Explaining order flow in the foreign exchange market in Iceland

Kari Sigurdsson

London School of Economics

Word count: 5759 The copyright of this dissertation rests with the author and no quotation from it or information derived from it may be published without the prior written consent of

the author.

Abstract In recent years market microstructure models have been developed that are quite

successful in explaining exchange rate dynamics over short horizons with using order

flow (Evans 2001). This should not come as a surprise since order flow can be thought of

as a proxy for supply and demand. However, market microstructure theory emphasizes

the informational aspect of order flow and treats it as an exogenous variable. The

interesting question is therefore whether it is possible to explain the order flow and treat

it as endogenous variable.

This paper focuses on explaining the order flow by using two approaches. The

first approach belongs to traditional time series models (ARMA and GARCH). The

second one is applicable due to special circumstances in Iceland and uses macro

economic variables to explain the order flow. The research question is: “Is it possible to

explain order flow?”

The answer put forth in this study is that time series models have reasonable

explanatory power for speculative customer order flow (R2=0.29) but almost no

explanatory power for non-speculative order flow. Furthermore, monthly foreign trade

figures appear to be worthless in explaining the order flow despite the high ratio of

foreign trade to currency market turnover in Iceland.

2

Introduction Determinants of exchange rates have been studied extensively during the last

decades and many macroeconomic models of nominal exchange rates have been

developed. However, they have all been shown to perform poorly for shorter than 6

months horizons (Frankel 1995). The proportion of monthly exchange rates changes that

these models can explain is essentially zero (Meese and Rogoff 1983a; Meese and Rogoff

1983b). After the landmark results by Meese and Rogoff, researchers have searched

extensively for macroeconomic variables that can explain exchange rate movements over

short horizons. In the past couple of years, several papers have presented models of a

new kind. Instead of relying exclusively on macroeconomic determinants, these models

are based on market microstructure theory and have included order flow as a determinant

for exchange rate dynamics (see for example (Lions 1995), (Evans 2001) and (Rime

2001)). The models have shown that order flow is an important determinant of exchange

rates, both intra day and between weeks.

After it became clear that order flow explained a large fraction of exchange rate

movements over short horizons the question has been what is really driving the order

flow. Two main hypotheses have been put forth: (i) Private portfolio shifts, unrelated to

macroeconomic variables (these shifts can be due to changing risk tolerance, changing

hedging demand, changing liquidity demand, etc.) and (ii) Macroeconomic information

(Evans 2001). The latter hypothesis has been supported empirically and it has been

shown that about 50 percent of the variance in order flow is driven by macroeconomic

announcement (Evans 2001).

In contrast with the study of Lyons and Evans, this paper focuses only on order

flow dynamics and does not relate it to the exchange rate. This is done because order

flow per se is an interesting aspect of the currency market (it can be thought of as a proxy

for supply and demand). The remainder of the paper is in five sections. Section one

presents two models for explaining the order flow. The first one is ARMA-GARCH

model on daily frequency and the second one is a linear regression on monthly frequency

with macroeconomic explanatory variables. Section two describes the special

circumstances in the Icelandic currency market and the dataset that was obtained from the

market. Section three presents the empirical results. Section four concludes.

3

Models

The Lyons and Evans model (Evans 2001) examines the link between order flow

and macro announcements and then the authors go on relating exchange rate variation to

various sources. This paper takes a different approach. Instead of trying to explain

exchange rate variation by using order flow and announcements, this paper focuses

exclusively on explaining the customer order flow by paying closer attention to the

special Icelandic order flow dataset and special circumstances in the Icelandic currency

market. The question whether order flow affects the exchange rate is left unanswered.

Two models are used to explain the customer order flow. The first one is

traditional time series model with ARMA terms and GARCH variance. The second

approach is less traditional and makes use of macro economic variables in trying to

explain the order flow. It is only appropriate because of the quality of the dataset and the

special circumstances in Iceland.

