For example, the efficiency models of Equations 29.2 and 29.3 are derived based on a joint hypothesis: i the market equilibrium re-turns or prices are assumed to be some functions of the
Trang 1Chapter 29 MARKET EFFICIENCY HYPOTHESIS
MELODY LO, University of Southern Mississippi, USA
Abstract
Market efficiency is one of the most fundamental
research topics in both economics and finance Since
Fama (1970) formally introduced the concept of
market efficiency, studies have been developed at
length to examine issues regarding the efficiency of
various financial markets In this chapter, we review
elements, which are at the heart of market efficiency
literature: the statistical efficiency market models,
joint hypothesis testing problem, and three
categor-ies of testing literature
Keywords: market efficiency; security returns;
in-formation; autocorrelation; serial correlation
(tests); random walk model; (sub)martingale;
hy-pothesis testing; (speculative) profits; trading rules;
price formation
29.1 Definition
The simplest but economically reasonable
state-ment of market efficiency hypothesis is that
security prices at any time fully reflect all
avail-able information to the level in which the profits
made based on the information do not exceed
the cost of acting on such information The cost
includes the price of acquiring the information
and transaction fees When the price formation in
equity market satisfies the statement, market
participants cannot earn unusual profits based
on the available information This classical
market efficiency definition was formally
intro-duced by Fama (1970), and developed at length
by researchers in the field
29.2 The Efficient Market Model Much of work on this line of research is based on
an assumption that the condition of market equi-librium can be stated in terms of expected returns Although there exists diversified expected return theories, they can in general be expressed as fol-lows:
E( ^pi,tþ1)¼ [1 þ E(^rri,tþ1 jIt)] pi,t, (29:1)
where E is the expected value operator; pi,t is the price of security i in period t, ri,tþ1 is the one-period rate of return on security i in the one-period ending at tþ 1, and E(ri,tþ1 jIt) is the expected rate of return conditional on information (I ) avail-able in period t Also, variavail-ables with hats indicate that they are random variables in period t The market is said to be efficient, if the actual security prices are identical to their equilibrium expected values expressed in Equation (29.1) In other words, if the actual security price formation fol-lows the market efficiency hypothesis, there would
be no expected returns=profits in excess of equilib-rium expected returns For a single security, this concept can be expressed as follows:
E( ^Zi,tþ1 jIt)¼ 0, and
Trang 2where Zi,tþ1 is the return at tþ 1 in excess of
the equilibrium expected returns anticipated at t
This concept can also apply to the entire security
market Suppose that market participants use
infor-mation, It, to allocate the amount, li(It), of funds
available to each of n security that makes up the entire
security market If the price formation of each of n
security follows Equation (29.2), then the total excess
market value at tþ 1( ^Vtþ1) equals to zero, i.e
E( ^Vtþ1 jIt)¼Xn
i¼1
li(It)E( ^Zi,tþ1 jIt)¼ 0: (29:3)
The general efficient market models of Equations
(29.2) and (29.3) are the foundations for empirical
work in this area Researchers in the field largely
agree that security prices ‘‘fully reflect’’ all available
information has a direct implication: successive
returns (or price changes) are independent
Conse-quently, researchers tend to conclude market is
efficient if there are evidences that demonstrate
E( ^Zi,tþ1 jIt)¼ 0 and Zi,t is uncorrelated with Zi,tþk
for any value of k Similarly, if E( ^Vtþ1 jIt)¼ 0 and
Vi,t is uncorrelated with Vi,tþk for any value of k,
market is evident to be efficient
Based on efficiency models in Equations (29.2)
and (29.3), two special statistical models,
submar-tingale and random walk, are closely related to the
efficiency empirical literature The market is said
to follow a submartingale when the following
con-dition holds:
E( ^Zi,tþ1 jIt) $ 0 for all t and It: (29:4)
The expected returns conditional on Itis nonnegative
and has an important implication on trading rule
This means investors should hold the security once it
is bought during any future period, because selling it
short cannot generate larger returns More
import-antly, if Equation (29.4) holds as equality, the market
is said to follow a martingale Researchers usually
conclude that security prices follow ‘‘patterns’’ and
market is inefficient when the empirical evidences are
toward rejection of a martingale model
The security prices exhibit the random walk
statistical property if not only that the successive
