An Introduction to New
Micro Exchange Rate Economics
[Entry
from the New Palgrave Dictionary of Finance and Economics]
Models of foreign
exchange (FX) market microstructure examine the determination and behavior of
spot exchange rates in an environment that replicates the key features of
trading in the FX market. Traditional macro exchange rate models play little
attention to how trading in the FX market actually takes place. The implicit
assumption is that the details of trading (i.e., who quotes currency prices and
how trade takes place) are unimportant for the behavior of exchange rates over
months, quarters or longer. Micro-based models, by contrast, examine how
information relevant to the pricing of foreign currency becomes reflected in
the spot exchange rate via the trading process. According to this view, trading
is not an ancillary market activity that can be ignored when considering
exchange rate behavior. Rather, trading is an integral part of the process
through which spot rates are determined and evolve. Recent micro-based FX
models also differ from other areas of microstructure research in their focus
on the links between trading, asset price dynamics, and the macroeconomy.
Recent research on
exchange rates stresses the role of heterogeneity (e.g., Bacchetta and van
Wincoop 2003, and Hau and Rey 2002). Micro-based exchange-rate models start
from the premise that much of the information about the current and future
state of the economy is dispersed across agents (i.e., individuals, firms, and
financial institutions). Agents use this information in making their every-day
decisions, including decisions to trade in the FX market at the prices quoted
by dealers. Dealers quote prices (e.g. dollars per unit of foreign currency) at
which they stand ready to buy or sell foreign currency; they will purchase
foreign currency at their bid quote, and sell foreign currency at their ask
quote. Agents that choose to trade with an individual dealer are termed the dealer’s
customers. The difference between the value of purchase and sale orders initiated by customers during any
trading period is termed customer order flow. Importantly, order flow is
different from trading volume because it conveys information. Positive (negative)
order flow indicates to a dealer that, on balance, their customers value
foreign currency more (less) than his asking (bid) price. By tracking who
initiates each trade, order flow provides a measure of the information
exchanged between counterparties in a series of financial transactions.
Trading in the FX
market also takes place between dealers. In direct interdealer trading, one
dealer asks another for a bid and ask quote, and then decides whether he wishes
to trade. When the dealer initiating the trade purchases (sells) foreign
currency, the trade generates a positive (negative) interdealer order flow
equal to the value of the purchase (sale). Interdealer trading can also take
place indirectly via brokerages that act as intermediaries between two or more
dealers. In recent years electronic brokerages have come to dominate
interdealer trading, but the interdealer order flow generated by brokered
trades plays the same informational role as the order flow associated with
direct interdealer trading.
Micro-Based
Exchange Rate Determination
At first sight,
the pattern of FX trading activity seems far too complex to provide any useful
insight into the behavior of exchange rates. However, on closer examination, two
key features emerge: First, the equilibrium spot exchange rate does not come
out of a “black box”. Instead, it is solely a function of the
foreign currency prices quoted by dealers at a point in time. This is a
distinguishing feature of micro-based exchange rate models and has far-reaching
implications. Second, information about the current and future state of the
economy will only impact on exchange rates when, and if, it affects dealer
quotes. Dealers may revise their quotes in response to new public information
that arrives via macroeconomic announcements. They may also revise their quotes
based on orders they receive from customers and other dealers. This order flow
channel is the means though which dispersed information concerning the economy
affects dealer quotes and hence the spot exchange rate. The role played by
order flow in transmitting information to dealers, and hence to their quotes,
is another distinguishing feature of micro-based exchange rate models.
Micro-based models
incorporate these two features of FX trading into a simplified setting.
Canonical multi-dealer models, such as
In this trading
environment, optimal quote decisions take a simple form; all dealers quote the
same FX price to both customers and other dealers. We can represent the period-
quote as
,
(1)
where 
. 
is the log price
of foreign currency quoted by all dealers, and 
denotes exchange
rate fundamentals. The form for fundamentals differs according to the
macroeconomic structure of the model. For example, in Evans and Lyons (2004b), includes home and
foreign money supplies and household consumption. In models where central banks
conduct monetary policy via the control of short-term interest rates (i.e.,
follow Taylor-rules),will include variables used to set policy. More generally, will include a
term that identifies the foreign exchange risk premium.
