Exchange Rate Economics: Theories and Evidence

Market microstructure approach 357
negatively related to the spread because of economies of scale leading to more
efficient processing of trades and greater competition amongst market makers.
Jorion (1996),in turn,argues that this implies that expected volume should also be
negatively related to the spread,a relationship which is formally captured in the
model of Easley and O’Hara (1992). Furthermore,unexpected trading volume,
since it reflects contemporaneous volatility through the mixture of distribution
hypothesis,should be positively related to bid–ask spreads. In the context of the
study discussed in Section 14.3,Jorion (1996) confirms the negative relationship
between volatility and expected volume and he interprets these results as a confirmation
that bid–ask spreads reflect primarily inventory carrying costs that depend
primarily on price uncertainty and trading activity.
Hseih and Kleidon (1996) empirically examine the predictions from the standard
informational asymmetry models of Admati and Pfleiderer (1988) and
Subrahmanyam (1991),discussed earlier. In particular,Hseih and Kleidon (1996)
use the data of Bollerslev and Domowitz (1993) (these data are Rueters indicative
bid–ask quotes for the period 9 April–30 June 1989),in two markets,London and
New York. They show that the volume and volatility in these markets follows the
same U-shaped pattern as in the NYSE: volume and volatility is much greater at
the open and close of business. However,they also show that the bid–ask spread
actually goes up at the open and close of markets and this is at odds with the
key prediction of the Admati and Pfleiderer (1988) model. Subrahmanyam argues
that this kind of result is consistent with his extension of the model to risk-averse
traders: the increased trading by informed traders results in lower market liquidity
and higher costs. However,Hseih and Kleidon (1996) argue that the asymmetric
information models of Admati and Pfleiderer (1988) and Subrahmanyam (APS)
are not consistent with foreign exchange data on spreads and volatility for two
reasons. First,Subrahmanyam’s extension of the Admati and Pfleiderer model is
at the cost of loosing the main prediction of their model,namely,the concentrated
trading equilibrium to account for simultaneous high volume and high volatility.
Second,another interesting feature of the empirical evidence presented by Hseih
and Kleidon (1996) is that they find that at the time of the close of the London
market,when volume and volatility is high,there is no corresponding high volume
and volatility pattern in New York and this contradicts the kind of assymetric
information model of APS where knowledge of the economic structure is common
even in the presence of idiosynchratic information.
Hseih and Kleidon (1996) propose two alternative explanations for the kind of
results that they report. First,they propose using a broader class of information
models and,in particular,those models which relax the assumption that traders
have perfect knowledge about the preferences and beliefs of other traders in the
market. What they have in mind is that with differential information sets at the
start of a day’s trade,market participants need to get a ‘feel’ for the market at that
time. The most important elements of this feel are participants in the market at
that point in time and their trading behaviour immediately prior to the trading
period. Hsieh and Kleidon (1996) go on to argue that ‘traders report that,until
they have got a feel for the market they are uncertain of their view and hence,