returns are independent but also that they are identically distributed Using f to denote the dens-ity function, the random walk model can be ex-pressed as follows:
f (ri,tþ1 jIt)¼ f (ri,tþ1) for all t and It: (29:5) The random walk property indicates that the return distributions would repeat themselves Evidences
on random walk property are often considered to
be a stronger supportive of market efficiency hy-pothesis than those on (sub)martingale property
29.3 The Joint Hypothesis Problem The continuing obstacle in this line of empirical literature is that the market efficiency hypothesis per se is not testable This is because one cannot test market efficiency hypothesis without imposing restrictions on the behavior of expected security returns For example, the efficiency models of Equations (29.2) and (29.3) are derived based on
a joint hypothesis: (i) the market equilibrium re-turns (or prices) are assumed to be some functions
of the information set and (ii) the available infor-mation is assumed to be fully utilized by the mar-ket participants to form equilibrium returns, and thereby current security prices As all empirical tests of market efficiency are tests of a joint hy-pothesis, a rejection of the hypothesis would al-ways lead to two possible inferences: either (i) the assumed market equilibrium model has little abil-ity to capture the securabil-ity price movements or (ii) the market participants use available information inefficiently Because the possibility that a bad equilibrium model is assumed to serve as the benchmark can never be ruled out, the precise inferences about the degree of market efficiency remains impossible to identify
29.4 Three Categories of Testing Literature The empirical work on market efficiency hypoth-esis can be categorized into three groups First, weak-form tests are concerned with how well past
Trang 3security returns (and other explanatory variables)
predict future returns Second, semi-strong-form
tests focus on the issue of how fast security price
responds to publicly available information Third,
strong-form tests examine whether security prices
fully reflect private information
29.4.1 Weak-Form Tests
Controversy about market efficiency centers on the
weak-form tests Many results from earlier works
on weak-form tests come directly from the
submar-tingale expected return model or the random walk
literature In addition, much of the earlier works
consider information set as just past historic
re-turns (or prices) The most frequently used
proced-ure to test the weak form of efficient markets is to
examine whether there is statistically significant
autocorrelation in security returns using serial
cor-relation tests A pattern of autocorcor-relation in
se-curity returns is interpreted as the possibility that
market is inefficient and market participants are
irrational, since they do not fully exploit
specula-tive opportunities based on the price dependence
The serial correlation tests are tests of a linear
relationship between current period’s returns (Rt)
and past returns (Rt1):
Rt¼ a0þ a1Rt1 þ «t, (29:6)
where Rtis the rate of return, usually calculated as
the natural logarithm first differences of the
trad-ing price (i.e Rt¼ ln Pt ln Pt1; Pt and Pt1 are
the trading prices at the end of period t and of
period t 1, respectively.), a0 is the expected
re-turn unrelated to previous rere-turns, and a1 is the
size of first-order autocorrelation in the rate of
returns For market efficiency hypothesis to hold,
a1 needs to be statistically indifferent from 0
After conducting serial correlation analysis,
Kendall (1953) concluded that market is efficient
because weekly changes in 19 indices of British
industrial share prices and in spot prices for cotton
and wheat exhibit the random walk property
Roberts (1959) notes that similar statistical results
can be found when examining weekly changes in
Dow Jones Index (See also Moore, 1962; Godfrey
et al., 1964; and Fama, 1965.) Some researchers later argued that the size of serial correlation in returns offers no precise implications on the extent
of speculative profits available in the market They propose that examining the profitability of various trading rules can be a more straightforward meth-odology for efficiency tests A representative study that adopted this methodology was done by Alex-ander (1961), where he examines the profitability
of various trading rules (including the well-known y% filter rule) Despite a positive serial correlation
in return series, he also discovers that y% filter rule cannot outperform buy-and-hold rule He thus concludes that the market is still an efficient one Similarly, Fama and Blume (1966) find positive dependence in very short-term individual stock price of the Dow Jones Industrial index Yet, they also suggest that market is efficient because the overall trading costs from any trading rule, aiming
