Volume, Order Flow and the FX market: Does it matter who you are?∗ Geir H. Bjønnes† Dagfinn Rime‡ Haakon O.Aa. Solheim§ February 2002 Comments welcome Preliminary and incomplete. Do not quote

Abstract We study the impact of order flow and volume using an unique data set of daily trading in the Swedish krona (SEK) market. The data set covers 95 per cent of worldwide SEK-trading, and is disaggregated on 13 reporting banks’ buying and selling with seven different counterparties in five different instruments. The preliminary sample covers the first six months of 1998, while the whole data set, which we will receive during the spring 2002, begins in 1993. A unique feature in our data is the ability to differentiate between counterparties. We focus on three relationships: Between (i) volume and volatility, (ii) order flow and exchange rate changes and (iii) the dynamics of liquidity to the market. We find that while we can restate previously reported findings from the literature, the findings depend on the identity of the counterparty. We find that the volume of the presumably least informed counterpart contributes positively to volatility, indicating a noise-trader role for this group. Furthermore, customers’ order flow contribute significantly to exchange rate changes, supporting the importance of these flows previously stated in ∗

We thank Antti Koivisto for helpful discussions and assistance with collecting the data

set. †

Stockholm Institute for Financial Research, email: [email protected]. Norges Bank (Central Bank of Norway) and Stockholm Institute for Financial Research. email: [email protected]. § Corresponding author. Norwegian School of Management (BI). email: [email protected]. ‡

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the literature. Finally, we find that customer order flow is a function of past returns in the equity markets, changes in the interest rate differential and exchange rate movements. Again this process will differ between different groups of counterparties, indicating the importance of heterogeneity. Keywords: Order flow analysis, volume volatility relation, microstructure, exchange rates JEL Classification: F31

1

Introduction

This paper revisits three previously studied topics in the financial literature; the relationship between volume and volatility, between order flow and exchange rate changes and the topic of liquidity provision. In the FX-market such research has until recently been difficult due to the lack of good trading data. In this paper we use a unique data set provided by Sveriges Riksbank (the Swedish central bank). The data is based on daily reporting from 13 primary dealers. Each primary dealer reports total purchases and sales with six categories of counterparties: (1) Swedish primary dealer, (2) foreign primary dealer, (3) Swedish bank, (4) foreign bank, (5) Swedish customer, and (6) foreign customer. For each counterparty-category total purchases and sales are split into different instruments, (i) spot, (ii) outright forwards, (iii) short swaps (tomorrow-next), (iv) FX swaps, and (v) options.1 The data covers as much as 95 per cent of all currency trading in Swedish kroner. This paper makes several contributions. First, we use transaction volume data which covers almost the entire market for Swedish kroner to examine the relationship between volatility and transaction volume. This is an important contribution considering the lack of transaction volume data from other currency markets. As in many other studies from different market settings (see Karpoff, 1987), we find a positive relationship between volatility and volume. The results are also consistent with Galati (2000) who finds a positive relationship between volatility and volume for five of the seven currencies measured against dollar. Compared with the currency markets studied by Galati (2000), the Swedish currency market is larger. Second, with our detailed data we can for the first time study the underlying source of the relationship between volume and volatility in the foreign exchange market. In particular, we examine the importance of heterogeneity in explaining volatility. This is possible since total transaction volume can be split into different categories of counterparties. Studies from other 1

A short swap is a contract to be delivered within two days, e.g. before a spot contract.

