual profit. First, there is a transaction cost present each time a currency is

trade. Second, there is bid-ask spread in the currency price that is very small for some

10 Sharpe ratio over a 1-year period is approximated as ( )

NE x k t

x

,

σ where σx is the standard deviation over

26

currencies but it is not for others. Third, if we are assuming to borrow one dollar each period to

follow the rule of buying and selling foreign currency, this represents another cost. It is important

to know how many times a switch of currencies will occur for addressing the first and second

costs. For the third, this cost is present each month.

TABLE 5 SWITCHING CURRENCY

NUMBER OF TIMES A SWITCH OF CURRENCY OCCURS IN 336 MONTHS (1973-2000)

Number of

times a switch occurs

%

Australia 58 18.29 Belgium 51 16.08 Canada 66 20.82 Denmark 57 17.98 France 49 15.45 Germany 53 16.71 Ireland 47 14.82 Italy 48 15.14 Japan 41 12.93 Netherlands 53 16.71 New Zealand 42 13.24 Spain 41 12.93 Switzerland 55 17.35 UK 59 18.61 Brazil 6 1.18 Chile 18 5.67 Greece 45 14.19 Hong Kong 65 20.5 India 43 13.56 Indonesia 14 4.41

the short horizon and N is the number of short periods in a 1-year period.

27

Korea 22 6.94 Mexico 22 6.94 Philippines 36 11.35 Portugal 45 14.19 Singapore 61 19.24 S. Africa 51 16.08 Sri Lanka 27 8.51 Thailand 50 15.77

Counting how many times sk,t from equation (2) changes sign we have information about

the numbers of trades that would occur for each currency. In this respect, again there is a

difference between emerging and developed countries that cab be noticed in table 5. The

average number of switches for the developed country currencies is 51 which means that it will

happen less than two times per year (1.8 times). For the emerging countries the average is 36,

that is, less than 1.3 times per year.

Studies (Szakmary and Mathur, 1997; LeBaron, 1999; Goodman 1979, Dooley and

Shaffer, 1983) that incorporate transaction costs for currency markets from developed

countries state that they are low, in the range of 0.05 to 0.2% for currencies like DM, CD, JY,

SF and BP. Ratner and Leal 1999, mention that transaction costs in emerging markets are

higher due to significant inefficiencies, they use actual costs for stock trading from different

emerging markets. Matheussen and Satchell assume 2% transaction cost for emerging markets.

These authors consider transaction costs as the ones related with broker fees and commissions.

Here, we use these numbers to recalculate the trading rule profits after transaction costs,

that is 0.1% for developed and 2% for emerging. The annualized return after transaction costs

are shown in table 6. 16% and 5.6% are the after cost returns from trading rules used in

developed and emerging country currencies respectively.

28

Bid-ask spread in the currency markets from developed economies are small and quite

constant over time (Melvin and Tan, 1996). Based on Financial Times they are around .05-

.09% for currencies like DM, CD, JY, SF and BP. In developing economies we may expect

large swings in the bid-ask spread on domestic currency. Using data from March 1987 to

August 1990, Melvin and Tan calculate bid-ask spreads for 25 currencies. 23 of them are part

of our sample of countries. Subtracting average bid-ask spreads to the annualized currency

returns after transaction costs, the ones from developed countries are slightly decreased to

15.84%, while for the emerging economies they are considerable reduced to 3.66%.

Third source of cost is the borrowing-lending interest rate differential. Assuming the

signal from equation (2), the trader needs to borrow each month to be able to follow the trading

rule. We may think that the trader starts with US$1 investment and lends this dollar every

month, at the same time that he borrows to buy or sell foreign currency. No matter what the

outcome of the strategy with the foreign currency is, at the end of the month, the trader will have

the interest produce by the dollar he lent, minus the interest of the one he borrowed. The

difference between the lending and borrowing rate in the US will reduce his profit as another

source of cost. LeBaron (1998) mentions that interest rate differential of 3% per year is

probably an upper bound on the borrowing and lending spread for the October 1977-

December 1989 period. Incorporating this annual cost, the returns from the developed countries

are reduced to 12.87% as an average. For the emerging economies the returns almost

disappear.

