Intermediaries as Information Aggregators:

An Application to U.S. Treasury Auctions

Nina Boyarchenko, David Lucca and Laura Veldkamp

Federal Reserve Bank of NY and NYU Stern School of Business

December 2014

The views expressed here are those of the authors and do not necessarily

reflect those of the Federal Reserve Bank of New York or the Federal

Reserve System

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Motivation

Why do investors operate through intermediaries?

In standard theories, intermediaries ameliorate financial frictions:

- lower information asymmetries (monitoring and screening

borrowers)

- offer diversification/leverage/maturity transformation

Rationales do not apply to Treasury auctions

- Intermediaries observe client order flows and advise them

- This paper ⇒ intermediaries are information aggregators

Study effect of intermediation on auction revenues

2 / 17
Outline

Start with a simple framework: A menu auction of financial

assets, with heterogeneous information about asset value

New twist: Intermediaries (primary dealers) observe order flow,

share average info with clients, and bid on their own account

Calibrate model to Treasury auction results

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Effect of intermediation

Gate-keeping intermediaries (e.g. a “full commitment” IPO):

Reduce expected auction revenue

Reduce revenue variance

Information intermediaries have the opposite effect:

Increase expected auction revenue

Increase revenue variance

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Institutional detail

Competitive (price-contingent) and non-competitive bids (retail

and FIMA)

Clearing rate set by first accepting non-comp bids, then comp bids

in ascending rate order up to offered amount

PDs account for large shares of allotted amounts

Explicit/implicit minimum bidding requirements

Other institutional investors can bid directly or indirectly

Most investors’ bids are placed indirectly

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Allotted shares by bidders

0

.2

.4

.6

.8

1

Apr08 Apr09 Apr10 Apr11 Apr12 Apr13 Apr14

PD Indirect Direct Non-competitive

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Number of primary dealers

1992 PD Operating Policy

1998 PD Scorecard

2010 PD Operating Policy

0

10

20

30

40

50

Jan60 Jan65 Jan70 Jan75 Jan80 Jan85 Jan90 Jan95 Jan00 Jan05 Jan10 Jan15

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Basic model

N investors are evenly assigned to 1 of D dealers

All have exponential utility − exp(ρjWj)

ρjis ρD for dealers ρ for investors and

Wj = W0 − qjp + qjf

Future value of security f ∼ N(μ, τ

−1

f

)

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Model structure

Type Information Decisions Strategic Demand

Market orders Non-competitive x ∼ N



0, τ−1

x



Investors (N) si, s ̄, p Bidding Price-takers qi (p|si

,s ̄)

Dealers (D) s ̄, p Bidding Strategic qd (p|s ̄)

Large invest. (1) sL, s ̄, p Bidding; intermediationStrategic qL (p|sL,s ̄)

Each investor has a signal

si = f

|{z}

“fundamental”

+ εi

|{z}

“noise”

; εi ∼ N(0, τ

−1

ε

)

Dealers disseminate average s ̄jto their clients

s ̄j = f + ε ̄j; ε ̄j ∼ N



0, D/Nτ

−1

ε



⇒ Dealers aggregate information (reduce uncertainty)

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Model structure

Type Information Decisions Strategic Demand

Market orders Non-competitive x ∼ N



0, τ−1

x



Investors (N) si, s ̄, p Bidding Price-takers qi (p|si

,s ̄)

Dealers (D) s ̄, p Bidding Strategic qd (p|s ̄)

Large invest. (1) sL, s ̄, p Bidding; intermediationStrategic qL (p|sL,s ̄)

Large, strategic investor chooses between bidding directly or

through a dealer

Trade-off: gain access to s ̄ but disclose sL to dealer

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Model intuitions

Optimal bids q(p) condition on information in realized price p

Equilibrium price:

p = A + B (f + ε ̄)

| {z }

s ̄

+Cx (1)

Investors use p to learn about f but

Not perfectly revealing of s ̄ because of market orders x

More dealers ⇒ less precise s ̄ ⇒ price less informative about f

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Basic model solution

Investors bid

qi(p) = E[f |si

,s ̄, p] − p

ρV[f |si,s ̄, p]

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Basic model solution

Dealers bid

qd (p) =

E[f |s ̄, p] − p

ρDV[f |s ̄, p] + dp/dqd

Having a dealer lowers payoff uncertainty:

V[f |si,s ̄, p] < V[f |si, p]

Increasing the number of dealers

Makes dealers less strategic: lowers dp/dqd

⇒ Dealers less sensitive to information.