Two things are unique for the situation in Iceland and motivate the second

approach. The first one is the high ratio between external trade and foreign exchange

market turnover (see Table 1). The second thing is the high precision in measuring

external trade because Iceland is an island and custom authorities can therefore keep

good track of foreign trade. This is not the case in the Euro zone for example where a lot

of different customs authorities have to coordinate the registration of foreign trade.

Table 1 International trade and currency market turnover

Currency Annual international trade in goods (billion USD)

Annual spot turnover (trillion USD)

Ratio of trade to turnover

US dollar 1,561 97,994 0.00%Euro 3,699 49,928 0.01%Japanese yen 627 30,187 0.00%Pound sterling 497 12,578 0.00%Swiss franc 138 8,083 0.00%Canadian dollar 401 4,696 0.01%Swedish krona 128 1,839 0.01%Danish krone 80 896 0.01%Norwegian krone 75 782 0.01%Icelandic krona 4 0.01 29%

Source: OECD, Central Bank of Iceland, BIS

The length of the dataset (1.5 year) makes it possible to get sufficiently many data

points to estimate a regression on monthly frequency. The external trade figures are on

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monthly frequency but order flow and interest rates are on daily frequency and one is

always forced to use the lowest frequency. The difference in frequency between order

flow and macroeconomic variables is a well known challenge in the literature about

exchange rates but using external trade, aggregating order flow on monthly frequency

and using one month interest rates is a reasonable compromise considering of how likely

candidate the external trade is in explaining the order flow.

Order flow and time series approach

ARMA-GARCH model is used for the times series approach. The ARMA terms

represent the time series structure in the first moment. The ARMA model is represented

by

ARMA(1,1)

Xt = c + p1 Xt-1 - q1 εt-1+ εt (Equation 1)

where Xt is the order flow in ISK, εt is a normally distributed error term, p1 is the

autoregressive term and q1 the moving average term. c, p1, q1 are estimated parameters.

The GARCH terms capture the time dependence in the second moments,

frequently referred to as volatility clustering. The GARCH model is represented by

GARCH(2,1)

σ2t = χ + θ1·ε2

t-1+θ2·ε2t-2+ φ1·σ2

t-1 (Equation 2) σ2

t is the one period ahead forecast variance, θ1 and θ2 represent autoregressive terms

and φ1 the moving average term. χ, θ1, θ2 and φ1 are estimated parameters.

Order flow and macroeconomic variables

Two equations where estimated for the macroeconomic variables. The first

equation is for buying and selling order flow and is represented by

Xt = β0 + β1·Et + β2·It + εt (Equation 3)

where Xt is either buying or selling order flow,·Et is export of goods and It is import of

goods, all measured in ISK. εt is a normally distributed error term. β0, β1 and β2 are

estimated parameters.

The second equation is for net order flow and is represented by

Nt = β3 + β4·(Et -It) + εt (Equation 4)

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where Nt is net order flow, Et export of goods and It import of goods, all measured in ISK.

εt is a normally distributed error term. β3 and β4 are estimated parameters.

As mentioned above absolute value of export and import of goods is

approximately 30% of the total turnover in the currency market so if there is a definite

connection between order flow and trade the import should explain selling and export

should explain buying of ISK for non-speculative trading. However, there is no reason to

expect trade to influence speculative order flow directly unless speculators take big

positons on the basis of information about foreign trade. This is deemed to be an unlikely

situation.

Data

The microstructure of the market for the Icelandic krona has changed

considerably during the last decade. In 1995 capital and foreign exchange transactions

were liberalized and in 1997 commercial banks took over the role of market making from

the Central Bank of Iceland (Már Guðmundsson 2000). The market turnover has

increased rapidly since then and the market has evolved from being small with high

degree of Central Bank involvement to a more liquid market with low Central Bank

involvement.

The Icelandic inter-bank market for foreign currency is a closed decentralized

market that consists of four Icelandic commercial banks and the Central Bank (see Figure

1 ). One of the main characteristics of the inter-bank market is the small size and limited

liquidity. The turnover is approximately 0.00003% of the turnover in the USD market

with an average number of 33 trades per day where most trades are 1.5 million USD.