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market settings suggest that heterogeneity among the market players may be important in understanding volatility (see e.g. Grinblatt and Keloharju, 2001). In asymmetric information models (e.g. Kyle, 1985; Admati and Pfleiderer, 1988) more trade from informed investors increase volatility because of the generation of private information. On the other hand, Shalen (1993) argue that uninformed traders increase volatility because they cannot differentiate liquidity demand from fundamental value change. Daigler and Wiley (1999), studying futures markets, find that trade of presumable less informed investors tend to be more correlated with volatility than trade of more informed investors. Our results point in the same direction, as we we find a positive correlation between volume from customers and volatility. Third, we use our data to explain movements in foreign exchange rates. In particular, we study the effects of customer trades. Recent literature by Evans and Lyons (2002) and Fan and Lyons (2000) suggest that orders from customers are important in explaining movements in foreign exchange rates because customer orders are the ultimate driver of all interdealer trading. We are able to test for the relationship between order flow and the exchange rate differentiating between customer order flow, interbank flows and central bank interventions. We find that customer order flow2 is much more important than interbank flows for the determination of exchange rates. Fourth, we test for exogeneity between order flow and the exchange rate. Killeen, Lyons and Moore (2001) argue that order flow shall be both weakly and strongly exogenous with regard to a flexible exchange rate. They find that order flow is strongly exogenous before the EMS exchange rates were fixed, and endogenous after the rates were fixed. However, we do not find that order flow is strongly exogenous with regard to the exchange rate in our sample. Rather the exchange rate seems to Granger cause changes in order flow. Last, we will look at the dynamics of liquidity provisions. Chordia, Sarkar and Subrahmanyam (2001) investigate the determinants of bond and stock market liquidity. They find that volume changes in bond and stock markets “are predictable to a considerable degree using lagged market returns, lagged spreads, and lagged volume.”(Chordia et al., 2001). However, little theoretical work has been done on movements in liquidity. We identify determinants of order flow. We find that customer order flow does depend on returns in the exchange market, as well as past returns in the Swedish equity markets and changes in interest differentials. However, the determinants of order flows differ between groups. This confirms our 2

Notice that our definition of customer order flow contain trades made by customers as well as by non-reporting banks with reporting banks.

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Figure 1: Swedish and German Interest rates and the SEK/EUR exchange rate 1.3

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indication that foreigners and locals, customers and interbank markets are different. The paper is organized as follows. Section 2 gives a detailed presentation of our data. In Section 3 we derive testable hypothesis and present the results. Section 4 concludes.

2

Data

The sample used is daily observations for the first six months of 1998, making a total of 129 observations.3 According to the market survey conducted by BIS in 1998 this period was calm. Our dependent variable is the SEK/DEM exchange rate.4 In the order flow regressions we use return (∆ log SEK/DEM), while in the volatility regressions we use the absolute value of return. We focus on SEK/DEM since this covers close to 100 of all interbank trading and 80–90% of customer trading. We use the 3 month money market interest rate from Sweden and Germany. These series, together with the interest rate differential, is showed in figure 1. The figure confirms that this was a calm period. Interest rates are very stable throughout (the exception being a reduction of the Swedish repo rate by 25 points June 10), with a mean interest rate differential around 100 3

The whole data set on transaction flow starts in 1993 and run up to present. We will receive all the data during the spring. 4 Note however, that as the standard is changing to EUR, the exchange rate is indexed to the EUR equivalent terms (SEK/DEM*1.95583).

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points and a standard deviation of 14 point. The exchange rate is similarly stable, with only 4.6% between top and bottom. Mean return is 0.004% with 0.3 standard deviation and maximum return being ca. 1%. Although the fall of 1998 was very turbulent in financial markets we are apparently not picking up anything from that period in this sample. This is assuring as we do not want to pick extreme events in the current analysis. The remainder here will be devoted to the description of the transaction flows since they represent the most novel part of this paper. The data set is provided to us from the Sveriges Riksbank (Central Bank of Sweden), and will eventually contain daily observations starting in 1993 until today. The data set is extremely detailed. The Riksbank receives daily reports from 13 Swedish and foreign banks on their buying and selling of five different instruments against seven different counterparts. The reported series is an aggregate of Swedish krona trading against all other currencies, measured in krona, and covers 90–95 of all worldwide trading in SEK exchange rates. As mentioned above, the vast majority of the flows is SEK/DEM transactions. The 13 reporting banks are anonymized. We know however that there are five Swedish banks, five foreign banks, and three branches of foreign banks located in Sweden. The reporters are the main liquidity providers in the SEK-market. The five instruments are spot, forward, options, short swaps (tomorrow/next) and long swaps. In this paper we focus on the spot and forward flows. Particularly important is the counterparty information. The seven counterparties are Swedish and foreign customers, reporting banks, and other banks, and in addition the Riksbank. This enables us to distinguish customer order flow from interbank trading between the liquidity providers and interbank trading with more speculative banks. This is to the best of our knowledge an unique feature of the present data set. Figure 2 shows the net selling of foreign currency to different counterparts by the reporting banks in the spot market. A positive number indicates that the counterpart group has accumulated currency from the reporters. We immediately see that Swedish customers and other banks are accumulating currency in the spot market (right panel). This is in line with the current account surplus of Sweden. Figure 3 shows the net purchase of foreign currency spot and forward. These data are unique in several aspects. (i) The series are longer and/or of higher frequency than in many previously available data sets.5 Evans and Lyons (2002) have daily observations from the interbank market but only for 5

The whole data set is long (nine years). The present sample however is of similar length as Evans and Lyons (2002).