Inhibits information aggregation: precision of s ̄jfalls, V[f |si,s ̄, p]

rises

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Calibration

Assume investors hedge interest rate risk by shorting a replicating

portfolio of off-the-runs (from a 1pm estimated yield curve)

Net revenue measure is the price of the on-the-run minus

off-the-run portfolio

Match target parameters:

Coefficient of the estimated equilibrium pricing equation:

p = −17[4.7] + .97[.03]f + 124[34]x

Other parameters: mean allotted shares by direct, indirect, dealer

and non-competes (including “imputed” FIMA), mean and

standard deviation of auction/issue price

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Effect of one vs. no dealer

55 60 65 70 75 80 85 90 95 100 105

−100

−80

−60

−40

−20

0

Fundamental uncertainty (τ

f

−1/2, bps)

Expected excess revenue (bps)

1 Dealer

Competitive

55 60 65 70 75 80 85 90 95 100 105

10

20

30

40

50

60

70

80

Fundamental uncertainty (τ

f

−1/2, bps)

St. dev. of excess revenue (bps)

1 Dealer

Competitive

Less uncertainty with information aggregation

⇒ Higher revenues

⇒ More sensitivity to information ⇒ more volatility

Effect of information intermediaries is opposite to IPO

underwriters

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Changing the number of dealers

5 10 15 20 25 30 35 40 45 50

−10

−5

0

5

10

15

20

25

30

Number of dealers

Expected excess revenue (bps)

τ

f=0.5*Baseline

Baseline

τ

f=2*Baseline

5 10 15 20 25 30 35 40 45 50

20

30

40

50

60

70

80

Number of dealers

St. dev. of excess revenue (bps)

τ

f=0.5*Baseline

Baseline

τ

f=2*Baseline

Adding dealers: increases competition, total demand but

disaggregates information

⇒ Higher revenues because of first two effects

⇒ More uncertainty lowers information sensitivity ⇒ lower volatility

Work-in-progress on separating effects (only varying information

aggregation ⇒ both revenue/volatility decrease)

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Intermediation choice

Large investors bid indirectly for intermediate number of dealers

- Few dealers: dealer demand very sensitive to information, so

optimal for large investor not to disclose signal

- Many dealers: dealers have less precise information

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Minimum bidding requirements

Primary dealers have minimum bidding requirements:

Post 2010 Operating Policies: pro-rata share of offered amount with

“reasonable” bids to market

A dynamic constraint: high bids in some auctions relax constraint

in future auctions

⇒ Introduce low bidding penalty χ

Without penalty:

qd (p) =

E[f |s ̄, p] − p

ρDV[f |s ̄, p] + dp/dqd

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Minimum bidding requirements

Primary dealers have minimum bidding requirements:

Post 2010 Operating Policies: pro-rata share of offered amount with

“reasonable” bids to market

A dynamic constraint: high bids in some auctions relax constraint

in future auctions

⇒ Introduce low bidding penalty χ

With penalty

qd (p) =

E[f |s ̄, p] − (1 − χ) p

ρDV[f |s ̄, p] + (1 − χ) dp/dqd

Higher χ lowers strategic component of demand but also price

elasticity

⇒ Higher auction revenue but higher volatility

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Conclusions

Present a theoretical framework to capture key institutional

features of Treasury auctions

Intermediaries aggregate information:

⇒ Intermediation results in higher revenues but also higher variance

⇒ Increasing the number of intermediaries raises competition but

disaggregates information

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