The inter-bank market is open from 9:15 to 16:00 (local time) on all business days of the

year.

6

Bank 1

Bank 3

Bank 4

Bank 2

Central Bank Customers

Customers

Customers

Customers

Customer order flow

Inter-bank order flow

Figure 1 The Icelandic inter-bank market for foreign currency

The order flow data is from two of the biggest commercial banks with about 56%

combined market share in the inter-bank market and considerably higher market share in

the aggregate customer order flow1. Customers are classified into two groups:

Speculators and Non-speculators. The banks (i.e. the data vendors) classified customers

separately according to the following criterion:

Speculators: Customers whose trades are mainly (more than 50% of trades)

to profit on short term fluctuations in the exchange rate. Non-Speculators: All other customers (excluding other inter-bank dealers).

Classification was done on customer level so all transactions from a certain

customers fall into the same group. Classification of each transaction would of course be

more accurate but unrealistic from a practical point of view. Separate classification (done

by the banks) can affect data consistency but this was unavoidable due to the confidential

obligations towards the customers.

As mentioned before the dataset is of good quality because (i) it is longer than

usual order flow datasets, (ii) it contains exact values of order flow, but often these types

1 A general market consensus.

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of datasets only contain +1 and –1 for buy and sale respectively, (iii) the order flow is

between customer and broker, but sometimes it only has dealer-to-dealer flow and (iv)

last but not least the dataset is split up into two groups of speculators and non-speculators

which has never been seen before in the order flow literature.

Daily total turnover in the inter-bank market is from the Icelandic Central Bank.

Each transaction is only counted once.

In addition to order flow data interest rates are often used in order flow models.

In this case interest rates are the rate of interest at which banks borrow funds from other

banks (LIBOR for the bigger currencies but local inter-bank market rates for the smaller

ones). The foreign interest rates are then weighted according to the Icelandic current

account (i.e. trade weighted). The weights are adjusted yearly according to official

current account figures.

In addition to daily order flow, turnover and interest rates, a dummy variable for

announcements was created. The variable takes value of one in case of announcement

but zero otherwise. Types of announcements that are generally considered to influence

the order flow were chosen after an interview with a currency broker. The

announcements are about change of trading rules, external trade figures, total fish quota2,

major Central Bank intervention, major government funding agreements, consumer price

index, Central Banks balance sheet announcement and interest rate changes. A total of

67 announcements where used.

2 Fish and fish products account for 40% of goods exported from Iceland and announcement of fishing quota affects market behavior.

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Table 2 Order flow - summary statistic Mean Median St.dev. Skewness Kurtosis

Customer Speculators

Buy 0.73 0.39 0.91 1.95 4.22Sell -0.69 -0.38 0.84 -1.70 2.70Net 0.04 0.00 0.68 0.57 6.35

Non-Speculators

Buy 0.81 0.67 0.50 2.07 6.24Sell -0.77 -0.62 0.60 -2.50 8.63Net 0.04 0.07 0.58 -1.06 5.87

Total

Buy 1.54 1.21 1.15 1.86 4.56Sell -1.46 -1.14 1.15 -1.55 2.84Net 0.08 0.12 0.83 -0.50 2.93

Inter-bank market

Total market turnover 4.29 2.58 5.09 2.40 7.45

Interest rates RISK-Rforeign 0.07 0.08 0.02 4.09 56.33

Period: 3 July 2000 – 31 December 2001 (1.5 years) Number of observations: 375 All numbers are in billion ISK Interest rates are overnight inter-bank offer rate and foreign interests rates are trade weighted. Source: Islandsbanki, Landsbanki, Central Bank of Iceland and British Bankers Association.