While equation (1)
takes the present value form familiar from standard international macro models,
here it represents how dealers quote the price for foreign currency in
equilibrium. All dealers choose to quote the same price in this trading
environment because doing otherwise opens them up to arbitrage, a costly
proposition. (Recall that quotes are publicly observed and good for any amount,
so any discrepancy between quotes would represent an opportunity for a riskless
trading profit.) Consequently, the month- quote must be a function of information known to all
dealers. Equation (1) incorporates this requirement with the use of the
expectations operator, 
, that denotes
expectations conditioned on information common to all dealers at the start of
month , 
. This is not to say that all dealers have the same
information. On the contrary, the customer order flows received by individual
dealers represent an important source of private information so there may be a
good deal information heterogeneity across dealers at any one time. The
important point to note from equation (1) is that due to the “fear of
arbitrage”, individual dealers choose not to quote prices based on their
own private information. In this trading environment, dealers use their private
information in initiating trade with other dealers, and, in so doing,
contribute to the process through which all dealers acquire information.
The implications of
micro-based models for the dynamics of spot rates are most easily seen by
rewriting (1) as
,
(2)
where 
, and
.
(3)
Equation (2)
decomposes the change in the log spot rate (i.e., the depreciation rate for the
home currency) into two components: the expected change
identified by the first term, and the unexpected change, 
, shown in equation (3). Both terms contribute to exchange
rate dynamics in micro-based models. In equilibrium, dealers’
period– quote must be based on expectations,, that match the risk-adjusted returns on different assets.
This means that variations in the interest differential between home and
foreign bonds can contribute to the volatility of the depreciation rate via the
first term in (2). The second term,
, identifies the impact of new information received by all
dealers between the start of periods and 
. Equation (3) shows that new information impacts on the FX
price quoted in period to the extent it
revises forecasts of the present value of fundamentals based on dealers’ common
information.
As an empirical
matter, depreciation rates are very hard to forecast, so the dynamics of spot
rates are largely attributable to the effects of news. Here micro-based models
have a big advantage over their traditional counterparts because their
trade-based foundations provide detail on how news affects spot rates. In
particular, as equation (3) indicates, micro-based models focus on how new
information about the fundamentals reaches dealers and induces them to revise
their FX quotes.
News concerning
fundamentals can reach dealers either directly or indirectly. Common knowledge
(CK) news operates via the direct channel. CK news contains unambiguous
information about current and/or future fundamentals that is simultaneously
observed by all dealers and immediately incorporated into the FX price they
quote. In principle, macroeconomic announcements (e.g. on GDP, industrial
production or unemployment) could be a source for CK news, but in practice they
rarely contain much unambiguous new information. In fact, CK news events appear
rather rare. The indirect channel operates via order flow and conveys dispersed
information about fundamentals to dealers. Dispersed information comprises
micro-level information on economic activity that is correlated with
fundamentals. Examples include the sales and orders for the products of
individual firms, market research on consumer spending, and private research on
the economy conducted by financial institutions. Dispersed information first
reaches the FX market via the customer order flows received by individual
dealers. These order flows have no immediate impact on dealer quotes because
they represent private information to the recipient dealer. The information in
each customer flow will only impact on quotes once it is known to all dealers.
Interdealer order flow is central to this process. Individual dealers use their
private information to trade in the interdealer market. In so doing,
information on their customer orders is aggregated and spread across the market.
This process is known as information aggregation. Dispersed information is
incorporated into dealer quotes once this process is complete.
Empirical Evidence
The appeal of
micro-based models is not solely based on their theoretical foundations. In marked
contrast with traditional exchange-rate models, micro-based models have enjoyed
a good deal of empirical success. Evans
and Lyons (2002a) first demonstrated their empirical power when studying
the relation between depreciation rates and interdealer order flow at the daily
frequency. In particular, they show that aggregate interdealer order flow from
trading in the spot dollar/dmark market on day
accounts for 64 percent of the variation in the depreciation
rate, 
, between the start of days and 
. This is a striking result because macro models can account
for less than 1 percent of daily depreciation rates. It is also readily
explained in terms of equations (2) and (3). Aggregate interdealer order flow
during daytrading provides a measure of the market-wide information
flow that dealers use to revise their quotes between the start of days and . This contemporaneous relationship between depreciation
rates and interdealer order flows appears robust. It holds for many different
currencies and for different currency-order flow combinations (e.g., Evans and Lyons 2002b, Payne 2003 and Froot
and Ramadorai 2005). It is also worth emphasizing that order flow’s
impact on spot rates is very persistent. There is very little serial
correlation in the daily depreciation rates for major currencies, so the order
flow impact on current FX quotes persists far into the future.