TABLE 6

ANNUALIZED MONTHLY RETURNS AFTER DIFFERENT COSTS

29

Returns – A Returns – (A+B) Returns–(A+B+C)

Emerging countries 0.0559 0.036699 0.006699

Developed countries 0.1603 0.158725 0.128725

a = transaction cost (broker fees and commissions) b = exchange rate bid-ask spread c = lending-borrowing interest rate differential

With the assumptions made in this paper, we can see that the profits from a moving

average trading rule applied to several currencies from developed economies existed even after

considering several sources of cost. This is not true for some currencies from emerging markets,

with inefficiencies present in higher transaction costs and bid-ask spreads.

Central Bank Intervention

We try to explain these profits from the currency trading with central bank intervention,

specifically, with leaning against the wind intervention. This is not an easy task because there are

not intervention data for the currencies considered here. Researches have always had a problem

with intervention data because it can only be found for two countries (Switzerland and

Germany) from the ones studied in this paper.

TABLE 7 CHANGE IN RESERVES, LEANING AGAINST THE WIND AND NON-LEANING

AGAINST THE WIND INTERVENTION %

� IN RESERVESa

LAWIb NLAWIc

All 0.4743 43.65 56.35 Developed 0.3047 52.25 47.75

30

Emerging 0.657 35.05 64.95 a As defined in equation 5. b Percentage of the change in reserves that followed leaning against the wind intervention. c Percentage of the change in reserves that followed non-leaning against the wind intervention.

What we do first to explore the central bank intervention in our set of countries is to

calculate the change in reserves as a percentage of GDP (equation 5) and to make a

comparison regarding the type of country. Second, we calculate the LAW and NLAW

intervention following equations (6) to (9), to see what percentage of the change in reserves can

be considered leaning against the wind or non-leaning against the wind intervention. These

percentages are shown in table 7 grouped by type of country.

Some points can be noticed from the previous exercise. The change in international

reserves as a percentage of the nominal GDP expressed in US dollars, shows a difference in the

two groups of countries. Emerging countries had more change in reserves, corrected by GDP,

during the 73-00 period. Second, the LAW intervention was contrasted among groups of

countries. In the developed countries 52% of the change in reserves followed the leaning against

the wind rule. For the emerging economies it was only 35% of the time. Thus, change in

reserves in emerging economies was higher but their central banks were not leaning against the

wind to protect the exchange rate from fluctuations. So, what were they doing?

Neely, 2000 gives some clues to find an answer to that question. He states that change

in reserves are bigger than intervention because reserves may be used for other purposes

besides supporting the currency. For example, they can be used for the payment of foreign

currency denominated public debt and its interest. Contrasting the interest payment for the

foreign debt as a percentage of GDP from developed and emerging countries, large differences

31

can be found. For the 73-00 period, the average of annual foreign debt payments for developed

countries represents 0.093% of the GDP. In the emerging economies it was 2.73% of the GDP.

Without more precise measures of intervention, we might think that the change in

reserves was used to lean against the wind in developed countries and to pay foreign debt or

interest on that in emerging countries.

That could be an explanation why the emerging economies appear like following a non-

leaning against the wind type of intervention. The change in reserves, LAW and NLAW

intervention were also calculated for the two subperiods, no big differences were found

compared with the complete sample.

Intervention regression

Looking for explanations for the monthly returns from using the trading rule, equation

(10) is regressed for all the countries, using as a dependent variable the returns with interest rate

and without. If leaning against the wind intervention has some explanatory power for the returns

the β1 coefficient should be significant and positive. Table 8 presents the regressions results for

the 28 countries when the returns including interest rates are used as dependent variables. For

comparison, two other regressions were estimated using the returns as dependent variable. In

column second and third of table 8 the explanatory variable is the change in reserves from

equation (5). Columns fourth and fifth showed the regression coefficients for the leaning against

the wind intervention. The last two columns show the coefficients for the non-leaning against the

wind intervention. The constant term is significant for most of the cases in the three different

specifications.

32

The estimated coefficient when using change in reserves as explanatory variable is

significant for three currencies, showing a negative coefficient for two of them. The change in

reserves is showing more than intervention and this variable does not have explanatory power

for the majority of the currency returns.

For the non-leaning against the wind intervention, we expected to find that the variable

is either non-significant or negative. The more the central bank acts non-leaning against the

wind, the less returns traders can make because they are following the wrong signal. Assuming

that they expect the central bank to intervene against the wind, they would react accordingly,

but the returns are not as expected and thus this strategy will loss explanatory power for the

moving average returns. In 9 currencies, the NLAW intervention is significant, in seven of these

the sign of the coefficient is negative as expected.

