The Choice of Direct Dealing or Electronic Brokerage in Foreign Exchange Trading

Transcription

1 The Choce of Drect Dealng or Electronc Brokerage n Foregn Exchange Tradng Mchael Melvn Arzona State Unversty & Ln Wen Unversty of Redlands

2 MARKET PARTICIPANTS: Customers End-users Multnatonal frms Central banks Hedge funds... Tradtonally trade wth dealers, not each other Trades prvate nfo to dealers 2

3 MARKET PARTICIPANTS: Dealers Trade wth customers Low transparency Trade wth each other nterbank market Multple of customer trades Passng hot potato postons 3

4 INTERBANK TRADING Snce 1930s, drect telephone tradng Snce 1960s voce brokers speaker boxes 1987, Reuters Dealng Untl early 1990s, trade splt almost n half between drect dealng and voce brokers 4

5 ELECTRONIC BROKERS 1992, Reuters Dealng , Mnex and EBS 1995, EBS/Mnex merger 5

6 ELECTRONIC BROKERS Market and Lmt orders prce/tme prorty Anonymous pror to trade Lower costs Greater transparency Contnuous multlateral nteracton 6

7 CUSTOMER INTERNET TRADING Nonbank stes Take prces from nterbank market Not elect. Brokers, ste s counterparty to trades May 1996, Deal4Free (CMC Group) March 2001, OANDA Bank stes Request quotes from several banks August 1996, FX Connect (State Street) Aprl 2000, Currenex Multple bank quotes and crossng network Increases competton and lowers costs 7

8 ELECTRONIC BROKERS Start from a base of zero n 1992 Aprl 2001 Aprl 1998 FRB of NY 54% 32% Bank of England 66% 30% Bank of Japan 48% 37% 8

9 Queston: How would a trader choose when facng two competng tradng venues? Theoretcal model Choce of tradng venue for large and small traders Emprcal Analyss Tests hypotheses Summary & Dscusson 9

10 Tradng Venues Drect Dealng (DD) Immedacy of transacton Electronc Brokerage (EB) Watng tme dscount factor δ Transacton cost s (dealer s bd-ask spread) Transacton cost c c<s 10

11 Theoretcal Model Players One large trader who trades a large amount Many small traders who trade 1 unt Strateges Go to DD Go to EB Don t trade 11

12 Theoretcal Model Asset (Currency) a random future value v Expectaton E ( v) = u Varance Payoff DM: CN: u s σ v δ ( u c) 12

13 Effectve Dscount Rate Effectve Dscount Rate: For a small trader: For a large trader: : dscount factor, δ s = Eβ t δ = Eβ l l β : number of perods t takes for a small trader to fnd a match t s : number of perods t takes for a large trader to fnd a match t l t F () t F () t l ts Et Et, Eβ Eβ l s l s t s 0 < β <1 13

14 Optmal Decson Rules Trade wth DD f u s > δ ( u c), and u s >0 Trade wth EB f u s< δ ( u c), δ ( u c) >0 Indfferent f u s = δ ( u c) >0 No trade f δ ( u c) <0, u s <0 14

15 Optmal Outcome u<c nobody would trade c<u<s exclusve EB tradng u>s two possble equlbra when DD & EB coexst The large trader trades wth DD and small traders go to the EB. ( s δ lc) /(1 δ l ) < u < ( s δ sc) /(1 δ s ), δ s > δ l; The large trader trades on EB and small traders trade wth DD. (ruled out) 15

16 Emprcal Analyss Data Descrpton Reuters D electronc brokerage Mark/Dollar Oct 6-10, 1997, 130,535 orders Avalable Informaton: order type, order entry tme, removal tme, removal code, prce, quantty ordered and quantty dealt 16

17 Duraton tme of orders Average duraton for lmt orders s longer than that for market orders Mean watng tme s longer for unsuccessful lmt orders than flled lmt orders Tme of day effect Clusterng n the duraton data 17

18 Descrptve Statstcs for Duraton Flled Lmt Orders Faled Lmt Orders Flled Market All sample Orders Number of Orders Mean (mn) Std Devaton Range Skewness Kurtoss

19 Table 3 Intradaly Pattern of Duraton Tme of Day Average Duraton Number of Orders Percentage % % % % % % % % % % % % % % % % % % % % % % % % 19

20 Estmaton of duraton model Three Hypotheses Sze Effect Prce Impact Lqudty Effect 20

21 ACD model ACD Model Duraton Condtonal duraton ε x ψ s an IID error sequence EACD (flat hazard functon) Webull ACD (monotone hazard functon) x =ψ ε 21

22 ACD model Burr ACD model Inverted U-shaped Hazard functon Hazard functon ncreasng for small duraton and decreasng for long duraton Nests EACD and WACD model as specal cases 22

23 Burr-ACD Burr-Dstrbuton: Densty Functon: Hazard Functon EACD WACD ) 1 1 ( ) 1 (1 1) 1 ( ) ( ) ( 2 2 ) 1 (1 2 κ σ κ σ σ ψ ψ κ Γ + Γ + Γ = + f κ κ κ κ ξ σ ξ κ θ x x x x x h + = ) ;,..., ( γ γ ),..., ( = x x x x h x x x h ψ 1 ),..., ( 1 1 = 23

24 Representatve Hazard Functons 2.5 Hazard Functon Webull 0.5, 0 Burr 2, Duraton 24

25 ACD model Concerns: Dependent Varable: Condtonal duraton Rght hand sde of estmaton equaton needs to be postve Non-negatvty constrants on the coeffcents of exogenous varables 25

26 Log ACD Model Log-ACD Model Duraton x = exp(ψ ) ε ψ : Logarthm of condtonal duraton ε s an IID sequence as n ACD model. Log-ACD(1,1) specfcaton ψ x 1) βψ = ω + α ln( + 1 x ε 26

27 Censorng Potental bas from gnorng unflled orders or partal flls Estmate jont lkelhood n c 1 c f(x ;X ) g(x ;X ) = f(x ;X ) g(x ;X ) = 1 F C 27

28 Model Estmaton Over peak European busness hours 8:00am 5:00 pm GMT Varables SIZE: Quantty submtted n mllons of dollars PRICEDIF: submsson prce - last transacton prce DEPTH: depth of order book 28

29 Model Estmaton Dummy varables DummyBP 1 for buy orders wth prcedf>0; 0 otherwse DummyBN 1 for buy orders wth prcedf<0; 0 otherwse DummySP 1 for sell orders wth prcedf>0; 0 otherwse DummySN 1 for sell orders wth prcedf<0; 0 otherwse 29

30 Model Estmaton Burr Log-ACD (1,1) model ψ + + δ δ x = ω + α ln( + βψ DummyBP DummySP 1) + + δ δ δ 1 DummyBN DummySN SIZE + δ 5 DEPTH 30

31 Model Estmates (flled orders) Coeffcent Std. Error T-Stat Prob SIZE DummyBP DummyBN DummySP DummySN LDEPTH MDEPTH

32 Model Estmates (censored orders) Coeffcent Std. Error T-Stat Prob SIZE DummyBP DummyBN DummySP DummySN LDEPTH MDEPTH

33 Estmated Hazard Functon Hazard Functon Duraton Burr, ,

34 Conclusons Explan choce of tradng venues Large traders prefer drect dealng whle small traders utlze the electronc brokerage Emprcal results consstent wth hypotheses from theory. Large orders wat longer on EB gven the depth of the market and prce compettveness. 34