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79 days. Wei and Kim (1997), Cai, Cheung, Lee and Melvin (2001) and Rime (2001) have long series, but only at weekly frequency. Fan and Lyons (see Lyons, 2001) also have long series, but only for the monthly frequency. (ii) We have the complete trading of several banks. The weekly series mentioned above aggregate over the banks. (iii) Detailed counterparty information. The data in Evans and Lyons (2002) is only interbank flow, but do not differentiate between groups of banks, while the data in Wei and Kim (1997), Cai et al. (2001) lump all flows, customers and interbank, together. The data in Rime (2001) differentiate between local customers and foreigners which primarily are banks. Information on customer flow is important because this is the basic source of demand. Fan and Lyons can distinguish between financial and non-financial customers, but have only series for one bank. On the other hand, Evans and Lyons and Cai et al. have observations on the main exchange rates.

3

Results

In this section we present some results. One should have in mind that these estimations are only conducted on a set covering 129 trading days of data. A longer set is necessary to confirm the trends reported below. However, this is a problem experienced in much of the microstructure literature. The large number of variables that can be extracted from our set do however make for a rather complex system of variable names. To assure that all tables can be understood properly table 1 provides variable names and explanations. In the first section we discuss the relationship between volume and volatility. In the second section we discuss contemporaneous correlation between order flow and returns. In the last section we look at the dynamics of order flow and test for Granger causality between order flow and exchange rates. Notice that all estimations are done using daily observations.

3.1

Volume versus volatility

How to measure volatility is not given. As mentioned above, volatility is low in the period under investigation. We find no evidence of an ARCH process in in the exchange rate over this sample period. The optimal way to measure volatility in a sample of this kind would probably be to take the difference between highest and lowest observed value intra day. However, for some reason such series do not seem to be available

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8

Variable lSEK returns absreturns rdif3 rdif10 lOMX OFLGGL OFSGGL OFFGGL OFLRGL OFSSGL d() LLSLL LLSLLU LLSLLE SGSLLU FGSLLU LRSLLU SSSLLU D1, D2, D3, D4

Table 1: Variable explanations Explanation The log of the price of ECU denominated in SEK d(lSEK) absd(lSEK) Swedish 3 month interbank rate - German 3 month interbank Swedish 10 year gov. bonds - German 10 years gov. bonds Log of the OMX equity index (Stockholm exchange) Aggregate order flow all customers and non-reporting banks, spot and forward Agg. order flow from Swedish customers and non-reporting banks in the spot and forward market Agg. order flow from foreign customers and non-reporting banks in the spot and forward market Aggregate order flow in the interbank market, spot and forward Aggregate order flow, Sveriges Riksbank First differential Total volume transacted in the spot market Unexpected volume Expected volume Total unexp. volume from Swedish customers and other banks in the spot market Total unexp. volume from foreign customers and other banks in the spot market Total unexp. volume, interbank, spot market Total unepx. volume, Riksbanken, spot market Dummies for Monday, Tuesday, Wednesday and Thursday respectively