to utilize the price dependence to profit, is suffi-ciently large to eliminate the possibility that it would outperform the buy-and-hold rule In gen-eral, results from earlier work (conducted before the 1970s) provide no evidence against efficient market hypothesis since they all report that the autocorrelations in returns are very close to 0
As more security data becomes available, the post-1970 studies always claim that there is signifi-cant (and substantial) autocorrelation in returns Lo and MacKinlay (1988) report that there is positive autocorrelation in weekly returns on portfolios of NYSE stocks grouped according to size In particu-lar, the autocorrelation appears to be stronger for portfolios of small stocks According to Fisher’s (1966) suggestion, this result could be due to the nonsynchronous trading effect Conrad and Kaul (1988) investigate weekly returns of size-based port-folios of stocks that trade on both Wednesdays to somehow alleviate the nonsynchronous trading ef-fect However, as in Lo and MacKinlay (1988), they find positive autocorrelation in returns and that this pattern is stronger for portfolios of small stocks
On another note, the post-1970 weak-form test studies focus on whether variables other than past
Trang 4returns can improve return predictability Fama
and French (1988) use dividend yield to forecast
returns on the portfolios of NYSE stock They find
that dividend yield is helpful for return
predictabil-ity On the other hand, Compbell and Shiller
(1988) report that earnings=price ratio increases
the return predictability In summary, recent
stud-ies suggest that returns are predictable when
vari-ables other than past returns are used and the
evidences seem to be against the market efficiency
hypothesis that was well supported before the
1970s
29.4.2 Semi-strong-Form Tests
Each of the semi-strong-form tests is concerned
with the speed of price adjustment to a particular
public information event The event can be
macro-economic announcement, companies’ financial
reports, or announcement on stock split The initial
work in this line of research was by Fama et al.,
(1969), in which they studied the speed of price
adjustment to the stock-split announcement Their
results show that the informational implications of
a stock split are fully reflected in the price of a share
at least by the end of the month, or most probably
almost immediately after the day of the stock-split
announcement They therefore conclude that the
stock market is efficient because the prices respond
quite speedily to new public information Waud
(1970) uses residual analysis to study how fast
mar-ket reacts to the Federal Reserve Bank’s
announce-ment on discount rate changes The result suggests
that market responds rapidly to the interest-rate
announcement even when the Federal Reserve
Board is merely trying to bring the discount rate in
line with other market rates Ball and Brown (1968)
investigate the price reactions to the
annual-earn-ings announcement They conclude that market
participants seem to have anticipated most
infor-mation by the month’s end, after the
annual-earn-ings announcement These earlier studies (prior to
the 1970s), focusing on different events of public
announcement, all find supportive evidences of
market efficiency hypothesis Since the 1970s, the
semi-strong-form test studies have been developed
at length The usual result is that stock price adjusts within a day of the announcement being made pub-lic Nowadays, the notation that security markets are semi-strong-form efficient is widely accepted among researchers
29.4.3 Strong-Form Tests The strong-form tests are concerned with whether prices fully reflect all available information so that
no particular group of investors have monopolistic access to some information that can lead to higher expected returns than others It is understandable that as long as some groups of investors in reality
do have monopolistic access to the information, the strong-form market efficiency hypothesis is impossible to hold In fact, both groups of special-ists, NYSE (see Niederhoffer and Osborne, 1966) and corporate insiders (see Scholes, 1969), have monopolistic access to information, and which has been documented Since the strong-form effi-ciency model is impossible to satisfy, the main focus in this line of work is to assess if private information leads to abnormal expected returns, and if some investors (with private information) perform better than others because they possess more private information The most influential work before the 1970s was by Jensen (1968, 1969) where he assessed the performance of 115 mutual funds Jensen (1968) finds that those mutual funds under examination on average were not able to predict security prices well enough to outperform the buy-and-hold trading rule Further, there ap-pears no evidence suggesting that individual mu-tual fund performs significantly better than what