While consistent
with the idea that dispersed information is impounded into spot exchange rates
via interdealer order flow, these results do not provide direct evidence on the
ultimate source of exchange rate dynamics. According to micro-based models, the
analysis of customer order flows should provide the evidence. In particular, if
interdealer order flows measure the market-wide information flow that carries
the information concerning fundamentals originally motivating customer orders,
customer orders should also have explanatory power for depreciation rates. This
is indeed the case. Evans
and Lyons (2004b) show that a significant contemporaneous relationship
exists between depreciation rates and the customer order flows of a single
large bank. Moreover, the strength of this relationship increases as we move
from a one day to a one month horizon. This, too, is consistent with
micro-based models: At longer horizons, customer flows from a single bank
should be a better proxy for the market-wide flow of information driving spot
rates.
Micro-based models
also make strong empirical predictions about the relationship between order
flows and fundamentals. According to equation (1), dealers are forward-looking
when quoting FX prices, so spot rates embody their forecasts for fundamentals
based on common information, . One empirical implication of this observation is that spot
exchange rates should have forecasting power for fundamentals. While there is
some evidence that this is true for variables that comprise fundamentals in
many models (Engel and West 2005), the forecasting power is rather limited.
Micro-based models also have implications for the forecasting power of order
flows: If order flows convey information about fundamentals that is not yet
common knowledge to all dealers (i.e., not in ), then they should have incremental forecasting power for
fundamentals, beyond the forecasting ability any variable in . This is a strong prediction: it says that order flow should
add to the forecasting power of all other variables in , including the history of spot rates and the fundamental
variable itself. Nevertheless, Evans and Lyons (2004b) find ample support for
this prediction using customer order flows and candidate fundamental variables
such as output, inflation and money supplies. These findings provide direct
evidence on the information content of customer order flows, and provide a new
perspective on the link between exchange rates and fundamentals.
Dispersed
information concerning fundamentals need not only come from the activities of
individuals, firms and financial institutions. Scheduled announcements on
macroeconomic variables (e.g. GDP, inflation, or unemployment) can also be a
source of dispersed information. If agents have different views about the
mapping from the announced variable to fundamentals, then the news contained in
any announcement, while simultaneously observed, will not be common knowledge.
For example, two firms may interpret the same announcement on last
quarter’s GDP as having different implications for future GDP growth.
Differing interpretations about the implications of commonly observed news will
be a source of customer order flows because they imply heterogeneous views
about future returns, which in turn, induces portfolio adjustment. Thus,
micro-based models raise the possibility that the exchange rate effects of
macro announcements operate via both a direct channel (i.e., when the
announcement contains CK news) and an indirect channel. Love and Payne (2002)
and Evans and Lyons (2003, 2005b) find evidence that both
channels are operable. Evans and Lyons estimate that roughly two–thirds
of the effect of a macro announcement is transmitted indirectly to the
dollar/mark spot rate via order flow, and one-third directly into quotes. With
both channels operating, macro news is estimated to account for more than
one-third of the variance in daily depreciation rates. This level of
explanatory power far surpasses that found in earlier research analyzing the
impact of macro news on exchange rates (e.g., Andersen et al. 2003). It also
further cements the link between spot rates and the macro variables comprising
fundamentals.
Order Flows, Returns and the Pace of
Information Aggregation
The process by which
the information contained in the customer flows becomes known across the
market, and hence embedded into FX quotes, is complex. The individual customer
and interdealer orders received by each dealer contain some dispersed
information about the economy, but extracting the information from each order
constitutes a difficult inference problem. Under some circumstances, the
inference problems are sufficiently simple for every dealer to learn all there
is to know about fundamentals in a few rounds of interdealer trading. In this
case, the pace of information aggregation is very fast, so that new information
concerning fundamentals is quickly reflected in dealer quotes whether the news
is initially dispersed or common knowledge. The resulting dynamics for exchange
rates over weeks, months or quarters will be indistinguishable from the
predictions of macro models. Under other circumstances, the inference problem
facing individual dealers is sufficiently complex to slow down the pace of
information aggregation. Here it takes many rounds of interdealer trading
before the dispersed information concerning fundamentals becomes known across
the market. This scenario is much more likely from a theoretical perspective. Evans and Lyons (2004a) show that
the conditions needed for fast information aggregation are quite stringent. Of
course, because interdealer trading takes places continuously, dispersed
information could be completely embedded in FX quotes in a short period of
calendar time (e.g., a day), even if the pace of information aggregation is
slow. In principle, dealers might be able to learn a good deal from the
multitude of orders they receive in a typical day, even if individual orders
are relatively uninformative. The question of whether it takes significant
amounts of calendar time before dispersed information is embedded in FX quotes
can only be answered empirically.