in the SEK/DEM currency cross, only in SEK/USD. We choose to measure volatility as the absolute value of return from close to close.6 Table 2 report the first four estimations on volatility. In estimation (1) we explain volatility using a number of “fundamental variables”—e.g. absolute change in the stock market index and short term interest rates, and with daily dummies. As can be seen, the explanatory power is weak. In estimation (2) we add the change in total volume in the spot market, d(LLSLL). In line with a number of previous papers we find a significant relationship between volume and volatility. Hartmann (1999) report that “unexpected” flows increase spreads in the exchange rate market, while expected flows reduce spreads. We differentiate between expected and unexpected flows by using an AR(5) model. We estimate an AR(5) for each variable, and use the fitted values as expected flows, and the residuals as the unexpected flows. As can be seen from estimation (3) unexpected flows seems to drive the volume-volatility relationship. Expected flows have no significant relationship with volatility. Estimation (4) is conducted using only unexpected flows. As pointed out, an interesting part of our data is the ability to differentiate between counterparties. Daigler and Wiley (1999) are able to distinguish between counterparties on the basis of their proximity to the market. They find that the volume from customers far from the market seems to increase volatility more than volume from presumably more informed traders. In our sample we can distinguish between the Riksbank, reporting currency banks, other banks and customers. We choose to pool customers and other banks in two distinct groups: Swedish and foreign. We pool all inter-reporter trade in one group. “Reporters” comprise the main large banks trading in SEK. One should therefore expect reporters to be well informed about the SEK-market. Likewise, the Riksbank should be expected to have superior knowledge of this market. The least informed counterparties are expected to be customers and other banks. In table 3 we report a number of estimation where we differentiate between counterparties. In estimation (5) we include all the four groups. We only include unexpected flows, as expected flows have little or no power. We find that only the volume of foreign customers seem to have significant relationship with volatility. One should however remark that our results are not clear cut. The sign for both reporters and the Riksbank is also positive, indicating a positive relationship between volume and volatility. Only 6

The results for high-low in SEK/USD do not differ significantly from those reported below. However, R2 is somewhat lower.

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Note3: Coefficient sizes: All returns are measured such that a return of 1 per cent is 0.01. Volume is measured in million SEK.

Note1: Estimated with OLS Note2: * - 5 per cent, ** - 1 per cent

Table 2: Estimating |returns| (a)—01.01.1998 to 06.30.1998 |returns| (1) t-stat (2) t-stat (3) t-stat (4) t-stat Constant 0.0021 4.83 ** 0.0004 0.76 0.0020 1.49 0.0024 14.82 Market spot volume, D(LLSLL) 0.0000 3.89 ** Unexpected volume, D(LLSLLU ) 7.69E-08 4.34 ** 7.30E-08 4.35 Expected volume, D(LLSLLE) 4.88E-09 0.10 Monday dummy, D1 -0.0001 -0.17 0.0004 0.82 0.0003 0.60 Tuesday dummy, D2 0.0000 0.05 0.0001 0.18 -0.0001 -0.20 Wednesday dummy, D3 -0.0002 -0.32 0.0000 -0.02 -0.0001 -0.22 Thursday dummy, D4 0.0001 0.14 0.0001 0.16 0.0000 0.03 Abs. stock index return, |d(lOM XC)| 0.0432 1.96 0.0263 1.24 0.0348 1.54 Abs. interest differential, |d(rdif )| -0.0028 -0.30 -0.0064 -0.72 -0.0053 -0.57 Adjusted R2 -0.02 0.09 0.11 0.13 DW 2.03 2.11 2.10 2.00 S.E. of regression 0.0019 0.0018 0.0018 0.0018

**

**

the sign for Swedish customers and other banks is negative. We probably need a longer data set to be able to confirm whether information (or lack of information) is really the driving force in the volume-volatility relationship.

3.2

Order flow versus returns

Order flow is here defined as change in net position over the day. We define order flow in such a manner that a negative order flow indicates that the counterparty sell foreign currency, and purchases SEK. According to standard microstructure theory we should expect a positive correlation between order flow and the value of foreign currency denominated in SEK. One should point out that change in net position is not enough to really establish that a flow is an ‘order flow’. Lyons (2001) define an order flow as a signed transaction. To identify order flow we need to know who initiates the trade. That information is not contained in our data. However, all trades between customers and reporters or between other banks and reporters are initiated outside the reporting bank. To our knowledge it is also reasonable to expect that trades between customers and other banks and reporters are initiated outside the reporting bank. We will therefore make the assumption that daily change in the net position of customers and other banks can be considered as daily order flow. Change in net positions between reporters is an interbank flow. It is not given whom initiates these trades. Also trades conducted by Sveriges Riksbank is problematic seen from the order flow perspective. Rightly, Sweden does in this period have a floating exchange rate. The money supply should therefore be exogenous, and trades by the Riksbank should be expected to be initiated by the Riksbank. However, it is not clear that the Riksbank does not take e.g. liquidity considerations when doing transactions in the currency market. Indeed we find a negative correlation between the change in the Riksbank’s net positions and returns in the exchange rate market. Table 4 reports a number of estimations on returns. In estimation (1) we regress returns on the change in the three month interest rate differential to German interest rates and the change in the the stock exchange. We find a significant relationship between returns in the exchange rate and returns in the stock market, indicating that the SEK strengthen when the stock market rise. However, the explanatory power of this model is low. In estimation (2) we include aggregate customer order flow—i.e. the change in net positions over all customers and other banks in the spot and forward markets.7 We find that this variable, as expected, is significant and 7