In 18 out of the 28 currencies we could find significant and positive coefficients for the

leaning against the wind intervention. The regressions were also estimated for the non-interest

rate case and the results were similar (not shown here). In all the cases the coefficients are

positive as expected, meaning that the more the central bank intervene with a leaning against the

wind strategy, the more returns traders could make using a moving average trading rule.

TABLE 8

INTERVENTION REGRESSIONS

� IN RESERVESA LAWIB NLAWIC

á0 á1 β0 β1 ã0 ã1

Australia 0.01151 (7.15)

-0.19527 (-0.30)

0.00836 (5.16)

2.61307 (3.15)

0.01135 (8.52)

-0.3184 (-0.40)

Belgium 0.01432 (8.11)

0.234066 (0.50)

0.01334 (8.53)

0.86017 (1.41)

0.014703(9.78)

0.38330 (0.67)

33

Canada 0.00556 (7.69)

0.005240 (0.01)

0.00465 (5.32)

1.71196 (2.59)

0.00545 (9.57)

0.74256 (0.14)

Denmark 0.01560 (8.71)

-0.18846 (-0.66)

0.01421 (7.74)

0.22346 (0.86)

0.014468 (9.51)

0.121173 (0.40)

France 0.01520 (9.01)

-1.01330 (-1.13)

0.01462 (7.43)

3.85497 (2.68)

0.01512 (10.36)

-0.72066 (-0.74)

Germany 0.01453 (8.76)

0.173763 (0.30)

0.01347 (8.19)

0.7163 (2.08)

0.01545 (10.73)

-0.72965 (-1.89)

Ireland 0.01337 (6.98)

0.089430 (0.54)

0.01361 (7.08)

0.03995 (0.21)

0.012774 (8.33)

0.284429 (1.72)

Italy 0.013875 (6.94)

-0.354114 (-0.50)

0.01369 (8.36)

0.02424 (1.07)

0.010644 (6.96)

-0.97210 (-1.96)

Japan 0.01562 (8.59)

0.464114 (0.30)

0.01050 (6.35)

0.29320 (7.21)

0.015725 (11.43)

0.709801 (0.70)

Netherlands

0.015838 (8.73)

-0.301023 (-0.60)

0.01395 (7.66)

0.04262 (1.01)

0.014899 (9.83)

0.253125 (0.46)

New Zealand

0.01132 (6.49)

0.124489 (0.49)

0.01169 (7.07)

-0.01226 (-0.78)

0.008809 (6.15)

1.18244 (4.33)

Spain 0.014090 (8.05)

-11.10372 (-0.28)

0.01065 (6.09)

0.80054 (1.96)

0.013896 (9.62)

-14.7918 (-0.28)

Switzerland 0.01713 (7.87)

-0.018626 (-0.11)

0.01601 (7.56)

0.13289 (2.38)

0.016654 (9.41)

0.060058 (0.33)

UK 0.01514 (9.64)

-0.796005 (-1.08)

0.01278 (8.81)

1.26756 (2.95)

0.015286 (10.95)

-1.40912 (-1.96)

Brazil -0.0297 (-1.04)

-1.2E+10 (-8.58)

-0.06670 (-0.47)

0.45700 (0.64)

-8.34E-03 (-0.26)

-1.2E+10 (-7.61)

Chile 0.017508 (2.29)

-0.665264 (-0.95)

-0.00703 (-1.16)

0.18854 (4.32)

0.009598 (1.35)

0.800055 (0.94)

Greece 0.01145 (7.08)

-0.214587 (-0.76)

0.00775 (4.63)

0.53039 (2.55)

0.009807 (7.15)

0.319434 (1.27)

Hong Kong 0.000373 (0.57)

-0.006448 (-1.15)

-0.00025 (-0.44)

0.00779 (0.34)

-0.00011 (-0.20)

0.006737 (0.69)

India 0.00848 (5.82)

-0.407381 (-0.59)

0.00674 (5.20)

0.00779 (0.34)

0.007199 (5.96)

1.027037 (1.28)

Indonesia 0.004292 (0.92)

0.693068 (0.85)

0.00508 (1.08)

0.812721 (3.40)

0.004878 (1.77)

0.091679 (0.15)

Korea 0.00692 (3.50)

0.013345 (0.20)

0.00159 (0.74)

0.362667 (3.88)

0.006062 (3.52)

0.102720 (1.44)

Mexico 0.01072 (2.30)