Table 2 presents summary statistics for the order flow data. Speculators and non-

speculators are buying and selling approximately equal amounts of ISK. However,

looking at the median and standard deviation one can see that their trading pattern is

different. The order flow of non-speculators is centered around the mean while the

speculators’ order flow mass is close to zero. This fundamental difference is reflected in

higher variance and lower absolute median (see Figure 2 and Figure 3 in the appendix).

Non-speculators have steady demand for (supply of) ISK while speculators have low

demand (supply) on average but sometimes they buy (sell) excessively.

While there appears to be a clear difference in the buying and selling order flow

between these groups, one can not distinguish any fundamental difference between the

groups from the net order flow (see Figure 4 in the appendix). Furthermore the

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distribution of the total order flow exhibits similar pattern as return series, i.e. fat tails

(kurtosis is above 0) and low spread around the mean compared to normal distribution

(see Figure 5 ). The distribution is little bit skewed to the left (long left tail).

By visually examining the inter-bank market turnover (see Figure 6 ) two things

can be observed. Volatility clustering and about 70% of the inter-bank order flow can be

directly related to the customer order flow. The first observation is common to financial

time series but the second one is because the banks do not take large positions and

therefore customer order flow is directly transmitted to the inter-bank market. In fact, no

market maker is allowed to have a net foreign exchange balance in all currencies

exceeding the equivalent of 30% of equity at the beginning of the year. For the year 2002

this number was approximately 18 billion ISK ((Bunadarbanki Islands 2002; Islandsbanki

2002; Kaupthing 2002; Landsbanki Islands 2002)). It is worth mentioning that the

currency market in Iceland is relatively young and the turnover has increased rapidly in

the last few years. In 1996 the turnover was 80 billion ISK (80% were Central Bank

transactions) and in 2001 this figure was 1200 billion ISK and only 2% of the

transactions were due to the Central Bank (Central Bank of Iceland 2002b). The market

has therefore developed considerably and will most probably keep on growing until some

equilibrium level has been reached.

Interest rates difference was on average 7 percentage points and that created an

incentive for speculators to do the “carry trade”, i.e. borrow in foreign currencies and

invest in Iceland. This was especially lucrative when the currency bands were in place

but they were abolished on 27 Mars 2001. While the currency band held, there was a

limit on the maximum loss on the carry trade3. One of the simplest way to gain from the

interest rate difference is to buy the ISK forward but the forward exchange rate is

calculated according to the interest rate difference and if the exchange rate depreciates by

less that the interest rate difference the speculator makes a gain. This kind of trade was

common in 1999 and 2000 and the net increase in speculator position was estimated to be

3 The currency band was not credible during the last period of its existence. In this period the ISK became very weak and started to reach its higher bound. At the same time the forward exchange rate (offered by the banks) was outside the band and that would have been an arbitrage opportunity if the band held. If market participant believed the band to hold they would have used this arbitrage opportunity by buying ISK forward but very few did.

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approximately 60 billion ISK during these two years ((Bunadarbanki Islands 2002;

Islandsbanki 2002; Kaupthing 2002; Landsbanki Islands 2002)).

Table 3 Macroeconomic variables - summary statistic

Mean Median Variance Skewness Kurtosis Foreign trade

Import -16.75 -16.99 2.46 -1.56 4.68Export 15.22 14.83 2.56 0.48 -0.17Net -1.53 -1.60 2.87 0.35 -0.53

Interest rate difference RISK-Rforeign 6.65 6.51 0.76 0.43 -0.83

Period: 3 July 2000 – 31 December 2001 (1.5 years) Number of observations: 18 Interest rate difference is 1m inter-bank offer rate (trade weighted)

Table 3 shows that Iceland was a net importer during the period. Balance of

goods was about –7% and –0.5% as a percentage of GDP in the year 2000 and 2001,

respectively. Higher order moments are not very important in this case because the

sample is small and therefore very sensitive to individual observations. Average monthly

interest rate difference was 6.7 percentage points, which is similar to the overnight

interest rate difference.