As it seems like the participants in our data set do manage spot and forward together,

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12 Note1: Estimated with OLS Note2: * - 5 per cent, ** - 1 per cent

Table 3: Estimating |returns| (b)—01.01.1998 to 06.30.1998 |returns| (1) t-stat (5) t-stat (6) t-stat Constant 0.002 4.825 ** 0.002 4.977 ** 0.002 15.209 Unexp., spot, SGp, D(SGSLLU ) -1.42E-08 -0.241 -7.97E-09 -0.142 Unexp., spot, FG, D(F GSLLU ) 1.39E-07 3.946 ** 1.31E-07 3.803 Unexp., spot, rep., D(LRSLLU ) 8.23E-08 1.114 7.58E-08 1.040 Unexp., spot, Riksb., D(SSSLLU ) 1.02E-06 1.176 1.01E-06 1.175 Monday dummy, D1 0.000 -0.167 0.000 0.860 Tuesday dummy, D2 0.000 0.050 0.000 0.124 Wednesday dummy, D3 0.000 -0.318 0.000 0.098 Thursday dummy, D4 0.000 0.137 0.000 0.003 Abs. stock index ret., |d(lOM XC)| 0.043 1.963 0.035 1.675 Abs. interest dif., |d(rdif )| -0.003 -0.299 -0.005 -0.584 2 Adjusted R -0.02 0.17 0.17 DW 2.03 2.01 1.92 S.E. of regression 0.002 0.002 0.002

0.17 1.89 0.002

5.134

t-stat 0.002 15.208 ** 1.52E-07

**

(7)

**

**

positive. The size of the parameter might be difficult to interpret. However, notice that returns is measured in change in logarithms of the exchange rate. Flows are measured in millions of SEK. Evans and Lyons (2002) report an order flow effect of 0.5 per cent in the DEM/USD market for a trade of 1 billion USD. A trade of 1 billion USD would equal approximately a trade of 8000 million SEK. That would give an effect of 0.006 on the exchange rate, or in other terms, an effect of 0.6 per cent. This is close to the findings of Evans and Lyons. In estimation (3) we differentiate between counterparties. Interestingly, we find that Swedish and foreign customers and other banks behave as expected: the parameter is significant and the sign is positive.8 Inter-reporter flows are not significant. Order flow from the Riksbank is significant, but with a negative sign. This might indicate that we do not interpret the order flow definition properly with regard to the central bank. The lack of effect from interbank trade may also be due to noise introduced through our definition of order flow. A potential problem in estimations (1-3) is the question whether or not order flows ar endogenous with regard to the exchange rate. The microstructure theory clearly predicts that order flow should be exogenous. However, as the results in the next section will show, this is not clear in our data. One possibility is that order flow is partly a function of feedback trading. This could affect our results significantly. To control for feedback trading we include 5 lags of the return in the exchange rate in estimation (4). Only one of the five lags is significant—lag 4. However, as can be seen when comparing estimation (3) and (4), although R2 rises considerably, the results for the order flow variables does not change noticeably.

3.3

The dynamics of order flow

On volume vs. volatility there is a vaste theoretical literature. On order flow vs. returns there is a growing literature. On the dynamics of order flow little has been done in the theoretical area. Some hypotheses have been put forward. Killeen et al. (2001) argue that under a flexible exchange rate one should expect order flow and exchange rates to be (i) cointegrated, (ii) the order flow should be weakly exogenous with regard to the exchange rate, and (iii) the order flow should be strongly we find it reasonable to include both spot and forward positions when calculating order flow. 8 Notice that the size of the order flow parameter for these two groups is no in fact exactly 0.5 per cent per 1 billion USD equivalent.