we expect from random chances Using Sharpe– Lintner theory (see Sharpe, 1964; Lintner, 1965), Jansen (1969) developed a model to evaluate the performance of portfolios of risk assets Most im-portantly, he manages to derive a measure of port-folio’s ‘‘efficiency’’ The empirical results show that on average the resources spent by the funds managers to better forecast security prices do not generate larger portfolio returns than what could
Trang 5have been earned by equivalent risk portfolios
selected either by random selection trading rule
or by combined investments in market portfolios
and government bonds Jansen further interprets
his results that probably mutual fund managers do
not have access to private information These
re-sults are clear in line with strong-form market
efficiency models because evidence suggests that
current security prices have fully reflected the
ef-fects of all available information After the 1970s,
there is less of new research examining investors’
access to private information that is not reflected
in security prices Representative studies were done
by Henriksson (1984) and Chang and Lewellen
(1984) In tests of 116 mutual funds, Henriksson
(1984) reports that there is difference between
mu-tual fund returns and Sharpe–Lintner market line
Similarly, Chang and Lewellen (1984) note that
examination of mutual fund returns show no
sup-portive evidence of fund managers’ superior
selec-tion abilities In short, recent studies largely agree
to prior literature’s view that investors with private
information are unable to outperform a passive
investment strategy Evidences are still in favor of
the existence of market efficiency hypothesis
29.5 Conclusion
This review has been brief and so various issues
related to market efficient model have not been
considered Volatility tests of market efficiency,
and cross-sectional return predictability based on
various asset pricing models are just some of the
omitted issues For more details, readers are
referred to two excellent market efficiency survey
papers by Fama (1970, 1991)
REFERENCES Alexander, S.S (1961) ‘‘Price movements in speculative
markets: trends or random walks.’’ Industrial
Man-agement Review, 2: 7–26.
Ball, R and Brown, P (1968) ‘‘An empirical evaluation
of accounting income numbers.’’ Journal of
Account-ing Research, 6: 159–178.
Chang E.C., and Lewellen, W.G (1984) ‘‘Market tim-ing and mutual fund investment performance.’’ Jour-nal of Business, 57: 57–72.
Compbell J.Y and Shiller, R (1988) ‘‘Stock prices, earnings and expected dividends.’’ Journal of Fi-nance, 43: 661–676.
Conrad, J and Kaul, G (1988) ‘‘Time-variation in expected returns.’’ Journal of Business, 61(4): 409–425.
Fama, E.F (1965) ‘‘The behavior of stock market price.’’ Journal of Business, 38(1): 34–105.
Fama, E.F (1970) ‘‘Efficient capital markets: a review
of theory and empirical work.’’ Journal of Finance, 25(2): 383–417.
Fama, E.F (1991) ‘‘Efficient capital markets: II.’’ Jour-nal of Finance, 46(5): 1575–1617.
Fama, E.F and Blume, M (1966) ‘‘Filter rules and stock market trading profits.’’ Journal of Business (Special Supplement), 39: 226–241.
Fama, E.F and French, K.R (1988) ‘‘Dividend yields and expected stock returns.’’ Journal of Financial Economics, 22: 3–25.
Fama, E.F., Fisher, L., Jensen, M.C., and Roll, R (1969).
‘‘The Adjustment of Stock Prices to New Informa-tion.’’ International Economic Review, 5: 1–21
Fisher, L (1966) ‘‘Some new stock-market indexes.’’ Journal of Business, 39(1), Part 2: 191–225.
Godfrey, M.D., Granger, C.W.J., and Morgenstern, O (1964) ‘‘The random walk hypothesis of stock mar-ket behavior.’’ Kyklos, 17: 1–30.
Henriksson, R.T (1984) ‘‘Market timing and mutual fund performance: an empirical investigation.’’ Jour-nal of Business, 57: 73–96.
Jensen, M.C (1968) ‘‘The performance of mutual funds in the period 1945–64.’’ Journal of Finance, 23: 389–416.
Jensen, M.C (1969) ‘‘Risk, the pricing of capital assets, and the evaluation of investment portfolios.’’ Journal
of Business, 42: 167–247.
Kendall, M.G (1953) ‘‘The analysis of economic time-series, Part I: Prices.’’ Journal of the Royal Statistical Society, 96 (Part I): 11–25.
Lintner, J (1965) ‘‘Security prices, risk, and maximal gains from diversification.’’ Journal of Finance, 20: 587–615.
Lo, A.W and MacKinlay, A.C (1988) ‘‘Stock market prices do not follow random walks: evidence from a simple specification test.’’ Review of Financial Stud-ies, 1(1): 41–66.
Moore, A (1962) ‘‘A Statistical Analysis of Common Stock Prices.’’ PhD thesis, Graduate School of Busi-ness, University of Chicago.