Empirical Results

This chapter is divided into two subsections. The first is about traditional time

series approach (ARMA and GARCH models) and the second is about order flow and

macroeconomic variables.

Traditional time series approach

The ARMA(p,q) and GARCH(θ,φ) models were chosen after having estimated

several models and evaluated increase in explanatory power by including additional

variables. If the extra variable added little extra explanatory power or had poor

significance it was excluded on the basis of parsimony. Akaike info and Schwarz

criterion along with R2 were used as a basis for valuating different models.

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In addition to the autoregressive and moving average terms, two independent

explanatory variables were tested. Interest rates4 and announcements5 were tested for

order flow and one period ahead variance respectively but were insignificant in both

cases. The results rely therefore only on ARMA and GARCH terms. The reason why

interest rates and announcements do not explain direction of order flow or variability in

order flow is most probably because the market is both young (not fully developed) and

thin.

4 More specifically change in the trade weighted difference in overnight interest rates between Iceland and trading countries. 5 Dummy variable that takes value 1 if there was an announcement and zero otherwise

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Table 4 Time series model results Equation 1

ARMA(p,q) terms Equation 2 GARCH(θ,φ) terms

R2

c p1 q2 χ θ1 θ2 φ1 Customer

Speculators Buy 6.42 108 0.96 -0.77 5.48 1017 0.27 0.30

(1.46) (39.31) (-20.46) (11.33) (3.47)

Sell 0.98 -0.79 7.74 1017 0.14 0.28

(61.47) (-13.11) (8.98) (1.86)

Net -0.16 0.02

(-2.82)

Non-speculators

Buy 8.09 108 0.83 -0.70 0.06

(16.83) (8.23) (-5.87)

Sell -1.73 109 0.99 -0.88 2.22 1017 0.25 0.04

(-0.72) (70.53) (-30.89) (4.8) (3.78)

Net 2.22 1017 0.18 0.23 (4.8) (3.99) (1.9)

Total Buy 1.41 109 0.93 -0.71 8.68 1017 0.23 0.25

(4.48) (28.88) (-12.4) (11.86) (3.81)

Sell -1.27 109 0.97 -0.80 6.55 1017 0.40 0.25

(-3.29) (63.08) (-21.27) (10.28) (4.15)

Net 6.78 107 0.60 -0.72 4.57 1017 0.24 0.03

(2.73) (3.98) (-5.92) (17.71) (4.49)

Inter-bank market Total market turnover 4.29 109 0.56 1.78 1019 0.43 0.42 -0.93 0.32 (8.73) (16.35) (10.79) (5.67) (6.09) (-29.31)

t statistic in parenthesis (Calculated with bollerslev and Woolridge non-normality consistent covariances in the case of GARCH models and Newey West covariances in the case of pure ARMA models)

First moment (ARMA terms)

The first thing to notice by examining the ARMA terms in Table 4 is the

fundamental difference between the time series properties of speculative order flow and

non-speculative order flow. While the model fits rather well to the speculative flow

(implying predictability) the model is almost useless in describing the non-speculative

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order flow (implying no predictability). This is a very interesting difference that can

perhaps be explained economically by lack of liquidity or attempts to affect the exchange

rate. Lack of liquidity makes it impossible to build up big positions in short time without

moving the market. Thus the speculators need a few days to build up positions if they do

not want to affect the exchange rate and that is reflected in the high autocorrelation in

buying and selling order flow. The autocorrelation might also be because the speculators

are trying to create a desperation in the market by putting a lot of sales (buying) pressure

in order to gain from lower (higher) exchange rate. On the other hand, non-speculative

order flow can not be explained by correlation over time and the reason might be that the

motivation for trade is different for every firm and the aggregate numbers are therefore

unpredictable. It is also hard to find any economic reason why there should be time

series pattern in the non-speculative order flow because that appears to be mostly

connected to foreign trade6. Their trades are mainly determined by supply and demand in

markets for goods and service.

The figures are mostly import and export firms. The reason for their trade is

mainly determined by supply and demand in markets for goods and service.