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2.979 2.951 -0.637 -3.026 -0.507 -2.695

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2.880 3.442 -0.594 -3.194 -0.035 -2.689 2.445

t-stat 0.001 2.798 6.24E-07 7.06E-07 -1.76E-07 ** -3.78E-06 0.000 ** -0.053 0.195 0.30 2.15 0.001

** **

**

: We include 5 lagged values of return in this regression. Only the fourth lag is significant.

Note1: Estimated with OLS Note2: * - 5 per cent, ** - 1 per cent

Table 4: Estimating returns—01.01.1998 to 06.30.1998 returns (1) t-stat (2) t-stat (3) Constant 0.000 0.576 0.000 0.984 0.001 Tot. customer OF, D(OF LGGL) 7.72E-07 3.987 ** Swe. customer OF, D(OF SGGL) 6.61E-07 For. customer OF, D(OF F GGL) 6.11E-07 Reporter OF, D(OF LRGL) -1.94E-07 Reserves, D(OF SSGL) -3.63E-06 Interest dif., d(rdif ) 0.002 0.173 -0.002 -0.151 -0.006 Stock index return, d(lOM XC) -0.054 -2.472 * -0.051 -2.437 * -0.055 Lagged return (4) Adjusted R2 0.03 0.14 0.18 DW 2.09 2.02 2.05 S.E. of regression 0.003 0.003 0.003

** *

**

** **

**

Table 5: Estimating Granger causality—01.01.1998 to 06.30.1998 d(OFSGGL) does not Granger cause d(lSEK) F-stat 0.70 0.62 d(OFFGGL) does not Granger cause d(lSEK) F-stat 1.00 0.42 d(OFLRGGL) does not Granger cause d(lSEK) F-stat 1.20 0.32 d(lSEK) does not Granger cause d(OFSGGL) F-stat 2.56 0.03 * d(lSEK) does not Granger cause d(OFFGGL) F-stat 3.91 0.00 ** d(lSEK) does not Granger cause d(OFLRGGL) F-stat 1.55 0.18 Note1: Estimated with bivariate VAR, 5 lags. Probability levels in the rightmost column. Note2: * - 5 per cent, ** - 1 per cent

exogenous with regard to the exchange rate. Strong exogeneity is defined by Killeen et al. (2001) as the observation that order flow should Granger cause the exchange rate. The sample is to short to conduct satisfactory cointegration analysis.9 However, we believe that we can with some degree of reason do a Granger causality test. The results are reported in table 5. Evidence seems to be that one can not reject that there is no relationship from order flows to the exchange rate. This is the case for order flows from Swedish customers and other banks, for foreign customers and other banks, and for changes in net positions of reporters. However, one can reject a hypothesis of no relationship from the exchange rate to order flow for Swedish and foreign customers and other banks. This indicates that order flow in this sample seems to react to past returns, not the other way around. The Granger causality tests indicate that past returns in the currency market might be one explanatory variable for order flow. Chordia et al. (2001) find a relationship between the volume and past returns in the stock and bond markets. To investigate this issue closer we conduct a process of econometric modelling. 10 We use the general to specific modelling approach, and begin with a model containing the same period and five lags of returns in (i) the exchange rate and (ii) the Swedish stock index (OMX), (iii) the change in the three month and (iv) 10 year interest differential to Germany (the last indicating uncertainty about future inflation differentials), and order flows for the the four groups (v) Swedish customers and other banks, (vi) foreign customers and other banks, (vii) net changes in positions between reporters and (viii) net changes in reserves (measured as net changes in the positions of the Riksbank). We also include dummies for different week-days. We then eliminate variables have no explanatory power, and test modelling progress 9

Some preliminary results do indicate the presence of a stable cointegration vector. However, it seems like the exchange rate is weakly exogenous, not the order flow. 10 Since there is little theory in this area, we feel that this approach is justified.