Trang 6Niederhoffer, V and Osborne, M.F.M (1966) ‘‘Market
making and reversal on the stock exchange.’’ Journal
of the American Statistical Association, 61: 897–916.
Roberts, H.V (1959) ‘‘Stock market ‘patterns’ and
financial analysis: methodological suggestions.’’
Journal of Finance, 14: 1–10.
Scholes, M (1969) ‘‘A test of the competitive
hypoth-esis: the market for new issues and secondary
offer-ing.’’ PhD thesis, Graduate School of Business, University of Chicago.
Sharpe, W.F (1964) ‘‘Capital assets prices: a theory of market equilibrium under conditions of risk.’’ Journal
of Finance, 19: 425–442.
Waud, R.N (1970) ‘‘Public interpretation of federal discount rate changes: evidence on the ‘Announce-ment Effect’.’’ Econometrica, 38: 231–250.
Trang 7Chapter 30
THE MICROSTRUCTURE=
MICRO-FINANCE APPROACH
TO EXCHANGE RATES MELODY LO, University of Southern Mississippi, USA
Abstract
The vast empirical failure of standard macro
ex-change rate determination models in explaining
exchange rate movements motivates the development
of microstructure approach to exchange rates in the
1990s The microstructure approach of incorporating
‘‘order flow’’ in empirical models has gained
consid-erable popularity in recent years, since its superior
performance to macro exchange rate models in
explaining exchange rate behavior It is shown that
order flow can explain about 60 percent of exchange
rate movements versus 10 percent at most in standard
exchange rate empirical models As the
microstruc-ture approach to exchange rates is an active ongoing
research area, this chapter briefly discusses key
concepts that constitute the approach
Keywords: microstructure approach; order flow;
exchange rates; macroexchange rate models;
het-erogeneous information; private information; asset
market approach; goods market approach;
cur-rency; divergent mappings; transaction
30.1 Definition
The microstructure approach to exchange rates is
considered to be a fairly new but active research
area This line of research emerged in the early
1990s mostly due to the vast empirical failure
of standard macro exchange rate determination models In more recent years (the late 1990s), there was considerably a large amount of pub-lished work regarding the microstructure approach
to exchange rates, suggesting order flow is evident
to be the missing piece in explaining exchange rate behavior The following definition of the micro-structure approach to exchange rates comes dir-ectly from its pioneer, Richard Lyons (See Lyons, 2001)
The microstructure approach is a new approach
to exchange rates whose foundations lie in micro-economics (drawing particularly from microstruc-ture finance) The focus of the approach is dispersed information and how information of this type is aggregated in the marketplace By dispersed information, we mean dispersed bits of information about changing variables like money demands, risk preferences, and future inflation Dispersed information also includes information about the actions of others (e.g about different trading responses to commonly observed data) The fact that the private sector might be solv-ing a problem of dispersed information is not con-sidered in traditional macro models Rather, macro models assume that information about vari-ables like money demands, risk preferences, and inflation is either symmetric economy-wide, or in
Trang 8some models, asymmetrically assigned to a single
player – the central bank In reality, there are
many types of dispersed information that
ex-change rates need to impound Understanding the
nature of this information problem and how it is
solved is the essence of this micro-based research
agenda
30.2 Empirical Failure of Traditional Approaches
To Exchange Rates
The literature has documented extensively the little
ability traditional=standard exchange rate
deter-mination models have to explain exchange rate
behavior Meese and Rogoff (1983) show that a
random walk model outperforms the standard
international-finance models in forecasting
ex-change rates In that respect, Meese (1990) writes
that ‘‘ the proportion of (monthly or quarterly)
exchange rate changes that current models can
explain is essentially zero This result is quite
surprising, since exchange rate changes would be
entirely unpredictable only in very special cases of
the theoretical models discussed.’’ More recently, a
survey paper by Frankel and Rose (1995) also
notes that ‘‘To repeat a central fact of life, there
is remarkably little evidence that macroeconomic
variables have consistent strong effects on floating
exchange rate, except during extraordinary
circum-stances such as hyperinflations.’’
