Another relationship that has received much attention in the FX market is the one between trading volume and bid-ask spreads (implied by both inventory costs and asymmetric information models). However, due to the lack of good data on foreign exchange trading volumes at high frequencies, few studies have been focusing on the FX market. Therefore, empirical work on foreign exchange markets suffers from the fact that different data sources have been used to describe the time series behaviour of trading volume and all of these data sets have drawbacks.
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Measures of trading volume in the FX market
A number of studies [Grammatikos and Saunders (1986), Batten and Bhar (1993), Bessembinder (1994) and Jorion (1996)] have used data on futures contracts as a proxy for interbank trading volumes. A drawback of these data sets is that trading activity in futures is very small compared to OTC volumes (Dumas,1996). Therefore, the behaviour of spot and futures market may be different although there is some positive correlation between the two. Moreover, as Dumas (1996) points out, the choice of futures volume for an organized market to measure total volumes of a market working mostly over the counter may also induce an omitted-variable problem in the estimations. Finally, Hartmann (1999) argues that another source of possible biases in parameter estimates, when using futures volume, may be that the endogeneity of unpredictable turnover, measuring the rate of information arrival, is disregarded.
An alternative measure of trading volume is taken from the Bank of Japan, a data set on brokered transactions in Tokyo yen/dollar market, which has been used by Wei (1994) and Hartmann (1999). An obvious drawback of these data sets is the limited representation it provides for the total turnover in the global yen/dollar market.
Another group of studies used the frequency of quote arrival posted by Reuters on its FXFX page as a proxy for trading volume. This approach was used in studies such as the ones of Goodhart and Figliuoli (1991) and Bollerslev and Domovitz (1993). There are many limitations using the frequency of quote arrival as proxy of trading volume. First, of all these quotes are indicative quotes and not actual trades and moreover it is not possible to infer from a quote for which volume it is given. In addition, Reuters tick frequency may be low at times of high trading activity and high at times of low trading activity. This is due to the fact that banks act as data providers to programme an automated data input. When an
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important event occurs, traders are likely to act and trade actively rather than entering data for Reuters.
A data set that is free from the above limitations is the one for actual transactions in the OTC market. Lyons (1995) and Goodhart et al (1996) used such data (transactions in a week in 1992 and one day in 1993 respectively) but the limitation here is that these data sets cover only a limited segment of the foreign exchange markets and span a very short time period.
A recent paper by Galati (2000) uses a data set with high-frequency data on trading volumes for seven currencies from emerging market countries that are representative of foreign exchange market. As I mentioned above there is relatively little work on trading volume and spreads in foreign exchange markets due to the difficulty of obtaining data. Empirical research shows that trading volumes are highly autocorrelated, implying that volumes can be forecasted to a substantial degree. Therefore, trading volumes can have a different impact on spreads depending on whether they are expected or unexpected. There should be a negative relationship between spreads and expected trading volume (forecastable trading volume), because with higher expected trading volume, spreads should narrow to reflect economies of scale in market making (due to order processing costs) and higher competition among market-makers [Cornell (1978)]. Easley and O’Hara (1992) developed a model that implies spreads decrease with forecastable trading volume. By contrast, unexpected trading volumes (unforecastable trading volume) should have a positive impact on spreads to reflect the arrival of news (risk due to information asymmetry). In his study, Glassaman (1987) shows that the proxy of trading volume does not have the expected relationship with spreads. Jorion (1996) confirms the positive and significant correlation between volatility, volume and spread using currency futures data from the CME and option implied standard deviation (ISD) as a proxy of volatility.
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An important study by Bessembinder (1994) showed that the unpredictable component of volatility is measured by ARIMA model. Bessembinder (1994) examines the relationship between trading volumes and spreads. Coefficient estimates on the expected and unexpected components of futures trading volume (as a proxy for trading volume in the interbank foreign exchange market) support the proposition that expected and unexpected trading volumes have heterogeneous effects on bid-ask spreads. His data set consists of daily spot and six-month forward quotations (BP, SF, DM, JY against USD) as of the close of London trading, from January 1979 to December 1992. Hartmann (1999) applies a data set of brokered transactions in Tokyo of daily spot FX volumes for the period from December 1986 to January 1995 for the USD/JY exchange rate to examine the relationship between trading volume and bid-ask spreads. His results confirm those found by Bessembinder (1994). Predictable Dollar/Jen spot volume is negatively linked to spot spreads, while unpredictable volume is positively linked to spreads. However, his results are much more significant than Bessembinder’s at the 5% level.
Goodhart and Payne (1996) in their paper Microstructural dynamics in a foreign exchange electronic broking system examine the determinants of quote revisions and spreads and find that trades are a major factor in determining quotes and spreads. When transaction volume is high and the possibility of informed trading exists, the spread in the market widens when a deal occurs and this widening persists through time. They also incorporate in their analysis of spread determinants another variable: the lagged spread. The rational is that, after a removal or exhaustive transaction, an uncompetitively large spread will lead to more competitive quotes and hence spread reduction. They use data from Reuters D2000-2 electronic broking system taken from a record in June 1993. Their theoretical motivation comes from inventory trading models of Ho and Stoll (1983) and asymmetric information models of Glosten and Milgrom (1985)
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Galati (2000) examines the volume-spread relationship in the foreign exchange markets in emerging market countries. His results are in contrast to the predictions of the theory and previous empirical research, showing that in most cases spreads and trading volumes are negatively correlated. Results show that coefficients on unexpected volumes are negative and insignificant. The explanation that the author gives is that “the sample period may be too short to allow for changes in these foreign exchange markets that lead to more efficient trade processing and higher competition among market-makers”. In another study, Kouki (2003) found no evidence that volume has an information content on spread by using data from Tunisian dealers. In particular, both the euro and dollar transaction volume (when decomposed in unexpected and expected) have no significant effect in spread.