Discussion of

“Intermediaries as Information Aggregators: An

Application to U.S. Treasury Auctions”

by Nina Boyarchenko, David Lucca, and Laura Veldkamp

Haoxiang Zhu

MIT Sloan School of Management and NBER

December 5, 2014

Roundtable on Treasury Markets and Debt Management

Haoxiang Zhu (MIT Sloan) Discussion of “Intermediaries as Information Aggregators” December 2014 1
Main idea

Central idea: Why bid through dealers in Treasury auctions? Because

dealers aggregate investors’ information.

Figure 1: Allotted shares by bidders across all auctions

0

.2

.4

.6

.8

1

Apr08 Apr09 Apr10 Apr11 Apr12 Apr13 Apr14

datem

PD Indirect Direct Non-competitive

Source: Treasury auction results.

Source: Boyarchenko, Lucca and Veldkamp (2014)

Haoxiang Zhu (MIT Sloan) Discussion of “Intermediaries as Information Aggregators” December 2014 2
05 06 07 08 09 10 11 12 13 14

0

5

10

15

20

25

30

35

40

45

Auction Date

Billions

Competitive Accepted Amounts in 5−Year Treasury Auctions

Dealer

Indirect

Direct

Haoxiang Zhu (MIT Sloan) Discussion of “Intermediaries as Information Aggregators” December 2014 3
Main results

1 The value of having a dealer (versus having no dealer)

I Having one dealer leads to higher expected revenue and revenue

variance than having no dealer.

2 The effect of the number of dealers on expected revenue and variance

I Numerically, increasing the number of dealers increases expected

revenue and reduces revenue variance. (Effects of various channels are

not clear yet.)

3 How investors choose between direct versus indirect bidding

I Numerical illustration of when a large investor bids directly or

indirectly, and the effect on revenue.

4 The effect of imposing minimum bid requirement

I Numerically, a higher low-bid penalty increases expected revenue and

revenue variance.

Haoxiang Zhu (MIT Sloan) Discussion of “Intermediaries as Information Aggregators” December 2014 4
A thought-provoking paper with relevant and interesting questions

Analysis is still at early stage.

My discussion will focus on the model and alternative approaches.

Haoxiang Zhu (MIT Sloan) Discussion of “Intermediaries as Information Aggregators” December 2014 5
Baseline model

The model is similar to the classic noisy rational expectation equilibrium.

The asset (Treasury bond) has unknown value f ∼ N(μ, τ −1

f

).

The asset supply is 1 − x, where x ∼ N(0, τ −1

x

).

N nonstrategic investors bid in a Treasury auction using demand

schedules (price-quantity pairs), either directly or through a single

primary dealer.

Each investor i receives a signal si = f + i, where i ∼ N(0, τ −1



).

Everyone has exponential utility.

The primary dealer gathers investors’ information (s1,s2, . . . ,sN) and

reveals to investors the average  ̄s ≡

P

i

si/N.

The dealer acts as an extra bidder, using investors’ information  ̄s.

Haoxiang Zhu (MIT Sloan) Discussion of “Intermediaries as Information Aggregators” December 2014 6
Comment 1: The nature of information

What is the nature of proprietary information {si}—asset

fundamentals, inventories, or order flows?

Pre-auction learning is useful if the noisy supply 1 − x has a large

variance. But in the data noncompetitive bids are very small.

Common versus private values. Hortacsu and Kastl (2012) find no

evidence that dealers in Canadian Treasury markets learn fundamental

value from customers’ bids. They cannot reject the null hypothesis of

private values.

Haoxiang Zhu (MIT Sloan) Discussion of “Intermediaries as Information Aggregators” December 2014 7
Model horse race:

Model Common value Private value 1 Private value 2

Marginal value Constant Decreasing Constant

E[f | {sj}] si − λqi si

Mechanism Divisible Divisible Indivisible

Bidders wish to learn f Nothing P(winning)

Noisy supply matters? Yes No –

Effect of disclosing  ̄s:

Bidding strategy Yes No Yes

E[Revenue] ↑ None None if risk-neutral

↑ if risk-averse (?)

Var[Revenue] ↑ None ?

Haoxiang Zhu (MIT Sloan) Discussion of “Intermediaries as Information Aggregators” December 2014 8
Model horse race:

Model Common value Private value 1 Private value 2

Marginal value Constant Decreasing Constant

E[f | {sj}] si − λqi si

Mechanism Divisible Divisible Indivisible

Bidders wish to learn f Nothing P(winning)

Noisy supply matters? Yes No –

Effect of disclosing  ̄s:

Bidding strategy Yes No Yes

E[Revenue] ↑ None None if risk-neutral

↑ if risk-averse (?)