Two most frequently discussed standard
exchange rate determination approaches are
(1) goods market approach and (2) asset market
approach The goods market approach suggests
that exchange rates move to reflect necessary
changes in excess demand=supply of foreign
cur-rency resulting from international trades A
do-mestic economy necessarily demands for more
foreign currencies when its citizens consume more
imported goods The general prediction of goods
market approach is that an increase in domestic
trade deficit must lead to the depreciation of
do-mestic currency against foreign currency
How-ever, existing studies find no empirical evidence to
support any specific relation between current ac-count imbalance and exchange rate movements
In open economies, domestic citizens can pur-chase not only foreign goods but also foreign fi-nancial assets The asset market approach suggests that demand for foreign currency increases when domestic citizens increase their possessions on for-eign assets, and this in turn would cause domestic currency to depreciate against foreign currency Different from the goods market approach, the asset market approach also concerns the market efficiency issue Specifically, the theoretical models
on asset market approach determine equilibrium exchange rate at the level that no public informa-tion can lead to excess returns
In general, the empirical model specification for asset market approach is as follows (Lyons, 2001):
DEt ¼ f1(i,m,z)þ «1t, (30:1)
where DEt is changes in nominal exchange rate (usually monthly or weekly data is used), the func-tion f1(i,m,z) includes the current and past values of domestic and foreign interest rates (i), money sup-ply (m), and all other macro variables (z) Similar to the low predictability of goods market approach, the majority of asset market empirical studies re-port that macro variables in Equation (30.1) explain
10 percent only, at most, of exchange rate move-ments Further details on the empirical failure of various standard exchange rate determination models are well documented by Taylor (1995) The disappointing results from the existing ex-change rate models motivated researchers to look for sources responsible for the empirical failure They attribute the general empirical failure to the unrealistic assumptions shared among standard exchange rate determination models In detail, these models assume that every market participant learns new information at the same time when macroeconomic information=news is made public Further, all market participants are assumed to have the ability to impound macro information into prices to the same level However, both as-sumptions can easily be argued In reality, not only
Trang 9market participants’ information set is
heteroge-neous, but also their mapping ability from
avail-able information to price is impossible to be the
same The heterogeneity in information set is
evi-dent from the fact that foreign exchange traders,
working for different banks, each have their own
customers to deal with Transactions with different
customers offer each trader ‘‘private’’ information
that he may not intend to share with others In
addition, it is understandable that different people
tend to interpret the market impact of new
in-formation on exchange rate differently, regardless
whether the information is made available to all
of them at the same time This idea of divergent
mappings from information to prices is discussed by
Isard (1995, pp 182–183) who states that
‘‘econo-mist’s very limited information about the
relation-ship between equilibrium exchange rates and
macroeconomic fundamentals, it is hardly
con-ceivable that rational market participants with
complete information about macroeconomic
fun-damentals could use that information to form
pre-cise expectations about the future market-clearing
level of exchange rates.’’
30.3 Why Microstructure Approach?
The unrealistic assumptions in standard exchange
rate models mentioned above have been relaxed in
the literature that aims to explain why the financial
market crashed It is important to note that despite
events such as stock market crash and currency
crisis appear to be macro issues, they can be largely
explained by microstructure approach that
con-siders the existence of heterogeneous information
among market participants (see Grossman, 1988;
Romer, 1993; Carrera, 1999) For the same token,
Lyons argues that adopting microstructure
ap-proach to investigate the trading process of
ex-change rates may help our understanding on
when and how exchange rates move Lyons
(2001, p 4) notes that the microstructure approach
is an approach that relaxes three of the assets
approach’s most uncomfortable assumptions
First, on the aspect of information, microstructure
models recognize that some information relevant
to exchange rates is not publicly available Second,
on the aspect of players, microstructure models recognize that market participants differ in ways that affect prices Last, on the institutional aspect, microstructure models recognize that trading mechanism differs in ways that affect prices
30.4 The Information Role of Order Flow The central variable that takes the fundamental role in microstructure approach, but has never been presented in any of previous exchange rate models, is order flow Order flow is cumulative flow of signed transaction volume A simple ex-ample on how order flow is counted for individual transaction can be helpful Suppose that a dealer decides to sell 5 units of U.S dollars via a market order (one unit usually represents a transaction worth $1 million), then order flow is counted as –