Looking at the total order flow the time series model fits the total buying and

selling reasonably well and that is plausible since aggregated order flow for speculators

exhibits large degree of predictability and aggregated order flow is about 50%/50%

speculators and non-speculators.

Another interesting aspect of the data is the difference between net order flow and

buying and selling order flow. While buying and selling order flow exhibits

predictability in the case of speculators, net order flow is never predictable.

The AR(1) model fits total inter-bank market turnover quite well so turnover can

be described by periods of high turnover and periods of low turnover (see Figure 6 ). In

other words, high turnover today is followed by high turnover tomorrow. This fact can

perhaps partly be explained by the “hot potato” effect. In times of high turnover the same

million USD may travel between market makers until the price adjustment is sufficiently

big for someone to stop passing it to the next market maker. Alternative explanation

6 Foreign trade is approximately 30% of the total market turnover and non-speculative order flow is about 50% of total order flow. This implies that 3/5 of non-speculative order flow is for foreign trade.

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might be that customers use the opportunity to take positions in periods of high turnover

because it is less risky to trade in a liquid market. Turnover might therefore feed it self.

Second moment (Garch terms)

By visually examining the data (see Figure 6 ) one can see volatility clustering,

i.e. some periods are more volatile than others. As mentioned before this can partly be

explained by the “hot potato” effect but in general this is a common feature of return data

i.e. first moment is unpredictable but the second moment is time dependent. This fact can

(to some extent) be captured by parameterizing the volatility structure with GARCH

model. All the AR coefficients of the GARCH model are positive so variance is

positively related through time. High dispersion from mean on any trading day is

followed by high dispersion the following day.

Diagnostics

ARMA modeling relies on the stationary assumption so before doing time series

analysis one has to check whether the time series are stationary. Augmented Dickey

Fuller test was performed and the presence of a unit root was rejected at 1% significance

level for all the time series7. It is therefore safe to conclude that all the time series are

stationary.

Heterosketasticity in the error terms was taken into account by using Newey West

covariance estimator that is consistent in the presence of both heteroskedasticity and

autocorrelation of unknown form in the case of ARMA. Bollerslev and Woolridge

heterosketasticity consistent covariance was used for the GARCH models.

Serial correlation in error terms was tested by estimating the autocorrelation

function and in some of the models the error terms exhibit weak serial correlation i.e.

very few correlation coefficients are significant at the 95% significance level. This is

worth noting but the size of the coefficients does not give rise to serious violation of

independence assumption in the error terms. The p values of the significant coefficients

were only slightly below 0.05.

Test for serial correlation in squared residuals was performed and the coefficients

were almost always insignificant at the 95% level apart from few instances which can be 7 Presence of a unit root was rejected on the 99% significance level with and without an intercept and with 1 to 4 lagged dependent values.

15

interpreted as the 5% chance of getting significant values when the true value is zero

(type I error). Therefore it can be concluded that the GARCH terms capture the main

time series properties of the variance.

Explain order flow with macroeconomic variables

In the light of how high the value of foreign trade in goods is relative to the

foreign currency market turnover in Iceland it is worth testing whether foreign trade can

be used to explain the order flow. As mentioned before it is always challenging to build

statistical models with order flow and macroeconomic data because of the frequency

difference. Many macroeconomic variables are only available on monthly or quarterly

frequency while order flow is on daily or even transaction frequency. This is therefore a

question of compromise and in this case monthly frequency is used giving a total of 18

observations.

Interest rates and stock market indices have sometimes been used as

macroeconomic variables in order flow studies (see for example (Rime 2001)). The main

reason for this is not theoretical but practical (in other words data frequency). The

international finance theory is mainly about figures like GDP, prices and interest rates but

the first two are only available on quarterly and monthly frequency. Interest rates were

tested as an explanatory variable8 in this study but were found to be insignificant. Stock

market indices on the other hand do not have proper theoretical foundations and were not

tested as an explanatory variable in this study.

