Foreign Exchange Market Microstructure

Transcription

1 Foreign Exchange Marke Microsrucure Marin.. Evans 1 Georgeown Universiy and NBER Absrac This paper provides an overview of he recen lieraure on Foreign Exchange Marke Microsrucure. Is aim is no o survey he lieraure, bu raher o provide an inroducory our o he main heoreical ideas and empirical resuls. The cenral heoreical idea is ha rading is an inegral par of he process hrough which informaion relevan o he pricing of foreign currency becomes embedded in spo raes. Micro-based models sudy his informaion aggregaion process and produce a rich se of empirical predicions ha find srong suppor in he daa. In paricular, micro-based models can accoun for a large proporion of he daily variaion in spo raes. They also supply a raionale for he apparen disconnec beween spo raes and fundamenals. In erms of forecasing, micro-based models provide ou-ofsample forecasing power for spo raes ha is an order of magniude above ha usually found in exchange-rae models. Keywords: Exchange Raes, Microsrucure, Informaion Aggregaion, FX Trading. JEL No. F3, F4, G1 1 eparmen of Economics, Georgeown Universiy, Washingon C 20057, Tel: (202) , evansm1@georgeown.edu. This paper was prepared for he New Palgrave icionary of Economics. I hank Richard Lyons for valuable discussions and graefully acknowledge he financial suppor of he Naional Science Foundaion.

2 Inroducion Models of foreign exchange (FX) marke microsrucure examine he deerminaion and behavior of spo exchange raes in an environmen ha replicaes he key feaures of rading in he FX marke. Tradiional macro exchange rae models play lile aenion o how rading in he FX marke acually akes place. The implici assumpion is ha he deails of rading (i.e., who quoes currency prices and how rade akes place) are unimporan for he behavior of exchange raes over monhs, quarers or longer. Micro-based models, by conras, examine how informaion relevan o he pricing of foreign currency becomes refleced in he spo exchange rae via he rading process. According o his view, rading is no an ancillary marke aciviy ha can be ignored when considering exchange rae behavior. Raher, rading is an inegral par of he process hrough which spo raes are deermined and evolve. Recen micro-based FX models also differ from oher areas of microsrucure research in heir focus on he links beween rading, asse price dynamics, and he macroeconomy. Recen research on exchange raes sresses he role of heerogeneiy (e.g., Bacchea and van Wincoop 2003, and Hau and Rey 2002). Micro-based exchangerae models sar from he premise ha much of he informaion abou he curren and fuure sae of he economy is dispersed across agens (i.e., individuals, firms, and financial insiuions). Agens use his informaion in making heir every-day decisions, including decisions o rade in he FX marke a he prices quoed by dealers. ealers quoe prices (e.g. dollars per uni of foreign currency) a which hey sand ready o buy or sell foreign currency; hey will purchase foreign currency a heir bid quoe, and sell foreign currency a heir ask quoe. Agens ha choose o rade wih an individual dealer are ermed he dealer s cusomers. The difference beween he value of purchase and sale orders iniiaed by cusomers during any rading period is ermed cusomer order flow. Imporanly, order flow is differen from rading volume because i conveys informaion. Posiive (negaive) order flow indicaes o a dealer ha, on balance, heir cusomers value foreign currency more (less) han his asking (bid) price. By racking who iniiaes each rade, order flow 1

