The New Micro Approach

to Exchange Rates

A 15-Hour Course

Richard K. LyonsU.C. Berkeley and NBER

faculty.haas.berkeley.edu/lyons

Updated August 2003

Instructor Course Notes

Slides: These slides are not intended as a stand-alone reflection of the course (e.g., though they include various graphics from the textbook, full discussion of those graphics is not included). For a course description, see the course syllabus, which is available from my website. All figures and tables from the textbook are also at my website (pdf).

Problem Set: The course works best with a problem set (e.g., the one available from my website).

Case: The course works best with a case as well. For example, distribute order flow data from the FX spot or futures markets for students to examine links between exchange rates and those flows. See my website for access to FX order flow datasets.

Outline: 5 Three-Hour Sessions

Session 1: Intro to New Micro Approach– Course overview– Exch. rate economics overview– 5 upfront concerns about new approach– Order flow and price

Session 2: Information Theory and Causality– Theory overview– Kyle model– Glosten-Milgrom model– Information types

Session 3: Identifying Information in Data– Empirical overview– Statistical approach (micro)– Structural approach (micro)– Macro empirics and causality

Outline: 5 Three-Hour Sessions

Session 4: Marketmakers vs End Users– Customer data versus dealer data– Theory for customer data– Empirics of customer data– Case: Application to currency futures

Session 5: Applications and Next Frontiers– Policy: intervention, taxes, transparency,

etc.– Recent research – Open questions

Intro to New Micro Approach

Two Big Questions:

(1) What is the nature of the information this market is aggregating?

(2) How does it achieve this aggregation?

Modern Exch. Rate Economics

Modern ER Econ.

New Micro

Micro-founded

New Macro

Non-rational

•Focus: supply side of real econ.

•Info structure: CK (com. knowl.)

•Disconnect Q: why ER so little macro impact?

•Focus: info econ. of fin. markets

•Info structure: dispersed info

•Disconnect Q: why macro so little ER impact?

•Focus: sub-opt. behavior

•Approach: trending from noise, feedback, & chartism to behavioral econ.

Order Flow: Information Vehicle

(1) Definition: signed transaction flow – Buyer initiated minus seller initiated– Quoting marketmaker (MM): non-initiating side

– If auction, limit order is non-initiating

(2) Not same as demand– OF measures transactions (i.e., demands after

price has adjusted)– Unlike net demand, cum. OF may ≠ 0

– In some models cum. OF follows RW – Price impact differs depending on trader

identity – Link to info econ: whose trades info rich?

(3) Another perspective: Econometric Identification

– How ID demand and supply curves?– Think of scatter in P-Q space– Exclusion restrict.: shifts (vs moving along)

– Microstructure theory: IDs shifting

Order Flow’s Role Graphically

Macro Approach

Hybrid

Microstructure Approach

Public info Price

Private info

Price

Order flow

Information

Price

Order flow

Upfront Concerns about New Micro

“OF is just demand”– Addressed on last slide: not true– Demand is what moves price in macro models,

not transactions: at adjusted prices, demand shifts need not induce any trades

“Two sides to every trade, so what learn?”– True, but one side may be a demand curve shift, the other side price-induced move along curve

– Price needs to impound any info in the shift

“OF information is not fundamental”– Even if term defined narrowly to mean money

and income (M and Y), not necessarily true– OF can reflect (rationally) changing

expectations of future M and Y– Even if not reflecting expectations of M and Y,

OF can convey info about mkt-clearing risk premia (a la Portfolio Balance models)

– PB effects not traditionally called fundamental

– As empirical question, remains open

Upfront Concerns (2)

“Order flow effects on ER do not persist”– Theory: persistence depends on info type (more later)

– Distinguish nominal ER effects from real– Data: much evidence that nominal ER effects do persist

(more later; see also text pages 22-26).– Plots (text page 251) are not consistent with

impact that fades in months: levels would not track over years unless impact persists over years

– Profits: Rapid mean reversion of OF effects would imply trading strategies so profitable that they’re unrealistic

“Causality may be reversed”– Some reverse causality is almost surely present,

particularly during market stress (more later)– But on average, what feedback trading there is in FX

data appears to be negative (EL 2001,2002; Tien 2002)– So feedback does not account for the correlation

between OF and ER changes, which is strongly positive

– Perspective: Even if causality is equally important in both directions (extreme), explanatory power of order flow for ER changes is roughly 10 times that of macro empirical models

What Info Drives Order Flow?

4 Lines of Empirical Attack (so far)

(1) Macro news: OF less important then? – Empirical: may be more important—helps

market aggregate differential interpretations

(2) Disaggregate OF: identity matters?– Empirical: differential price impact across trader

types shows which types best informed

(3) Cross Currency: $/€ trades info for $/¥?– Empirical: pattern of cross-market effects

shows whether info specific to $, €, or ¥

(4) Macro expectations: OF proxy changes?– Price depends expected future macro– OF measures expectational “votes” over time?

Information Theory Overview

Info models of trading focus on:

E[V|Order flow]

where V is payoffs to holding asset (future divs for stocks, future interest differentials for FX).

Two Canonical Models

Kyle Model: E[V|batched orders]– Auction market structure

G-M Model: E[V|individual orders]– Dealer market structure

Kyle Auction Model

3 insights (text p. 77)

Model summary (figure 4.6)

Model timing (figure 4.5)

Intuition for equilibrium (82-84)

3 discussion points (85-87)

Glosten-Milgrom Dealer Model

3 insights (text p. 87)

Model summary (figure 4.8)

Model timing (figure 4.7)

Intuition for equilibrium (89-91)

3 discussion points (92-93)

Taxonomy of Information Types

Traditional split: public vs private– Public: to be impounded in price without

order flow role, not only must data be commonly observed, but traders must also agree on ER implications (i.e., plenty of room for OF role).