8 Trade weighted difference in 1m interest rates.

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Table 5 Macroeconomic variable results Equation 3

Explanatory variable β0 β1 β2

Speculators Buy -3.60 1010 3.05 0.31 (-2.63) (2.86) (0.55) Sell 2.67 1010 -3.00 0.28

(2.08) (-3.11) (0.48) Non-speculators

Buy 1.46 1010 -0.09 0.22 (4.69) (-0.38) (0.77) Sell -4.20 109 -0.25 -0.48

(-1.17) (-0.85) (-1.56) Total

Buy -2.2 1010 2.96 0.52 (-1.68) (2.55) (0.71) Sell 2.25 1010 -3.25 -0.20 (1.73) (-2.98) (-0.25)

t statistic in parenthesis (Newey West covariances)

Table 6 Macroeconomic variable results Equation 4

Explanatory variable β3 β4Speculators

Net 5.01 108 -0.24 (0.79) (-0.95)

Non-Speculators

Net 6.94 108 -0.06 (1.2) (-0.37)

Total

Net 1.2 109 -0.30 (2.5) (-1.89)

t statistic in parenthesis (Newey West covariances)

In short, the coefficients are not significant in almost all cases and the t-values for

the significant coefficients are not impressive. The conclusion is therefore that foreign

trade does not explain order flow. At first glance this might seem counterintuitive

because of the high ratio between foreign trade and order flow but there are a few other

things that can explain the unexpected results. First, there are only 18 observations so

even if the relationship holds in the long run it can deviate considerably in the short run

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because of other order flow (order flow that is not based on foreign goods trade) that

distort the picture. Second, payments for foreign trade do not necessarily take place in

the same month as the goods are delivered.

Diagnostics

The macroeconomic approach relies on few data points so it impossible to rely on

asymptotic results. Therefore one of the best ways to check the regression is to visually

inspect it and such inspection does provide further proof for the conclustion that trade

figures do not explain the order flow. There does not appear to be any relationship at all

and the slope in the regression is often consequence of outliers. One outlier has such

great impact that the slope becomes positive or negative.

Conclusion

The research question put forth in this paper was “is it possible to explain order

flow?”. Two approaches were used to answer that question. One approach was based on

ARMA-GARCH model and the second approach used macroeconomic variables as

independent variables to explain the order flow. The study found that it is possible to

explain 28% of speculative buying and selling order flow by using ARMA-GARCH

models but it is impossible to explain non-speculative order flow or net order flow by

using the same approach. Import and export figures and interest rates proved to be

worthless in explaining the order flow. The reason for positive autocorrelation in

speculative buying and selling can be because it takes a few days to build up large

position without moving the market or speculators might gain by trying to move the

market by selling (buying) many days in a row.

Although ARMA terms could account for some of the dynamics of speculative

order flow, almost all of the order flow exhibit volatility clustering that resulted in

positive AR coefficient in the GARCH model. Volatility clustering is a common feature

of financial time series data.

Total market turnover showed considerable autocorrelation and this effect can

perhaps partly be explained by the “hot potato” effect i.e. market makers pass the same

amount of money between each other until the exchange rate adjustment is sufficient big

for somebody to keep the money, i.e. stop passing it to the next market maker. Liquidity

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might also feed it self in the sense that high turnover stimulates more trading because it is

less risky to trade in a liquid market.

General characteristics of the order flow series was also analyzed and there seems

to be a fundamental difference in speculative order flow and non-speculative order flow.

The speculative order flow has lower absolute mean and fatter tails (see Figure 2 and

Figure 3 ). This fact is reflected in the predictability of speculative order flow and lack of

predictability in the non-speculative order flow.

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Appendix It is often useful to look at visual representation to get a better feeling for the data.

The following three graphs show the estimated densities for the order flow series. The

densities are estimated with normal kernel function and the bandwidth (governing the

degree of smoothness of the estimate, with larger values producing a smoother density

estimate) chosen according to Silverman (1986).