3 provides a measure of he informaion exchanged beween counerparies in a series of financial ransacions. Trading in he FX marke also akes place beween dealers. In direc inerdealer rading, one dealer asks anoher for a bid and ask quoe, and hen decides wheher he wishes o rade. When he dealer iniiaing he rade purchases (sells) foreign currency, he rade generaes a posiive (negaive) inerdealer order flow equal o he value of he purchase (sale). Inerdealer rading can also ake place indirecly via brokerages ha ac as inermediaries beween wo or more dealers. In recen years elecronic brokerages have come o dominae inerdealer rading, bu he inerdealer order flow generaed by brokered rades plays he same informaional role as he order flow associaed wih direc inerdealer rading. Micro-Based Exchange Rae eerminaion A firs sigh, he paern of FX rading aciviy seems far oo complex o provide any useful insigh ino he behavior of exchange raes. However, on closer examinaion, wo key feaures emerge: Firs, he equilibrium spo exchange rae does no come ou of a black box. Insead, i is solely a funcion of he foreign currency prices quoed by dealers a a poin in ime. This is a disinguishing feaure of micro-based exchange rae models and has far-reaching implicaions. Second, informaion abou he curren and fuure sae of he economy will only impac on exchange raes when, and if, i affecs dealer quoes. ealers may revise heir quoes in response o new public informaion ha arrives via macroeconomic announcemens. They may also revise heir quoes based on orders hey receive from cusomers and oher dealers. This order flow channel is he means hough which dispersed informaion concerning he economy affecs dealer quoes and hence he spo exchange rae. The role played by order flow in ransmiing informaion o dealers, and hence o heir quoes, is anoher disinguishing feaure of micro-based exchange rae models. Micro-based models incorporae hese wo feaures of FX rading ino a simplified seing. Canonical muli-dealer models, such as Lyons (1995) and Evans 2

4 and Lyons (2002a), posi a simple sequence of quoing and rading. A he sar of each period, dealers quoe FX prices o cusomers. These prices are assumed o be good for any amoun and are publicly observed. Each dealer hen receives orders from a subse of agens, his cusomers. ealers nex quoe prices in he inerdealer marke. These prices, oo, are good for any quaniy and are publicly observed. ealers hen have he opporuniy o rade among hemselves. Inerdealer rading is simulaneous and rading wih muliple parners is feasible. In his rading environmen, opimal quoe decisions ake a simple form; all dealers quoe he same FX price o boh cusomers and oher dealers. We can represen he period- quoe as s (1 b) b E[ f ], (1) i = i= 0 + i Ω where 0< b < 1. s is he log price of foreign currency quoed by all dealers, and f denoes exchange rae fundamenals. The form for fundamenals differs according o he macroeconomic srucure of he model. For example, in Evans and Lyons (2004b), f includes home and foreign money supplies and household consumpion. In models where cenral banks conduc moneary policy via he conrol of shor-erm ineres raes (i.e., follow Taylor-rules), f will include variables used o se policy. More generally, f will include a erm ha idenifies he foreign exchange risk premium. While equaion (1) akes he presen value form familiar from sandard inernaional macro models, here i represens how dealers quoe he price for foreign currency in equilibrium. All dealers choose o quoe he same price in his rading environmen because doing oherwise opens hem up o arbirage, a cosly proposiion. (Recall ha quoes are publicly observed and good for any amoun, so any discrepancy beween quoes would represen an opporuniy for a riskless rading profi.) Consequenly, he monh- quoe mus be a funcion of informaion known o all dealers. Equaion (1) incorporaes his requiremen wih he use of he expecaions operaor, E[. Ω ], ha denoes expecaions condiioned on 3

5 informaion common o all dealers a he sar of monh, Ω. This is no o say ha all dealers have he same informaion. On he conrary, he cusomer order flows received by individual dealers represen an imporan source of privae informaion so here may be a good deal informaion heerogeneiy across dealers a any one ime. The imporan poin o noe from equaion (1) is ha due o he fear of arbirage, individual dealers choose no o quoe prices based on heir own privae informaion. In his rading environmen, dealers use heir privae informaion in iniiaing rade wih oher dealers, and, in so doing, conribue o he process hrough which all dealers acquire informaion. The implicaions of micro-based models for he dynamics of spo raes are mos easily seen by rewriing (1) as Δ s = ( s E[ f Ω ]) + ε, (2) 1 b + 1 b + 1 where Δ s + 1 = s + 1 s, and ε ( [ ] [ ]). (3) 1 b i + 1 = b b E f i 1 + i Ω+ 1 E f = + i Ω Equaion (2) decomposes he change in he log spo rae (i.e., he depreciaion rae for he home currency) ino wo componens: he expeced change E[ Δs + 1 Ω ] ε idenified by he firs erm, and he unexpeced change, 1 = s 1 E[ s 1 Ω ], shown in equaion (3). Boh erms conribue o exchange rae dynamics in microbased models. In equilibrium, dealers period quoe mus be based on expecaions, E[ Δs + 1 Ω ], ha mach he risk-adjused reurns on differen asses. This means ha variaions in he ineres differenial beween home and foreign bonds can conribue o he volailiy of he depreciaion rae via he firs erm in (2). The second erm, ε + 1, idenifies he impac of new informaion received by all dealers beween he sar of periods and + 1. Equaion (3) shows ha new informaion impacs on he FX price quoed in period + 1 o he exen i revises 4