– Private: in fact, many sub-categories within

Private information 2x2

Actionlikely here?

Concentrated

Risk Related

Payoffs

Dispersed

Note: in a risk neutral world, only the payoff column is relevant.

Empirical Overview

Micro empirics: 3 basic approaches

– VAR (statistical)– Trade indicator (statistical)– Dealer problem (structural)

In FX, shift to electronic trading and associated data has enabled empirical work not possible 10 years ago.

– New micro is only approach among the 3 reviewed in first session that is empirically driven.

Existing data sets: see text pages 114-126.

VAR Empirical Approach

2 key assumptions (text page 128)

2 equations (equations 5.1 and 5.2)

Measuring OF’s info role (eqs. 5.6 and 5.9)

Structural Empirical Approach

Figure 5.6 (page 140) conveys economics

Table 5.1 (140) presents estimates– Coefficient on OF, Xjt, captures info effect

Emphasis: liquidity measured by price impact

Macro Empirics and Causality: Order Flow to Price?

3 Possibilities

(1) Order flow to price– Basis microstructure theory: OF transmits information

(NCK info, non common knowledge)– NCK information is ultimate cause, OF proximate:

causality runs from infoOFprice

(2) 3rd factor drives both–no caus. between– CK news: good for $ causes $ apprec. & OF>0?

– Inconsistent with rational expectations: immediate price adjustment to CK news eliminates incentive for OF>0 at new price

– News interpreted differently: causal role for OF restored– OF is how price setters learn about unforecastable

part of interpretations

(3) Price to order flow– Positive feedback ($ apprec. causes $ buying)?– Theory: Has to hold on average, not just for some traders.

Also, no rationale given empirical return autocorr. 0.– Empirical: feedback in FX negative, if anything (EL

2001,2002, Tien 2002). Even controlling for feedback, OF retains powerful causal role.

Sources of Exch. Rate Variation

(1) Public news impounded immediately and directly

(2) Public news impounded via OF

(3) OF unrelated to macro news (à la EL JPE 2002)

(4) Don’t know

Total Variation*

10%

20%

40%

30% _______100%

* See EL “How is Macro News Transmitted to Exchange Rates” for estimation details.

Sources of Exch. Rate Variation

(1) Public news impounded immediately and directly (a la traditional models)

– In data: 10% of total variation

(2) Public news impounded via OF– In data: 20% of total variation

(3) OF unrelated to macro news (a la EL JPE 2002)

– In data: 40% of total variation

(4) Don’t know– Remainder: 30% (see EL “How is Macro

News Transmitted to Exchange Rates?” for details)

Marketmakers vs End Users

Empirical noted above is marketmaker trades– Trade type pie: figure 9.1 (p. 245)– Which type is location of price discovery?– Which type is more primitive?

Important side point: OF ≠ capital flow

3 key hypotheses– Marketwide net to 0?– Single bank net 0 plus random error?– Single bank OF uncorrelated with ER?

Latter 2 soundly rejected– Figure 9.2 (p. 251)– Table 9.3 (p. 255)

Case 1: October 1998 $/¥

Rate moved > 10% in about 24 hours

Setting: No particular macro news – Russian default: August 1998– LTCM crisis: September

– Credit crunch faced by most leveraged players: speculative de-leveraging

Why early October?– Received wisdom: rush of hedge funds

unwinding yen carry trade– Subsidiary question: even if true, should

we call this fundamental?

Data: unlev. Fin. institutions key (p. 258-9) – How much distressed selling of $?– If distressed selling, expect in this segment?

Case 2: Weekly Flows in FX Futures

Data distributed in advance– 3 years weekly data on position changes of

participants self-identified as “speculators”

Basic questions from regressions– What is estimated price impact per $1

billion?– How compare to estimates from spot

market (e.g., text pages 187 and 254)?– Does variation of price impact across

contracts square with liquidity differences across mkts?

– Does price discovery occur in the futures market?

Do the 3 key hypotheses for end-user data apply here?

Policy Implications: Overview

Text chapter 8 and pages 272-275

Central Bank Intervention– Figure 8.3 (page 228)– Can CB trades mimic private trades (and

therefore private trade price impact)?– Why aren’t the channels through which

private trades affect price also operative for CB trades?

Fixed rate defenses Institution design in emerging mkts International currencies (€ rival $?) Transaction taxes

Policy: Can Spreads Info Efficiency?

3 Answers 3 Underlying Views

(1) Yes: Tobin tax argument

(2) No ER effects too short lived

(3) Yes: Info in flow process lost

Question: Does cause of spread matter?

Policy: Market Liquidity

3 Drivers of Market Liquidity

(1) Marketmaker mgmt of inventory risk

(2) Marketmaker mgmt of asym. info risk

(3) Other marketmaker costs (back office, technology, salaries, rents, etc.)

Question: Was decrease in $/€ liquidity (relative to previous $/DM liquidity) due to increase in (1) as argued by Hau, Killeen, and Moore? (See their 2002 Economic Policy article.)

Recent Research

Discussion of papers in supplemental reading packet.– Empirical papers: which approach? Data

from which trading segment? Main results?– Theory papers: which approach? Motivated

by which empirical results? Main results?

Gaps in the literature (text pages 268-270)– What we know– What we are likely to soon know

Overarching theme: information economics – Before New Micro approach, ER research

quite narrow in its consideration of richer information environments