Exchange Rate Economics: Theories and Evidence

Market microstructure approach 363
of trade). In addition to the advantage of the longer time period compared to
Lyons,Yao’s dealer also has a substantial volume of trade with non-dealer customers
(25% relative to 1% in the case of the Lyons data set) making his mix of
trade more representative of the market average.
Evans (1997) also uses Reuters D 2000–1 data,sourced from a customised feed at
the Bank of England,on interbank trading for the period 1 May–31 August ,1996.
In particular,he has access to time-stamped tick-by-tick data on all transactions
for nine USD bilateral currencies (the price,a bought-and-sold indicator and
cumulative trading volume). This data has the advantage over that of Lyons and
Yao since it contains 24 hour transaction data on the whole of the interbank
market,rather than one dealer,and makes it possible to analyse order flow’s role
in price determination for the overall market. As Lyons (2001) notes,the fact that
this data set spans such a long time span,and for so many currencies,is important
since it allows analysing exchange rate determination from more of an asset price
perspective than is possible with the other data sets and,second,it permits analysis
of intra-day patterns with more precision than prior studies (noted earlier) which
used indicative quotes. However,to set against these advantages,the data set only
consists of transaction prices,rather than the spread,and also does not include
the inventory position of dealers. Although the data is date-stamped to the second
decimal place,it is not necessarily continuous and for this reason Evans analyses
the data on a 5 minute by 5 minute basis. He shows that there is pronounced
heterogeneity across the markets in the sense that,in terms of transactions,the
DM is a much more active market than either the guilder or krona.
The nature of the data allows Evans to construct measures of market-wide
demand for currencies of the form:
D t =
(Number of dollar purchases) − (Number of dollar sales)
,(14.28)
Number of dollar purchases + sales
and he finds that there is a strong statistically positive correlation between this
excess demand measure and the change in the exchange rate. Evans argues that
the source of this correlation can be traced to an informational asymmetry between
traders during bilateral conversations. In particular,the nature of the foreign
exchange market – decentralised trading,lack of transparency and heterogenous
information – means that there is a tendency of under-adjustment of quotes with
respect to excess demand and the lack of transparency stops traders from observing
the extent to which the arrival of private information is correlated across the market.
This,in turn,creates the informational asymmetry referred to earlier.
Using a VAR analysis,Evans also examines the interactions between the transaction
prices,quantities and quotes. This analysis suggests that the posting of quotes
is a distinct activity rather than an adjunct of trading,although the data also indicate
that quote and trading activity are not made independently from each other.
Furthermore,changes in trading activity within the interbank market significantly
affect quotes,while innovations in quote activity affect transactions. These complex
interactions are not consistent with any extant microstructure model.