6 forecass of he presen value of fundamenals based on dealers common informaion. As an empirical maer, depreciaion raes are very hard o forecas, so he dynamics of spo raes are largely aribuable o he effecs of news. Here microbased models have a big advanage over heir radiional counerpars because heir rade-based foundaions provide deail on how news affecs spo raes. In paricular, as equaion (3) indicaes, micro-based models focus on how new informaion abou he fundamenals reaches dealers and induces hem o revise heir FX quoes. News concerning fundamenals can reach dealers eiher direcly or indirecly. Common knowledge (CK) news operaes via he direc channel. CK news conains unambiguous informaion abou curren and/or fuure fundamenals ha is simulaneously observed by all dealers and immediaely incorporaed ino he FX price hey quoe. In principle, macroeconomic announcemens (e.g. on GP, indusrial producion or unemploymen) could be a source for CK news, bu in pracice hey rarely conain much unambiguous new informaion. In fac, CK news evens appear raher rare. The indirec channel operaes via order flow and conveys dispersed informaion abou fundamenals o dealers. ispersed informaion comprises micro-level informaion on economic aciviy ha is correlaed wih fundamenals. Examples include he sales and orders for he producs of individual firms, marke research on consumer spending, and privae research on he economy conduced by financial insiuions. ispersed informaion firs reaches he FX marke via he cusomer order flows received by individual dealers. These order flows have no immediae impac on dealer quoes because hey represen privae informaion o he recipien dealer. The informaion in each cusomer flow will only impac on quoes once i is known o all dealers. Inerdealer order flow is cenral o his process. Individual dealers use heir privae informaion o rade in he inerdealer marke. In so doing, informaion on heir cusomer orders is aggregaed and spread across he marke. This process is known as informaion aggregaion. ispersed informaion is incorporaed ino dealer quoes once his process is complee. 5

7 Empirical Evidence The appeal of micro-based models is no solely based on heir heoreical foundaions. In marked conras wih radiional exchange-rae models, micro-based models have enjoyed a good deal of empirical success. Evans and Lyons (2002a) firs demonsraed heir empirical power when sudying he relaion beween depreciaion raes and inerdealer order flow a he daily frequency. In paricular, hey show ha aggregae inerdealer order flow from rading in he spo dollar/dmark marke on day d accouns for 64 percen of he variaion in he s d + depreciaion rae, Δ 1, beween he sar of days d and d + 1. This is a sriking resul because macro models can accoun for less han 1 percen of daily depreciaion raes. I is also readily explained in erms of equaions (2) and (3). Aggregae inerdealer order flow during day d rading provides a measure of he marke-wide informaion flow ha dealers use o revise heir quoes beween he sar of days d and d + 1. This conemporaneous relaionship beween depreciaion raes and inerdealer order flows appears robus. I holds for many differen currencies, and for differen currency-order flow combinaions (e.g., Evans and Lyons 2002b, Payne 2003 and Froo and Ramadorai 2005). I is also worh emphasizing ha order flow s impac on spo raes is very persisen. There is very lile serial correlaion in he daily depreciaion raes for major currencies, so he order flow impac on curren FX quoes persiss far ino he fuure. While consisen wih he idea ha dispersed informaion is impounded ino spo exchange raes via inerdealer order flow, hese resuls do no provide direc evidence on he ulimae source of exchange rae dynamics. According o microbased models, he analysis of cusomer order flows should provide he evidence. In paricular, if inerdealer order flows measure he marke-wide informaion flow ha carries he informaion concerning fundamenals originally moivaing cusomer orders, cusomer orders should also have explanaory power for depreciaion raes. This is indeed he case. Evans and Lyons (2004b) show ha a significan conemporaneous relaionship exiss beween depreciaion raes and he cusomer order flows of a single large bank. Moreover, he srengh of his relaionship 6