-1.358706 (-1.93)

-0.00172 (-0.37)

0.29157 (6.47)

0.009581 (2.05)

-1.66817 (-1.63)

Philippines 0.006838 (3.71)

0.010678 (0.14)

0.00561 (3.17)

0.63569 (2.09)

0.006817 (4.29)

0.013144 (0.14)

Portugal 0.010367 (5.03)

0.440631 (1.88)

0.01364 (7.79)

0.0330 (2.84)

0.014459 (8.41)

-0.31249 (-1.24)

Singapore 0.00632 (7.59)

-0.017894 (-0.46)

0.00332 (3.15)

0.24574 (5.31)

0.006462 (9.54)

-0.04873 (-1.70)

S. Africa 0.011602 (5.06)

0.042057 (0.05)

0.00805 (3.57)

0.72197 (2.30)

0.011219 (5.93)

0.557819 (0.54)

Sri Lanka 0.00863 (3.76)

-0.396433 (-1.36)

0.00325 (1.37)

0.04077 (1.25)

0.003133 (1.62)

-1.24612 (-3.97)

Thailand 0.00429 (2.46)

0.284433 (1.15)

0.00103 (0.55)

1.12090 (2.87)

0.001171 (1.03)

-1.40667 (-6.87)

34

t-values in parenthesis a x*

k,t = ák,0 + ák,1�inresk,t + æk,t b x*

k,t = βk,0 + βk,1LAWIk,t + εk,t c x*

k,t = ãk,0 + ãk,1NLAWIk,t + çk,t

Comparing the after-cost annual returns in those countries where the LAWI is significant

with the countries in which it is not, they were found significantly higher as we can see in table 9.

This was true even eliminating the data for Brazil that would biased downward the average

return of the second group. That conclusion also holds for the Sharpe ratios.

TABLE 9

ANNUAL RETUNS AND SHARPE RATIOS

ANNUAL RETURN AFTER COST

SHARPE RATIO AFTER COST

Countries with significant β1 0.123698 1.611712 Countries with insignificant β1 0.002102 0.82159 All the countries 0.0715 1.5730

For more than half the countries in the sample the proxy calculated for the LAW

intervention helps to explain the returns from the moving average and those are significantly

higher than the rest. This finding agrees with what Szakmary and Mathur (1997) found using a

different trading rule and daily data for foreign currency futures contracts. They studied five

currencies and the central bank intervention in a LAW direction is significant for all of them.

These five currencies are included here and they all have significant coefficients for the

intervention variable, they are CD, GM, JY, SF and BP.

35

The activism of the central bank in the foreign market may send signals to trend-

following speculators and allow them to earn abnormal returns trading currencies. This seems to

be true for 18 currencies in the sample. The activism of the central banks in the forex in a LAW

direction shows some relation with the profits that currency speculators could make using a

moving average trading rule for some currencies but not for all. These may be due to the fact

that the intervention variable calculated with the change in international reserves is hiding some

of the intervention operations that the central bank had during one specific month. One action

taken at the beginning of the month may cancel out another action taken in the middle so there is

no change in comparing two months. Then a more refined measure of intervention is needed to

test the relationship with trading rule returns.

One way to explore central bank intervention is to ask directly to them. This is not easy

because for the majority of the monetary authorities these intervention practices are kept secret.

In a survey to central banks done by Nelly, 2001, we found that several of the countries that we

consider here replied to the survey. The only central bank authorities that say they did not

practice any intervention during the last decade was the one from New Zealand. The monthly

return from the moving average trading rule in table 1 shows that New Zealand has the lowest

returns considering the developed countries and it also has the biggest difference when breaking

the sample in two periods, showing a decreasing of 1.07% in the average monthly return from

the 73-86 to the 87-00 period. If traders are observing central bank actions and there is no

intervention, their ability to earn profits is considerable reduced.

There are two final comments regarding the results presented. First, the proxy for

intervention is only a rough approximation for actual intervention; it may either overestimate or

36

underestimate the actual intervention. In addition, there exist a temporal aggregation problem,

for example, the central bank may aggressively buy dollars in the first half of the month and sell

dollars in the second half, and the reported change in reserves would be close to zero.

However, reserve changes are available for all the IMF countries.

Second, a simultaneity problem is present in the case of currency trade returns and

intervention, and the chain of causality is not clear. To address this simultaneity problem is an

opportunity for future research.

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Table 1.2