5 The negative sign is assigned for this $5 million transaction because it is a seller-initiated order Each transaction is signed positively or negatively depending on whether the initiator of the transac-tion is buying or selling Over time, order flow gives us a relative number of buyer-initiated versus seller-initiated orders in a market Thus, order flow provides information to dealers about the relative demand for currencies at any time in the market Since market participant must make buy-or-sell decisions according to available infor-mation (including their private inforinfor-mation), it is presumed that order flow is at certain level driven
by market fundamentals
Order flow plays a fundamental role in exchange rate movements because it has the function to transmit information that is not known by every-one in the market In fact, this concept of order flow transmitting information is intuitionally appealing As an example to describe the intuition, consider two traders (referred to dealer A and dealer B) in the foreign exchange market, and each of them trades for a particular bank Each bank of course has its own customers from whom it buys and sells foreign exchange When THE MICROSTRUCTURE=MICRO-FINANCE APPROACH TO EXCHANGE RATES 593
Trang 10dealer A trades with his own customers, he obtains
private information, such as the customers’ view of
the current market (price), and which, is not
known to dealer B However, when dealer A puts
orders in the inter-dealer market in an attempt to
balance out positions with outside customers (for
inventory concern), dealer A’s private information
is learned by dealer B An alternative example is
related to the idea of divergent mappings from
(public) information to prices Suppose dealer A
hears a macro announcement at the same time as
dealer B Although they do not know how each
other would interpret the announcement’s effect
on prices, they can learn this information by
watching how each other trades
A related question that is frequently asked is
‘‘does order flow really contain (market)
informa-tion?’’ The answer is positive The direct evidences
come from dealers themselves In surveys
con-ducted by Cheung and Wong (2000), about 50
percent of dealers who responded to the survey
claim that they believe banks with larger customer
base have information advantage This is because
they get to trade with more customers, and more
transactions ensure more private information,
which leads to better speculative opportunities
Further evidence is from empirical analysis,
which examine whether order flows have a
per-manent effect on prices The rationale behind this
empirical analysis is if order flow does not contain
any information about market fundamentals, it
can only have transitory effect on prices French
and Roll (1986) have used this methodology to
identify the information arrival Using vector
auto-regression models, Evans (2001) and Payne (1999)
found that order flow innovation has long-run
effect on prices This result provides evidence that
order flow does contain information related to
market fundamentals
The general empirical model specification for
microstructure approach to exchange rates can be
written as follows (Lyons, 2001):
DEt ¼ f2(X ,I,Z)þ «2t, (30:2)
where DEt is changes in nominal exchange rate between two transactions, function f2(X ,I ,Z) in-cludes the order flow (X), dealers’ inventory (I), and all other micro variables (Z) The microstruc-ture models predict that an upward move in price
is associated with a situation in which buyer-initiated trades exceed seller-buyer-initiated trades In other words, to support microstructure approach
to exchange rate, there needs to be a positive rela-tion between order flows and prices Lyons (2001) and Evans and Lyons (2002) have shown the con-siderably strong positive impact of order flow on exchange rates More precisely, they have shown that order flow can explain about 60 percent (ver-sus 10 percent at most in standard exchange rate empirical models expressed in Equation (30.1) ) of exchange rate movement
30.5 Conclusion The high explanatory power of order flow for exchange rate movements is exciting news for re-searchers in the area So far, all empirical evidences have suggested order flow is indeed the important missing piece in exchange rate determination Lyons (2001) thus claims that order flows help solve three exchange rate puzzles: (1) the determin-ation puzzle, (2) the excess volatility puzzle, and (3) the forward-bias puzzle Yet, there is not much agreement toward this claim (see Dominguez, 2003) Clearly, more research needs to be done before these puzzles may be solved
REFERENCES Carrera, J.M (1999) ‘‘Speculative attacks to currency target zones: a market microstructure approach.’’ Journal of Empirical Finance, 6: 555–582.
Cheung, Y.W and Wong, C.Y.P (2000) ‘‘A survey market practitioners’ view on exchange rate dynam-ics.’’ Journal of International Economics, 51: 401–423 Dominguez, K.M.E (2003) ‘‘Book Review: Richard
K Lyons, The microstructure approach to exchange rates, 2001, MIT Press’’ Journal of International Eco-nomics, 61: 467–471.