0,00

0,20

0,40

0,60

0,80

1,00

1,20

1,40

0 2 4 6 8Billion ISK

Freq

uenc

y

SpeculatorsNon-speculators

Figure 2 Distribution of customer buying order flow

20

0,00

0,20

0,40

0,60

0,80

1,00

1,20

1,40

0 1 2 3 4 5

Billion ISK

Freq

uenc

y

SpeculatorsNon-speculators

Figure 3 Distribution of customer selling order flow

0,00

0,20

0,40

0,60

0,80

1,00

1,20

-4 -2 0 2 4 6

Billion ISK

Freq

uenc

y

SpeculatorsNon-speculators

Figure 4 Distribution of customer net order flow

21

0,00

0,02

0,04

0,06

0,08

0,10

0,12

0,14

0,16

0,18

-4

-3,4

-2,8

-2,2

-1,6 -1

-0,4 0,2

0,8

1,4 2

2,6

3,2

3,8

Billion ISK

Freq

uenc

y

EmpiricalNormal

Figure 5 Distribution of customer total net order flow compared to normal

0

10

20

30

40

50

60

3.7.

2000

3.9.

2000

3.11

.200

0

3.1.

2001

3.3.

2001

3.5.

2001

3.7.

2001

3.9.

2001

3.11

.200

1

Billi

on IS

K

Turnover

Gross order flow

Figure 6 Inter-bank market turnover and gross order flow

(Meese 1983a; Meese 1983b; BIS 2002) (Central Bank of Iceland 2002a) (Chen 2001)

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References BIS, Bank for International Settlement (2002). Triennial Central Bank Survey of Foreign Exchange and Derivatives Market Activity 2001 - Final Results. Basel, Bank for International Settlements. Bunadarbanki Islands (2002). Annual Report 2001. Reykjavik, Iceland, Bunadarbanki Islands. Central Bank of Iceland (2002a). Annual Report 2001. Reykjavik, Central Bank of Iceland. Central Bank of Iceland, S. D. (2002b). Turnover in the inter-bank market for foreign exchange. Reykjavik, Central Bank of Iceland. Chen, Y. and Rogoff, K. (2001). "Commodity Currencies and Empirical Exchange Rate Equations." A paper for the September 28-29 Confrence on Empirical Exchange Rate Models in Madison Wisconsin. Evans, M. D. and Lyons, R.K. (2001). "Order Flow and Exchange Rate Dynamics." Draft: 19. January 2001. Evans, M. D. and Lyons, R.K. (2001). "Why Order Flow Explains Exchange Rates." Frankel, J. A. and Rose, A.K. (1995). Empirical Research on Nominal Exchange Rates. Handbook of International Economics, Elsevier Science. III: 1689-1729. Islandsbanki (2002). Annual Report 2001. Reykjavik Iceland, Islandsbanki. Kaupthing (2002). Annual Report 2001. Reykjavik, Iceland, Kaupthing. Landsbanki Islands (2002). Annual Report 2001. Reykjavik, Iceland, Landsbanki Islands. Lions, R. K. (1995). "Tests of Microstructural Hypothesis in the Foreign Exchange Market." Journal of Financial Economics(39): 321-51. Már Guðmundsson, Thórarinn G. Pétursson and Arnór Sighvatsson (2000). Optimar Exchange Rate Policy: The Case of Iceland. Working paper no. 8. Reykjavik, Central Bank of Iceland, Economics Department. Meese, R. A. and Rogoff, K. (1983a). "Empirical Exchange Rate Models of the Seventies." Journal of International Economics(14): 3-24. Meese, R. A. and Rogoff, K. (1983b). The Out-of-Sample Failure of Empirical Exchange Rate Models. Exchange Rate and International Macroeconomics. J. Frankel. Chicago, University of Chigaco Press. 14.

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Rime, D. (2001). "U.S. Exchange Rates and Currency Flows." PhD thesis at University of Stockholm.

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