Var[Revenue] ↑ None ?

Model selection is informed by data.

Haoxiang Zhu (MIT Sloan) Discussion of “Intermediaries as Information Aggregators” December 2014 8
Comment 2: Investors’ strategic incentives

Now back to the common-value model of the paper.

Sharing information creates a free-riding problem. Do investors want

to acquire the information at all, and under what conditions?

Investors are not strategic traders in the model. In reality they can

trade strategically by

I Taking into account the price impact and “bid shading”

I Partially disclosing their information to dealers, e.g. splitting the order

between indirect bidding and direct bidding

Example: Bidder 1 receives si, reports s

0

i

to dealer, bids indirectly based

on (P

j6=i

sj,s

0

i

), and bid directly based on si − s

0

i

.

Haoxiang Zhu (MIT Sloan) Discussion of “Intermediaries as Information Aggregators” December 2014 9
Suggestions:

1 Consider the cost of information acquisition.

2 Let investors be strategic.

3 Solve the binary {direct bidding, indirect bidding} problem for each

investor, assuming truthful reporting of siin indirect bidding.

I The paper studies the direct-versus-indirect choice of a single strategic

large investor and provides numerical illustrations.

4 If possible, allow splitting between direct and indirect bidding, with

partial information sharing with the dealer.

5 Some empirical tests?

Haoxiang Zhu (MIT Sloan) Discussion of “Intermediaries as Information Aggregators” December 2014 10
E.g., numerically, the paper finds:

Higher fundamental uncertainty ⇒ Indirect bidding is more likely.

This seems to hold post-2008, but not in a longer sample.

Haoxiang Zhu (MIT Sloan) Discussion of “Intermediaries as Information Aggregators” December 2014 11
E.g., numerically, the paper finds:

Higher fundamental uncertainty ⇒ Indirect bidding is more likely.

This seems to hold post-2008, but not in a longer sample.

05 06 07 08 09 10 11 12 13 14

0

2

4

6

8

10

12

14

16

Dates

Direct Bid Percentage vs. Bid Dispersion in 5−Year Treasury Auctions

20 × Direct/(Direct+Indirect)

High Yield minus Medium Yield (bps)

Haoxiang Zhu (MIT Sloan) Discussion of “Intermediaries as Information Aggregators” December 2014 11
Comment 3: Strategic incentives of dealers

Do dealers want to garble the information  ̄s?

How do investors monitor dealers for truth-telling?

Haoxiang Zhu (MIT Sloan) Discussion of “Intermediaries as Information Aggregators” December 2014 12
Comment 4: The number of dealers

In this paper, there are N investors and D dealers. D is changed while

N is held constant.

Two countervailing effects of increasing D while holding N fixed:

I Competition effect: More dealers means more competition ⇒ Higher

revenue

I Information effect: More dealers means more dispersed information and

potentially higher adverse selection ⇒ Lower revenue

Holding N fixed is less satisfactory. It seems more reasonable to have

D + N fixed. Non-primary dealers can still bid as investors.

Suggestion: Try fixing N + D and varying N/D.

I If free-riding and information acquisition are modeled, having a single

dealer is unlikely revenue-maximizing.

Haoxiang Zhu (MIT Sloan) Discussion of “Intermediaries as Information Aggregators” December 2014 13
Comment 5: Corner solutions with multiple dealers

With multiple dealers and normally-distributed signals, some investors

may end up shorting the bonds! But shorting is impossible in

Treasury auctions. The current model implicitly allows shorting.

If shorting is not allowed, some pessimistic information will be

truncated. Intuitively, this left-tail truncation should happen more

often if D is larger.

Conjecture: Increasing D creates a tradeoff between price discovery

and revenue:

I More left-tail truncation ⇒ Worse price discovery

I More left-tail truncation ⇒ Higher revenue?

Downside: Banning shorting can complicate the model considerably.

Haoxiang Zhu (MIT Sloan) Discussion of “Intermediaries as Information Aggregators” December 2014 14
Auction Design Questions

Should Treasury auctions be double auctions (i.e. allow shorting)?

Pre-auction when-issued market effectively allows shorting.

Post-auction shorting is done in OTC markets through repos.

Price discovery should improve with shorting

But revenue effect is unclear—it boils down to whether investors have

common value or private values for Treasury securities.

Uniform-price versus discriminatory-price: Treasury issuance auctions

are uniform-price, but Fed purchase auctions are discriminatory-price

(Song and Zhu 2014). Why?

What is the optimal frequency of Treasury auctions?

What is the optimal maturity structure of Treasury auctions?

Haoxiang Zhu (MIT Sloan) Discussion of “Intermediaries as Information Aggregators” December 2014 15