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

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

2 Introducton Electronc Brokerage n Foregn Exchange 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% 2

3 Introducton Tradtonal Drect Dealng Electronc Brokerage Allows for contnuous multlateral nteracton Allows for greater transparency 3

4 Outlne 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 4

Choce of tradng venue for large and small traders

5 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 5

6 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 6

7 Theoretcal Model Asset (Currency) a random future value v Expectaton E ( v) = u Varance Payoff DM: CN: u s σ v δ ( u c) 7

8 Effectve Dscount Rate Effectve Dscount Rate: For a small trader: δ s = Eβ t δ = Eβ l For a large trader: β : dscount factor, t s t l l 0 < β <1 : number of perods t takes for a small trader to fnd a match : number of perods t takes for a large trader to fnd a match t s t F () t F () t l ts Et Et, Eβ Eβ l s l s 8

perods t takes for a small trader to fnd a match : number of perods t takes

9 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 9

10 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) 10

11 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 11

Avalable Informaton: order type, order entry tme, removal

12 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 12

longer for unsuccessful lmt orders than flled lmt

13 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

0012 2.3782 Std Devaton 10.8107 18.6327 0.0008 14.9453 Range 398.394 802.6677 0.

14 Table 3 Intradaly Pattern of Duraton Tme of Day Average Duraton Number of Orders Percentage % % % % % % % % % % % % % % % % % % % % % % % % 14

43% 10 3.2417 7360 5.64% 11 2.1809 13006 9.96% 12 1.5406 16790 12.86% 13 1.4885 18976 14.54% 14 1.5596 14518 11.12% 15 2.391 6416 4.92% 16 4.

15 Estmaton of duraton model Three Hypotheses Sze Effect Prce Impact Lqudty Effect 15

16 ACD model ACD Model Duraton Condtonal duraton s an IID error sequence ε x EACD (flat hazard functon) x =ψ ε ψ Webull ACD (monotone hazard functon) 16

17 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 17

18 18 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 =

Γ + Γ + Γ = + f κ κ κ κ ξ σ ξ κ θ x x x x x h + = 2 1 1 1 1 )

19 Representatve Hazard Functons 2.5 Hazard Functon Webull 0.5, 0 Burr 2, Duraton 19

20 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 20

21 Log ACD Model Log-ACD Model Duraton x = exp(ψ ) ε x ψ ε : 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(

22 Model Estmaton Only completely flled lmt orders ncluded 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 22

23 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 23

24 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 24

25 Unrestrcted Model Coeffcent Std. Error T-Stat Prob ω x ) ln( 1 ψ SIZE DummyBP DummyBN DummySP DummySN LDEPTH MDEPTH κ σ Mean Log-lkelhood:

26 Restrcted Model Coeffcent Std. T-Stat Prob Error ω x ) ln( 1 SIZE DummyBP DummyBN DummySP DummySN LDEPTH MDEPTH κ σ Mean Log-lkelhood:

27 Estmated Hazard Functon Hazard Functon Duraton Burr, ,

28 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. 28

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

The Choice of Direct Dealing or Electronic Brokerage in Foreign Exchange Trading The Choce of Drect Dealng or Electronc Brokerage n Foregn Exchange Tradng Mchael Melvn Arzona State Unversty & Ln Wen Unversty of Redlands MARKET PARTICIPANTS: Customers End-users Multnatonal frms Central