Intermediaries as Information Aggregators: An Application to U.S. - PowerPoint PPT Presentation

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 1 / 17

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 3 / 17

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 4 / 17

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 5 / 17

Allotted shares by bidders 1 .8 .6 .4 .2 0 Apr08 Apr09 Apr10 Apr11 Apr12 Apr13 Apr14 PD Indirect Direct Non-competitive 6 / 17

Number of primary dealers 50 1992 PD Operating Policy 2010 PD Operating Policy 1998 PD Scorecard 40 30 20 10 0 Jan60 Jan65 Jan70 Jan75 Jan80 Jan85 Jan90 Jan95 Jan00 Jan05 Jan10 Jan15 7 / 17

Basic model N investors are evenly assigned to 1 of D dealers All have exponential utility − exp ( ρ j W j ) ρ j is ρ D for dealers ρ for investors and W j = W 0 − q j p + q j f Future value of security f ∼ N ( µ , τ − 1 ) f 8 / 17

Model structure Type Information Decisions Strategic Demand � � 0, τ − 1 Market orders Non-competitive x ∼ N x Investors ( N ) s i , ¯ s , p Bidding Price-takers q i ( p | s i , ¯ s ) Dealers ( D ) ¯ s , p Bidding Strategic q d ( p | ¯ s ) q L ( p | s L , ¯ s ) Large invest. (1) s L , ¯ s , p Bidding; inter- Strategic mediation Each investor has a signal ε i ∼ N ( 0, τ − 1 s i = f + ; ) ε i ε ���� ���� “noise” “fundamental” Dealers disseminate average ¯ s j to their clients � � 0, D / N τ − 1 s j = f + ¯ ε j ∼ N ¯ ε j ; ¯ ε ⇒ Dealers aggregate information (reduce uncertainty) 9 / 17

Model structure Type Information Decisions Strategic Demand � � 0, τ − 1 Market orders Non-competitive x ∼ N x Investors ( N ) s i , ¯ s , p Bidding Price-takers q i ( p | s i , ¯ s ) Dealers ( D ) ¯ s , p Bidding Strategic q d ( p | ¯ s ) q L ( p | s L , ¯ s ) Large invest. (1) s L , ¯ s , p Bidding; inter- Strategic mediation Large, strategic investor chooses between bidding directly or through a dealer Trade-off : gain access to ¯ s but disclose s L to dealer 9 / 17

Model intuitions Optimal bids q ( p ) condition on information in realized price p Equilibrium price: p = A + B ( f + ¯ ε ) + Cx (1) � ��� �� ��� � ¯ s 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 10 / 17

Basic model solution Investors bid q i ( p ) = E [ f | s i , ¯ s , p ] − p ρ V [ f | s i , ¯ s , p ] 11 / 17

Basic model solution Dealers bid E [ f | ¯ s , p ] − p q d ( p ) = ρ D V [ f | ¯ s , p ] + dp / dq d Having a dealer lowers payoff uncertainty: V [ f | s i , ¯ s , p ] < V [ f | s i , p ] Increasing the number of dealers Makes dealers less strategic: lowers dp / dq d ⇒ Dealers less sensitive to information. Inhibits information aggregation: precision of ¯ s j falls, V [ f | s i , ¯ s , p ] rises 11 / 17

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 12 / 17

Effect of one vs. no dealer 80 St. dev. of excess revenue (bps) Expected excess revenue (bps) 0 70 60 −20 1 Dealer 1 Dealer 50 Competitive −40 Competitive 40 −60 30 −80 20 −100 10 55 60 65 70 75 80 85 90 95 100 105 55 60 65 70 75 80 85 −1/2 , bps) 90 95 100 105 −1/2 , bps) Fundamental uncertainty ( τ f Fundamental uncertainty ( τ f Less uncertainty with information aggregation ⇒ Higher revenues ⇒ More sensitivity to information ⇒ more volatility Effect of information intermediaries is opposite to IPO underwriters 13 / 17

Changing the number of dealers 80 St. dev. of excess revenue (bps) 30 Expected excess revenue (bps) 70 25 τ f =0.5*Baseline 20 60 Baseline τ f =2*Baseline 15 50 10 5 40 0 τ f =0.5*Baseline 30 −5 Baseline τ f =2*Baseline −10 20 5 10 15 20 25 30 35 40 45 50 5 10 15 20 25 30 35 40 45 50 Number of dealers Number of dealers 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) 14 / 17

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 15 / 17

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: E [ f | ¯ s , p ] − p q d ( p ) = ρ D V [ f | ¯ s , p ] + dp / dq d 16 / 17

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 E [ f | ¯ s , p ] − ( 1 − χ ) p q d ( p ) = ρ D V [ f | ¯ s , p ] + ( 1 − χ ) dp / dq d Higher χ lowers strategic component of demand but also price elasticity ⇒ Higher auction revenue but higher volatility 16 / 17

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 17 / 17