8 increases as we move from a one day o a one monh horizon. This, oo, is consisen wih micro-based models: A longer horizons, cusomer flows from a single bank should be a beer proxy for he marke-wide flow of informaion driving spo raes. Micro-based models also make srong empirical predicions abou he relaionship beween order flows and fundamenals. According o equaion (1), dealers are forward-looking when quoing FX prices, so spo raes embody heir forecass for fundamenals based on common informaion, Ω. One empirical implicaion of his observaion is ha spo exchange raes should have forecasing power for fundamenals. While here is some evidence ha his is rue for variables ha comprise fundamenals in many models (Engel and Wes 2005), he forecasing power is raher limied. Micro-based models also have implicaions for he forecasing power of order flows: If order flows convey informaion abou fundamenals ha is no ye common knowledge o all dealers (i.e., no in Ω ), hen hey should have incremenal forecasing power for fundamenals, beyond he forecasing abiliy any variable in Ω. This is a srong predicion: i says ha order flow should add o he forecasing power of all oher variables in Ω, including he hisory of spo raes and he fundamenal variable iself. Neverheless, Evans and Lyons (2004b) find ample suppor for his predicion using cusomer order flows and candidae fundamenal variables such as oupu, inflaion and money supplies. These findings provide direc evidence on he informaion conen of cusomer order flows, and provide a new perspecive on he link beween exchange raes and fundamenals. ispersed informaion concerning fundamenals need no only come from he aciviies of individuals, firms and financial insiuions. Scheduled announcemens on macroeconomic variables (e.g. GP, inflaion, or unemploymen) can also be a source of dispersed informaion. If agens have differen views abou he mapping from he announced variable o fundamenals, hen he news conained in any announcemen, while simulaneously observed, will no be common knowledge. For example, wo firms may inerpre he same announcemen on las quarer s GP as having differen implicaions for fuure GP growh. iffering inerpreaions abou 7

9 he implicaions of commonly observed news will be a source of cusomer order flows because hey imply heerogeneous views abou fuure reurns, which in urn, induces porfolio adjusmen. Thus, micro-based models raise he possibiliy ha he exchange rae effecs of macro announcemens operae via boh a direc channel (i.e., when he announcemen conains CK news) and an indirec channel. Love and Payne (2002) and Evans and Lyons (2003, 2005b) find evidence ha boh channels are operable. Evans and Lyons esimae ha roughly wo hirds of he effec of a macro announcemen is ransmied indirecly o he dollar/mark spo rae via order flow, and one-hird direcly ino quoes. Wih boh channels operaing, macro news is esimaed o accoun for more han one-hird of he variance in daily depreciaion raes. This level of explanaory power far surpasses ha found in earlier research analyzing he impac of macro news on exchange raes (e.g., Andersen e al. 2003). I also furher cemens he link beween spo raes and he macro variables comprising fundamenals. Order Flows, Reurns and he Pace of Informaion Aggregaion The process by which he informaion conained in he cusomer flows becomes known across he marke, and hence embedded ino FX quoes, is complex. The individual cusomer and inerdealer orders received by each dealer conain some dispersed informaion abou he economy, bu exracing he informaion from each order consiues a difficul inference problem. Under some circumsances, he inference problems are sufficienly simple for every dealer o learn all here is o know abou fundamenals in a few rounds of inerdealer rading. In his case, he pace of informaion aggregaion is very fas, so ha new informaion concerning fundamenals is quickly refleced in dealer quoes wheher he news is iniially dispersed or common knowledge. The resuling dynamics for exchange raes over weeks, monhs or quarers will be indisinguishable from he predicions of macro models. Under oher circumsances, he inference problem facing individual dealers is sufficienly complex o slow down he pace of informaion aggregaion. Here i 8