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using F-tests. We report only final modelling results. The results for order flow from Swedish customers and other banks is reported in table 6. We see that our model has reasonable explanatory power—the R2 is above 0.5. Order flow from Swedish customers and other banks seems to depend on simultaneous and past flows, as could be expected. However, order flow also seems to depend on past returns in the exchange rate, past returns in the OMX and past interest rate changes. To summarise our findings: • As expected, a same day depreciation leads to flow out of SEK. However a depreciation leads to purchases of SEK two days hence. Assuming that this group contains a number of importers and exporters, this seem reasonable. • A change in the short term interest rate on the same day the previous week tends to affect flows. • Past returns on the Stockholm stock exchange is positively correlated with the purchase of SEK (remember that a purchase of SEK is a negative number). • An increase in the 10 year interest differential to Germany seems to make Swedish customers and other banks depart from SEK. This might indicate that an increase in the long term spread indicates increased uncertainty about conditions in Sweden. The results for the order flow of Swedish customers and other banks become even more interesting when compared with similar results for foreign customers and other banks, as reported in table 7. As can be seen, neither short-term interest rates nor the stock market index is contained in this regression. In fact the only lagged macro variable included is the long-term interest rate differential. On the other hand, daily dummies are important here. It seems like foreign flows depend on an other set of variables than local flows. With regard to inter-reporter flows, these do not depend on macro variables at all. However, one should notice that our ability to explain these flows are much less than our ability to explain customer flows. It is a well known from the banking literature that the amount of interbank flows are very difficult to justify in standard models. We must conclude that it is satisfying to find that macro variables do not explain changes in net positions between reporting banks. However, our findings in this area provides even more indications of the need for a good general equilibrium model of the interbank market. 16

Table 6: Estimating d(OF SGGL)—01.01.1998 t-stat Constant 270.43 1.85 d(OF SGGL)3 0.13 2.01 d(OF F GGL) -0.63 -10.82 d(OF F GGL)4 -0.11 -1.88 d(OF LRGL) -0.35 -3.05 d(OF LRGL)1 0.23 1.95 d(OF SSGL)5 -1.11 -2.44 d(lSEK) 1023.20 3.27 d(lSEK)2 -907.48 -2.85 d(rdif 3)5 -126.03 -2.75 d(lOM X)2 -206.16 -2.37 d(lOM X)3 -223.92 -2.55 d(lOM X)5 -153.50 -1.92 d(rdif 10) 9739.16 2.78 d(rdif 10)1 4957.39 1.49 d(rdif 10)3 -7323.82 -2.29 D2 -268.04 -1.10 Adjusted R2 0.61 AR 1-4 test 0.367 (0.83) ARCH 1-4 test 0.370 (0.83)

to 06.30.1998

* ** ** * ** ** ** * * ** *

Note1: Estimated with OLS Note2: * - 5 per cent, ** - 1 per cent

Table 7: Estimating d(OF F GGL)—01.01.1998 t-stat Constant 372.1383 2.229 d(OF SGGL) -0.72631 -10.615 d(lSEK) 1236.07 3.575 d(rdif 10)1 8208.599 2.364 d(rdif 10)4 -7736.53 -2.309 D2 -884.934 -3.063 D3 -842.137 -2.837 D4 -839.289 -2.873 2 Adjusted R 0.55 AR 1-4 test 0.973 (0.43) ARCH 1-4 test 0.588 (0.67) Note1: Estimated with OLS Note2: * - 5 per cent, ** - 1 per cent

17

to 06.30.1998 * ** ** * * ** ** **

Table 8: Estimating d(OF LRGL)—01.01.1998 to 06.30.1998 t-stat d(OF SGGL) -0.16 -3.41 ** d(OF LRGL)1 0.31 3.58 ** 2 Adjusted R 0.15 AR 1-4 test 1.100 (0.36) ARCH 1-4 test 0.918 (0.46) Note1: Estimated with OLS Note2: * - 5 per cent, ** - 1 per cent

4

Conclusions

Our starting point was: does it matter for the FX markets who you are? The answer is that different counterparties clearly seems to affect the markets differently. This opens a wide field of possible research questions. We have used this paper to investigate a number of different questions related to microstructure theory. As pointed out on several occasions above, our results should at this point be interpreted with care. However, we believe our results illustrate the potential in this kind of data. Such data can both be used to test the number of existing (and in part competing) theories that exists in the microstructure literature. It can also be used to point out interesting avenues for new theoretical research.

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