If the pace of
information aggregation is slow, customer order flows across the market contain
information that will only become known to all dealers at a later date. So, if
the customer orders received by an individual bank are representative of the
market-wide flows, they should have forecasting power for the future market-wide
flow of information that drives quote revision. Recent empirical findings
support this possibility. Evans and trading impacts on the deprecation rate, 
, via 
. The contemporaneous link arises because period-interdealer order flows measure the market-wide information
flow, . In contrast, the forecasting power of customer flows for
the depreciation rate arises because contains
information that was originally in the customer orders received by individual
banks before period- trading.
These forecasting
results are surprising both in terms of their horizon and strength. In particular,
out-of-sample forecasts based on customer flows from month 
can account for
roughly 16 percent of the variation in next month’s depreciation rate, . This finding suggests that the pace of information
aggregation is far, far slower than was previously thought; it seems to take
weeks, not minutes, for dispersed information to be fully assimilated across
the market. The level of forecasting power is also an order of magnitude above
that usually found in exchange rate models. For example, the in-sample
forecasting power of interest differentials for monthly depreciation rates is
only in the 2 – 4 percent range.
The slow pace of
information aggregation may shed light on one of the long-standing puzzles in
exchange rate economics; the disconnect between spot exchange rates and
fundamentals over short and medium horizons (Meese and Rogoff 1983). The idea
is quite simple. If changes in fundamentals are only reflected in spot rates
once information concerning the change is recognized by dealers across the
market, the slow pace of information aggregation will mask the link between the
depreciation rate and the change in fundamentals over short horizons, because
the latter is a poor proxy for the market-wide flow of information. Simulations
in Evans and Lyons (2004a) show
that this masking effect can be quite substantial. Fundamentals account for
only 50 percent of variation in spot rates at the two-year horizon even though
information aggregation takes at most 4 months.
One factor that
might contribute to the slow pace of information aggregation is the presence of
price-contingent order flow generated by feedback trading. Stop-loss orders, for
example, represent a form of positive feedback trading, in which a fall in the
FX price triggers negative order flow from customers wishing to insure their
portfolios against further losses. Feedback trading of a known form does not
complicate the inference problem facing dealers because the orders it generates
are simply a function of old market-wide information. However, when the exact
form of the feedback is unknown, it makes inferences less precise and so slows
down the pace of information aggregation. Osler (2005) argues that feedback
trading will be an important component of order flow when quotes approach the
points at which stop-loss orders cluster. A fall in FX quotes at these points
can trigger a self-reinforcing price-cascade where causation runs from quotes
to order flow.
Some
economists argued that the early empirical findings linking order flow and the
depreciation rate reflected the presence of positive feedback trading rather
than the transmission of dispersed information. Indeed, there is no way to tell
whether intraday causation runs from order flows to quotes or vice verse from
just the contemporaneous correlation between order flow and the deprecation
rate measured in daily data. However, the new evidence on the forecasting power
of order flow for both depreciation rates and fundamentals firmly points to
order flow as the conveyor of dispersed information. This is not to say that
feedback trading is absent. Portfolio insurance and other price-contingent
trading strategies (e.g., liquidity provision) undoubtedly contribute to order
flows and their presence may actually explain why the pace of information
aggregation is so slow.
Future Research
Exchange rate
research using micro-based models is still in its infancy. The past few years
have seen a rapid advance in theoretical modeling and some surprising empirical
results. Advances on the empirical side will be spurred by the greater
availability of trading data. On the theoretical side, micro-based modeling may
provide new insights into the determinants of the foreign exchange risk
premium, the efficacy of foreign exchange intervention, and the anatomy of
financial contagion.
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