10 akes many rounds of inerdealer rading before he dispersed informaion concerning fundamenals becomes known across he marke. This scenario is much more likely from a heoreical perspecive. Evans and Lyons (2004a) show ha he condiions needed for fas informaion aggregaion are quie sringen. Of course, because inerdealer rading akes places coninuously, dispersed informaion could be compleely embedded in FX quoes in a shor period of calendar ime (e.g., a day), even if he pace of informaion aggregaion is slow. In principle, dealers migh be able o learn a good deal from he muliude of orders hey receive in a ypical day, even if individual orders are relaively uninformaive. The quesion of wheher i akes significan amouns of calendar ime before dispersed informaion is embedded in FX quoes can only be answered empirically. If he pace of informaion aggregaion is slow, cusomer order flows across he marke conain informaion ha will only become known o all dealers a a laer dae. So, if he cusomer orders received by an individual bank are represenaive of he marke-wide flows, hey should have forecasing power for he fuure markewide flow of informaion ha drives quoe revision. Recen empirical findings suppor his possibiliy. Evans and Lyons (2004b, and 2005) show ha cusomer order flows have significan forecasing power for fuure depreciaion raes boh in and ou of sample. These resuls are qualiaively differen from he conemporaneous empirical link beween order flows and depreciaions raes discussed above. In he conex of equaions (2) and (3), he marke-wide flow of informaion from period- rading impacs on he deprecaion rae, Δ 1, via ε + 1. The conemporaneous link arises because period- inerdealer order flows measure he marke-wide informaion flow, ε + 1. In conras, he forecasing power of cusomer flows for he depreciaion rae arises because ε + 1 conains informaion ha was originally in he cusomer orders received by individual banks before period- rading. These forecasing resuls are surprising boh in erms of heir horizon and srengh. In paricular, ou-of-sample forecass based on cusomer flows from monh 1 can accoun for roughly 16 percen of he variaion in nex monh s depreciaion s + 9

11 s + rae, Δ 1. This finding suggess ha he pace of informaion aggregaion is far, far slower han was previously hough; i seems o ake weeks, no minues, for dispersed informaion o be fully assimilaed across he marke. The level of forecasing power is also an order of magniude above ha usually found in exchange rae models. For example, he in-sample forecasing power of ineres differenials for monhly depreciaion raes is only in he 2 4 percen range. The slow pace of informaion aggregaion may shed ligh on one of he longsanding puzzles in exchange rae economics; he disconnec beween spo exchange raes and fundamenals over shor and medium horizons (Meese and Rogoff 1983). The idea is quie simple. If changes in fundamenals are only refleced in spo raes once informaion concerning he change is recognized by dealers across he marke, he slow pace of informaion aggregaion will mask he link beween he depreciaion rae and he change in fundamenals over shor horizons, because he laer is a poor proxy for he marke-wide flow of informaion. Simulaions in Evans and Lyons (2004a) show ha his masking effec can be quie subsanial. Fundamenals accoun for only 50 percen of variaion in spo raes a he wo-year horizon even hough informaion aggregaion akes a mos 4 monhs. One facor ha migh conribue o he slow pace of informaion aggregaion is he presence of price-coningen order flow generaed by feedback rading. Soploss orders, for example, represen a form of posiive feedback rading, in which a fall in he FX price riggers negaive order flow from cusomers wishing o insure heir porfolios agains furher losses. Feedback rading of a known form does no complicae he inference problem facing dealers because he orders i generaes are simply a funcion of old marke-wide informaion. However, when he exac form of he feedback is unknown, i makes inferences less precise and so slows down he pace of informaion aggregaion. Osler (2005) argues ha feedback rading will be an imporan componen of order flow when quoes approach he poins a which sop-loss orders cluser. A fall in FX quoes a hese poins can rigger a selfreinforcing price-cascade where causaion runs from quoes o order flow. 10

12 Some economiss argued ha he early empirical findings linking order flow and he depreciaion rae refleced he presence of posiive feedback rading raher han he ransmission of dispersed informaion. Indeed, here is no way o ell wheher inraday causaion runs from order flows o quoes or vice verse from jus he conemporaneous correlaion beween order flow and he deprecaion rae measured in daily daa. However, he new evidence on he forecasing power of order flow for boh depreciaion raes and fundamenals firmly poins o order flow as he conveyor of dispersed informaion. This is no o say ha feedback rading is absen. Porfolio insurance and oher price-coningen rading sraegies (e.g., liquidiy provision) undoubedly conribue o order flows and heir presence may acually explain why he pace of informaion aggregaion is so slow. Fuure Research Exchange rae research using micro-based models is sill in is infancy. The pas few years have seen a rapid advance in heoreical modeling and some surprising empirical resuls. Advances on he empirical side will be spurred by he greaer availabiliy of rading daa. On he heoreical side, micro-based modeling may provide new insighs ino he deerminans of he foreign exchange risk premium, he efficacy of foreign exchange inervenion, and he anaomy of financial conagion. 11

13 References Andersen, T., T. Bollerslev, F. iebold, and C. Vega (2003), Micro effecs of macro announcemens: Real-ime price discovery in foreign exchange, American Economic Review, 93: Bacchea, P., and E. van Wincoop (2003), Can informaion dispersion explain he exchange rae disconnec puzzle? NBER Working Paper 9498, February, forhcoming, American Economic Review. Engel, C., and K. Wes (2005), Exchange raes and fundamenals, Journal of Poliical Economy, 113: Evans, M., and R. Lyons (2002a), Order flow and exchange rae dynamics, Journal of Poliical Economy, 110: Evans, M., and R. Lyons (2002b), Informaional inegraion and FX rading, Journal of Inernaional Money and Finance, 21: Evans, M., and R. Lyons (2003), How is Macro News Transmied o Exchange Raes? NBER Working Paper 9433, January. Evans, M., and R. Lyons (2004a), A New Micro Model of Exchange Raes, NBER Working Paper 10379, March. Evans, M., and R. Lyons (2004b), Exchange Rae Fundamenals and Order Flow, ypescrip, Georgeown Universiy, available a hp:// Evans, M., and R. Lyons (2005a), o Currency Markes Absorb News Quickly? Journal of Inernaional Money and Finance, 24: Evans, M., and R. Lyons (2005b), Meese-Rogoff Redux: Micro-Based Exchange Rae Forecasing, American Economic Review P&P, May. Froo, K., and T. Ramadorai (2005), Currecny Reurns, Inrinsic Value, and Insiuional-Invesor Flows, Journal of Finance, LX:

14 Hau, H., and H. Rey (2002), Exchange raes, equiy prices, and capial flows, NBER Working Paper 9398, ecember, Review of Financial Sudies, forhcoming. Love, R., and R. Payne (2002), Macroeconomic news, order flows, and exchange raes, ypescrip, London School of Economics, ecember. Lyons, R. (1997), A Simulaneous Trade Model of he Foreign Exchange Ho Poao, Journal of Inernaional Economics, 42: Meese, R., and K. Rogoff (1983), Empirical exchange rae models of he sevenies, Journal of Inernaional Economics, 14: Osler, C. (2005), Sop-loss orders and price cascades in currency markes, Journal of inernaional Money and Finance, 24: Payne, R. (2003), Informed rade in spo foreign exchange markes: an empirical invesigaion, Journal of Inernaional Economics, 61: