The full text on this page is automatically extracted from the file linked above and may contain errors and inconsistencies.
Federal Reserve Bank of Chicago
Repos, Fire Sales, and
Bankruptcy Policy
Gaetano Antinolfi, Francesca Carapella,
Charles Kahn, Antoine Martin, David Mills,
and Ed Nosal
WP 2012-15
Repos, Fire Sales, and Bankruptcy Policy
Gaetano Antinol…
Francesca Carapella
Antoine Martin
David Mills
Charles Kahn
Ed Nosal
May 2012
This version: November 2012
Abstract
The events from the 2007-2009 …nancial crisis have raised concerns
that the failure of large …nancial institutions can lead to destabilizing
…re sales of assets. The risk of …re sales is related to exemptions from
bankruptcy’s automatic stay provision enjoyed by a number of …nancial
contracts, such as repo. An automatic stay prohibits collection actions
by creditors against a bankrupt debtor or his property. It prevents a
creditor from liquidating collateral of a defaulting debtor since collateral
is a lien on the debtor’s property. In this paper, we construct a model
of repo transactions, and consider the e¤ects of changing the bankruptcy
rule regarding the automatic stay on the activity in repo and real investment markets. We …nd that exempting repos from the automatic stay is
bene…cial for creditors who that hold the borrowers’collateral. Although
the exemption may increase the size of the repo market by enhancing the
liquidity of collateral, it can also lead to subsequent damaging …re sales
that are associated with reductions in real investment activity. Hence,
policy makers face a trade-o¤ between the bene…ts of investment activity
and the bene…ts of liquid markets for collateral.
1
Introduction
An institution that su¤ers large losses may be forced to sell assets at distressed
or …re-sale prices. If other institutions revalue their assets at these temporarily
low market values, then they too may be forced to sell assets and su¤er losses.
As a result, the initial sale can set o¤ a cascade of …re sales that in‡icts losses
on many institutions, (French et al., (2010)). A number of commentators have
identi…ed …res sales as depleting the balance sheets of …nancial institutions and
aggravating the fragility of the …nancial system in the recent …nancial crisis,
(Shleifer and Vishny, 2011). Therefore, via defaults and …re sales, one troubled
Antinol…, Board of Governors and Washington University in St. Louis; Carapella, Board
of Governors; Martin, New York Fed; Mills, Board of Governors; Kahn, University of Illinois
at Urbana-Champaign; Nosal, Chicago Fed
1
institution can damage another and, as a result, reduce the …nancial system’s
capacity e¢ ciently allocate resources. Many commentators have identi…ed stress
in the repurchase agreement (repo) market as an important contributor to the
recent …nancial crisis.
This paper develops a model of a repo market. A repo is a borrowing arrangement where the …rst leg of the transaction has one party— the borrower— “selling”a security to another party— the lender— for cash, and the second leg, which
occurs at some predetermined future date, has the borrower repurchasing the
security from the lender for cash at a predetermined price. The security that
the lender holds in between the two legs is typically referred to as collateral.
We examine the implications of di¤erent bankruptcy policy rules on the activity
in a number of markets: the repo market, any market where the collateral of
defaulted borrowers can be resold, and the market for real investment. Under
current bankruptcy rules, the repo lender can liquidate the collateral if the borrower defaults before the end of the contract. In e¤ect, the repo contract is
exempt from a standard bankruptcy procedure, known as an automatic stay,
which prevents creditors from initiating collection actions against a bankrupt
debtor or his property. Collateral is considered to be a lien on the debtor’s
property, so an automatic stay prevents the creditor from liquidating collateral
when the debtor …les for bankruptcy. An exemption from an automatic stay,
which is a¤orded to a number of …nancial contracts, has raised concerns that
the default of a large …nancial institution could trigger destabilizing …re sales of
assets. Such fears are based on the failure, or near failure of Bear Stearns and
Lehman brothers in 2008.1
In our model, the possibility of borrower default motivates lenders to request
collateral from buyers as a form of insurance, (see also Mills and Reed (2012)).
However, the insurance function of the collateral is imperfect. If there is an
automatic stay in place, then the inability to immediately liquidate collateral
of defaulted borrower imposes a cost on holders of collateral. The cost can be
associated with the inability to convert relatively illiquid collateral into a liquid
asset, or with the risk that the collateral could lose value. We model the cost as
the inability to convert an illiquid asset into a liquid one.
If, instead, bankruptcy rules allow the lender to liquidate the borrower’s
collateral, then the collateral can be sold in a secondary market. Depending
on the liquidity of that market, sales of collateral can have important e¤ects
on other market participants. We focus on the e¤ect that lenders’ sales of
collateral have on real investments when investors are using assets similar to
the collateral to secure resources for the investment. Imbedded in our model is
an externality that implies that lenders do not take into account the e¤ect they
have on investors when making their initial repo decisions and their liquidation
decisions in the event of borrower default. Absent this externality, lenders would
internalize all the e¤ects of their sales of assets on the economy and make e¢ cient
investment decisions in the repo market. The externality is modeled by a
1 It is worth noting that these fears were, for the most part, not realized in part because
Bear Stearns was purchased by JP Morgan Chase, and the US broker dealer unit of Lehman
did not declare bankruptcy.
2
trading friction. The trading friction is an important ingredient in our model;
it is also an important ingredient in practice since repos and other …nancial
instruments that make use of collateral are traded in over-the-counter markets.
These sorts of markets are not competitive and, hence, are subject to trading
frictions.
The externality creates a trade o¤ for policy makers who contemplate using
bankruptcy rules as their policy instrument. On the one hand, exempting repo
transactions from the automatic stay is desirable because the ability to liquidate
the borrower’s collateral increases its value to the lender. In this sense, an
exemption from the automatic stay makes the repo market more liquid. On the
other hand, increased activity in the repo market can result in more pronounced
…re sales and reduction in investment activity in case of borrower default. The
relative size of these two e¤ects depend on the parameter values of the model
and, thus, so does the optimal bankruptcy rule.
Under the US Bankruptcy Code, most contracts are subject to an automatic
stay when a debtor …les for bankruptcy. This stay delays the ability of a creditor
to realize value through a sale of the collateral.2 Over the decades since the
current framework was established, an ever-increasing set of quali…ed …nancial
contracts (“QFC’s”), including repos, has been exempted from the stay.3 (In
the case of banks taken over by the FDIC or systemically important …nancial
institutions under Dodd-Frank, there may be a stay for a limited time even for
QFC’s.)
Our paper focuses on the trade-o¤ between the liquidity of the repo market
and the potential for …re sales related to the exemption from the stay. This
trade-o¤ is discussed in the legal literature; see Roe (2011). Du¢ e and Skeel
(2012) outline a number of costs and bene…ts associated with safe harbors from
the automatic stay in bankruptcy, including a variety of ways in which the stay
can decrease the value of the collateral contract, and on the other side, the
potential for costs in a …re sale. (They note that in particular money market
mutual funds holding repos may be forced by regulation to sell the collateral in
the case of the bankruptcy of a counterparty).
Bolton and Oehmke (2011) argue against privileged positions for derivatives
in bankruptcy, because it ine¢ ciently undermines the position of other creditors. The paper that is most closely related to our is Acharya, Anshuman,
and Viswanathan (2012) which also examines the costs of bankruptcy-induced
…re sales. Our paper does not address some of the bene…ts of the exemption
from the stay associated with close-out netting. An analysis of these bene…ts is
provided in McAndrews and Roberds (2003).
The paper is organized as follows. The basic model, without default, is
2
Generally speaking, the purpose of an automatic stay is to prevent the destruction of
value that can occur when creditors make a “mad dash” to seize the assets the bankrupt …rm.
To the extent that the assets used as collateral are …nancial assets rather than real assets, the
destructiveness of this "grab race" is less of a consideration, and so in this paper we focus
instead on the e¤ects of a rush to sell these assets in a less-than-perfectly liquid market.
3 For an account of the changes in the application of bankruptcy law to repos, see Garbade
(2006).
3
presented in the next section. The basic model is generalized to allow for defaults
in section 3. Section 4 examines the nature of a government policy intervention
and carefully analyzes the trade-o¤s that the government faces. Section 5 o¤ers
some concluding comments.
2
The Basic Model
The economy has 3 dates— 1, 2, and 3— and 2 goods— a and c. Good a is
durable— that is, it can be costlessly stored from one period to the next. Good
c is perishable.
There are 4 types of agents: lenders, L, borrowers, B, investors, I, and
traders, T . The measure of each type of agent is ni , where i 2 fL; B; I; T g.
Lenders and borrowers are born at the beginning of date 1. Borrowers live
at dates 1 and 2, and lenders live at dates 1, 2 and 3. Investors and traders are
born at the beginning of date 3 and live only at date 3.
Borrowers like to consume good a at date 2. They possess a costless technology that instantaneously converts good c into good a one-for-one in date 1
or date 2. They can also produce good c; but only at date 2, at a cost of 1 unit
of e¤ort per unit of good. The preferences of a borrower, U B , are given by
U B = a2
c2 :
(Subscripts indicate when the goods are produced or consumed.)
Lenders want to consume goods a and c at dates 2 and 3; they like good c
more than good a. Lenders can produce good c only at date 1, where one unit
of costly e¤ort produces one unit of good c. The preferences of a lender, U L ,
are given by
U L = u (c2 ) + c3 + a2 + a3 c1 ;
where u is increasing and strictly concave, and 0 < < 1.
Traders are endowed with c units of good c at date 3. They like to consume
goods a and c at date 3, and their preferences, U T , are given by
U T = a3 + c3 :
Investors are endowed with a units of good a at date 3. They like to consume
goods a and c, and their preferences, U I , are
U I = a3 + c3 :
Investors have a costless technology that instantaneously converts good c into
good a. Unlike the borrower’s technology, which is one-to-one, the investor’s
technology, f , is increasing, strictly concave and f 0 (a) > 1. The last assumption
implies that f is a productive technology in the sense that if the investor could
exchange his endowment of good a for a units of good c, then marginal return
is strictly greater than one for all levels of input c 2 (0; a].
4
Agents trade in pairs; that is, they are bilaterally matched. Agents are
matched at the beginning of date 1 and at the beginning of date 3. The date
1 and date 3 matching processes are independent of one another. Since investors and traders are not alive at date 1, only lenders and borrowers enter the
matching process at that time.
Some bilateral matches can generate surplus for the agents in the match.
For example, borrowers and lenders can bene…t from trading good c at date 1
for good c at date 2. In particular, a matched lender can produce good c at
date 1 and give it to the borrower, (who converts it into good a). In return,
the borrower can produce good c for the lender at date 2. Let this trading
arrangement be compactly represented by the “contract” (c1 ; c2 ). Note that
since the good c that is produced at date 1 is converted to good a one-for-one,
a1 = c1 ; and since good a is durable, a2 = a1 . Implicitly embedded in contract
(c1 ; c2 ) is a promise: the borrower promises to produce good c for the lender at
date 2. We will assume that agents can commit to any (feasible) promise they
make when matched.
Traders and investors can bene…t from exchanging good a for good c at date
3. In particular, a matched investor can exchange some of his endowment of
good a for some the the trader’s endowment of good c. The trading arrangement
between investors and traders can be represented by the contract (a3 ; c3 ), i.e.,
the investor gives up a3 units of his endowment of good a and receives c3 units
of the trader’s endowment of good c.
The date-1 contract, (c1 ; c2 ), between a matched lender and borrower is
determined by bargaining. The lender’s payo¤ (and surplus) associated with
contract (c1 ; c2 ) is u (c2 ) c1 . Since technology and durability of good a implies
that c1 = a1 = a2 , the borrower’s surplus is c1 c2 . Total match surplus
generated by contract (c1 ; c2 ) is
S BL = u (c2 )
c2 :
A borrower accepts contract (c1 ; c2 ) only if c1 c2 and a lender accepts only if
u (c2 ) c1 . For simplicity, we assume that the lender has all of the bargaining
power and makes a take-it-or-leave-it o¤er to the borrower. This bargaining
protocol implies that the lender will choose c2 = c1 and, hence, receives the
entire match surplus.4 The lender o¤ers contract (c ; c ) to the borrower, where
u0 (c ) = 1, since this o¤er maximizes match surplus. The borrower will accept
this o¤er.
Let mij represent the probability that agent i is matched with agent j at
date 1, and let m represent the measure of productive date 1 matches. We will
assume that the matching technology is Leontief in nature and takes the form
m = min nL ; nB , mLB = m=nL , mBL = m=nB , and mBB = mLL = 0: For
this matching technology one can interpret agents as directing their search to a
productive partner, where the “short side”of the market determines the number
of matches.
4 Dividing
surplus between the bargainers will not signi…cantly a¤ect our results).
5
Lenders have no incentive to enter the date 3 matching process, independent
of being matched or not at date 1, since they have nothing to o¤er in a date 3
match that could generate a match surplus. Therefore, the expected payo¤ to
a lender— measured before agents are matched at date 1— is mLB [u (c ) c ].
Since the lender has all of the bargaining power in a date 1 match with a
borrower, the expected payo¤ to a borrower is zero.
Only investors and traders enter the date 3 matching process. In an investortrader match, the investor’s payo¤ associated with contract (a3 ; c3 ) is f (c3 ) +
a a3 . The surplus that the investor receives is f (c3 )+(a a3 ) a = f (c3 ) a3 .
The trader’s payo¤ associated with contract (a3 ; c3 ) is a3 +c c3 , and the surplus
he receives is a3 + c c3 c = a3 c3 . Hence the total match surplus is
S IT = f (c3 )
c3 :
The investor will accept contract (a3 ; c3 ) only if f (c3 ) > a3 , and the trader
will accept the o¤er (a3 ; c3 ) only if a3
c3 . We assume that the investor
has all of the bargaining power. The investor will o¤er contract (a3 ; c3 ) to
the trader, where a3 = c3 = min fa; cg, which implies that the match surplus
is f (min fa; cg) min fa; cg. For convenience, de…ne {
min fa; cg, i.e., {
represents the amount of good c that an investor receives from a trader, and
the amount of good a that he gives the trader.
Let M ij represent the probability that agent i is matched with agent j at
date 3, and M represent the measure of date-3 productive matches. For a
Leontief matching function, M = min nI ; nT , M IT = M=nI , M T I = M=nT ,
and M II = M T T = 0. Since the investor has all of the bargaining power, his
payo¤ is M IT (f ({) {) + a, and the expected payo¤ to the trader is c.
Let pa represent the value to an investor of having an additional unit of good
a, measured in terms of good c, at the beginning of date 3 before matching takes
place. Then, when { = a,
pa = M IT f 0 (a) + 1
M IT ;
i.e., the investor is indi¤erent between receiving pa units of good c for sure, and
receiving an additional unit of good a.5
Consider the problem of a planner whose objective is to maximize total social
surplus, S; where
S
= m (u (c2 ) c1 ) + m (a2 c2 ) + M [(c
M [(a a3 ) + f (c3 ) a]
= m (u (c2 ) c2 ) + M (f (c3 ) c3 ) ;
c3 ) + a3
c] +
5 When { = c, then the price of good a is 1 since if the investor is given an additional unit
good a he will simply consume it. The average price of good a, however, is
M IT
c f (c)
a c
+
+ 1
a c
c
M IT
> 1:
We would argue that in this case, the average price is the relevant statistic when thinking
about gains from trade.
6
since c1 = a2 . Assuming that the planner must respect agent participation
constraints, total social surplus will be maximized at c2 = c and c3 = {, the
take-it-or-leave-it o¤ers made by the lender and investor, respectively.6 The
planner can implement this surplus as long as u (c2 )
a2
c2 and f (c3 )
a3 c3 , i.e., agent participation constraints are satis…ed. Although the planner
can redistribute surplus from the lender to the borrower (by increasing a2 from
c ) and from the investor to the trader (by increasing a3 from a), he cannot
increase total surplus compared to the equilibrium outcome.
The equilibrium in the basic model is Pareto e¢ cient. The basic model lacks
frictions that are needed to generate contracts that resemble repo contracts or
something that looks like a “…re sale.” In addition, since agents do not default
on the their contracts, the basic model can say nothing about bankruptcy or
bankruptcy policy. In the next section we introduce a borrower default friction
and examine how this a¤ects optimal contracts, and the relationship between
bankruptcy policy and …re sales.
3
A Model with Borrower Default
We extend the basic model by introducing the possibility of exogenous default
by borrowers. Default is modeled by having the possibility that borrowers die
between dates 1 and 2. With probability a random fraction
of borrowers
die, and with probability 1
no one dies.7 We will refer to the former outcome
as the default state, and the latter as the no-default state. From an ex ante
date 1 perspective, the probability that a borrower dies is
. We use two
parameters to describe default so that we can model a rare event, a ‘small’ ,
such as a major …nancial meltdown, a ‘big’ .
The Section 2 contract between the lender and borrower can be interpreted
as an unsecured (by collateral) loan since it is only the borrower’s promise that
supports the date 2 payment. In practice, it is not at all unusual for unsecured
creditors to receive nothing in the event that the borrower defaults. We model
this outcome by assuming that when a matched borrower— holding c1 units of
good a and promising to produce c2 units of good c at date 2— dies in between
dates 1 and 2, the good a he is holding “disintegrates,” and, as a result, the
lender receives nothing. Although there is little the lender can do about a
borrower’s broken promise to supply good c at date 2— the borrower is dead
after all— the lender can secure his claim against the borrower by contractually
preventing the borrower from holding good a between dates 1 and 2. Speci…cally,
the contract can specify that the lender produces good c at date 1 and gives
it to a borrower; the borrower then converts good c into good a, and gives
6 The planner also takes as given the matching techologies and the bargaining protocol of
the agents.
7 We can assume that with probability 1
, a …nite number, i.e., a set of measure zero,
of borrowers die. This way there can be defaults even in “good” times, but these defaults
are essentially unimportant for the economy. This would correspond to situations (in the real
world) where there are “fails” or defaults and these have no signi…cant implications for asset
prices or economic activity.
7
good a back to the lender to hold as collateral. At date 2, the collateral— good
a— is transferred back to the borrower if he produces good c for the lender; if
the borrower does not produce at date 2— because he has died— the collateral
becomes the property of the lender. This sort of contract partially insures the
lender against a borrower default: If the borrower dies, then the lender has the
collateral which is valuable to him at both dates 2 and 3.
Whether one interprets the above contract as a collateralized loan or a repo
contract depends on when the lender is able to use the collateral. In practice,
bankruptcy law speci…es when collateral can be used by the lender. Under the
US Bankruptcy Code, virtually all collateral is subject to an automatic stay
when a debtor …les for bankruptcy. This means that a secured (by collateral)
creditor is unable to access and use the collateral for a certain period of time
after a debtor defaults. However, some …nancial assets, such as derivatives and
repo contracts, are exempt from the automatic stay, which implies that a secured
creditor can immediately access and use the collateral as he sees …t. In terms of
the model, if the bankruptcy policy dictates an automatic stay in the event of a
debtor default, then the contract described above is a collateralized loan. In this
situation, in the event of a debtor default, the earliest that collateral can by used
by the lender is at date 3 after the matching process has been completed. If,
instead, the bankruptcy policy exempts the collateral from an automatic stay,
then the above contract is repo, and the lender can use the collateral as he sees
…t starting at date 2, when it becomes known that the debtor has (died and)
defaulted on his contractual payment of c2 . In the next section, we analyze the
implications of a bankruptcy policy that exempts the collateralized contracts
from an automatic stay. In the subsequent section, we examine the implications
of a bankruptcy policy that imposes an automatic stay on collateral.
Repo contracts
A repo contract is represented compactly by (~
c1 ; c~2 ), where the “initial loan”
size is c~1 , the amount of collateral is a1 = c~1 , and the “loan repayment”is c~2 . If
the borrower does not default, then the lender receives c~2 units of good c from
the borrower, and the lender transfers the collateral, c~1 units of good a to the
borrower at date 2. If the borrower defaults, then, at date 2 the lender owns
the collateral, a, which can be used by him starting at date 2.
Suppose a matched borrower dies. The lender can consume the collateral at
either dates 2 or 3, and his payo¤ is c~1 . Alternatively, the lender can enter
the date 3 matching process with his collateral. If he is matched with a trader,
then there are gains from trade because the lender’s relative valuation of good
a to good c is and the trader’s is 1. Hence, the lender’s payo¤ can exceed c~1
if he is matched with a trader.8
Denote the terms of trade in a lender-trader match by the contract (~
a3 ; c~3 ),
where the lender gives a
~3 units of good a to the trader in exchange for c~3
8 If the lender is matched with an investor, there are no gains from trade— since both agents
have good a— and his payo¤ will be c~1 .
8
units of good c. The payo¤ to the lender associated with contract (~
a3 ; c~3 ) is
(~
a2 a
~3 ) + c~3 , where a
~2 represents the amount of good a that the lender
brings into the match, and the payo¤ to the trader is a
~3 + c c~3 . Total surplus
in a lender-trader match is (1
)a
~3 :
Assume the lender has all of the bargaining power. The trader will accept
the lender’s o¤er only if a
~3 c~3 , and the take-it-or-leave-it assumption implies
that a
~3 = c~3 . The payo¤ to a matched lender holding collateral equal to a
~2 is
(~
a2
a
~3 ) + c~3
=
=
(~
a2
a
~2
if a
~2 c
c) + c if a
~2 > c
minf~
a2 ; c + (~
a2
c)g
(a concave function of a
~2 ) and the payo¤ to the trader is c. Since the lender’s
expected payo¤ associated with entering the date 3 matching process is strictly
greater than a
~2 = c~1 , he will always enter the date 3 matching process holding
collateral a
~2 when his borrower defaults.
The repo contract (~
c1 ; c~2 ) that the lender o¤ers the borrower in a date 1
match is clearly a¤ected by the possibility that his borrower defaults. Since the
lender has all of the bargaining power in the date 1 match, c~1 = c~2 = a
~1 = a
~2 .
Denote the probability that the lender is matched with a trader in the event
that his borrower dies by MdLT , and let Md denote the measure of matches
between traders and either lenders or investors at date 3 in the default state.
For the Leontief matching technology lenders and investors direct their search
to traders, and, therefore, Md = min nI + m; nT and
Md
:
m + nI
MdLT =
We can characterize the optimal date 1 repo contract, (~
c1 ; c~2 ), by considering
the following maximization problem, which is to choose the amount of good c1
to produce.
max c1 + (1
c1
) u (c1 ) +
MdLT minfc1 ; c + (c1
(1)
c)g + 1
MdLT
c1 ;
If the borrower does not die the lender consumes c1 units of good c at date 2. If
the borrower dies, the lender is able to enter the date-3 matching process since
there is an exemption on the automatic stay. He consumes c1 units of good a
if he is not matched. If he is matched, then the amount he consumes depends
on whether his collateral is less than or greater than the trader’s endowment.
The term in brackets arises because if a lender’s collateral, c1 units of good
a is less than the trader’s endowment of c, then he will be able to exchange
all of his collateral for good c: On the other hand if his collateral is greater
than the trader’s endowment, the lender will only be able to exchange part of
his collateral for good c: Note that there is a discrete decrease in the marginal
bene…t associated with having an additional unit of good c at c = c; the expected
marginal bene…t falls from
M LT + 1 M LT
to
.
9
Thus the choice c~1 is characterized by the …rst order conditions for this
problem as follows:
) u0 (c) +
> 1, then c~1 > c,
where (1
) u0 (~
c1 ) +
= 1;
(ii) If (1
) u0 (c) +
MdLT + 1 MdLT
< 1, then c~1 < c,
where (1
) u0 (~
c1 ) +
MdLT + 1 MdLT
= 1;
(iii) otherwise, c~1 = c.
(i) If (1
Suppose that the default state is realized and, as a result, nB borrowers
die in between dates 1 and 2. Then, at date 3, traders, investors and lenders will
enter the matching process. Denote the probability that an investor is matched
with a trader in the default state as MdIT , where MdIT = MdLT . The terms of
trade between a matched investor and trader is not a function of the matching
probability MdLT . Hence, the investor exchanges { = min fa; cg units of good a
for { units of good c with the trader. From the investor’s date 3 perspective,
when { = a, the price of good a, measured before agents are matched at date 3,
pa , is
pa = MdIT f 0 (a) + 1 MdIT :
It is important to emphasize that pa pa , since M IT
MdIT .9 When M IT >
IT
Md , pa > pa , and the lower price in the default state will be referred to as a
“…re sale”of asset a. The value of asset a decreases to investors because lenders’
enter the date-3 matching process to sell their collateral and this reduces the
probability that the investors are matched with traders. (In the no-default state,
an event that occurs with probability 1 , the price of asset a is pa .) There are
real e¤ects associated with the …re sale since the total amount of real investment
falls, compared to the situation where borrowers do not default.
4
Government policy
In the basic no-default model, the government cannot increase total social surplus, compared to the equilibrium allocation. When borrowers can default, however, a government may be able to increase total social surplus, compared to
the equilibrium allocation, by a¤ecting the ‡ow of lenders that enter the date-3
matching process. In particular, the government policy instrument is the speci…cation of automatic stay provisions or exclusions on collateral. Let represent
the fraction of lenders that are allowed to use the collateral of their defaulting
9 As above, M IT represents the probability that an investor is matched with a trader in the
no-default state. In the no-default state, lenders do not enter the date-3 matching process.
When { = c, the appropriate measure of gains from trade is the average price of good a, and
MdIT
c f (c)
a c
+
+ 1
a c
c
M d IT
M IT
10
c f (c)
a c
+
+ 1
a c
c
M IT :
borrower as they see …t starting at the beginning of date 2. An exemption from
an automatic stay on collateral for all lenders implies that = 1, and an automatic stay on all collateral, where lenders are only able to access their collateral
in date 3 after the matching process is completed, implies that = 0. When
= 1, the (~
c1 ; c~2 ) is a repo contract; when = 0, it is a collateralized loan
contract. Note that 2 (0; 1) can be interpreted as a partial exemption from an
automatic stay in the sense that some lenders,
m of them, are exempt from
an automatic say and others, (1
) m of them, are not. It is important to
emphasize that if a lender’s collateral is subject to an automatic stay, then he
(and his collateral) cannot participate in the date-3 matching process.
Government policy, through its e¤ect on , can a¤ect the payo¤s and behavior of the various agents in the economy. The expected payo¤ to a borrower,
WB , is
WB = mBL (1
) (~
a1 c~2 ) ;
where mBL is the probability that a borrower is matched with a lender at date 1.
The behavior of the borrower can be a¤ected by government policy since policy
can a¤ect c~1 , which, in turn, a¤ects a
~1 and c~2 . The payo¤ to the borrower
is una¤ected by government policy since the lender has all of the bargaining
power, c~2 = a
~1 , which implies that WB = 0.
The payo¤ to the lender, WL , is given by
WL
= mLB f a
~1 + (1
) u (~
c2 ) + [ (MdLT (~
c3 + a
~2
LT
1 Md
a
~2 )] + (1
) a
~2 g:
a
~3 ) +
Government policy can the the payo¤ of the lender directly— since appears
in WL — and indirectly through c~1 and c~2 — and, as a result, through a
~1 ,~
a2 , c~3 ,
and a
~3 .
The expected payo¤ to a trader, WT , is
WT
=
) M T I (^
a3
(1
MdT I (^
a3
=
(1
c^3 + c) + 1
c^3 + c) + MdT L (~
a3
) M T I + MdT I (^
a3
MTI c +
c~3 + c) + 1
c^3 ) + MdT L (~
a3
MdT I
MdT L c
c~3 ) + c;
where the ‘hat’ over the a3 and c3 represents (optimal) o¤ers made by the
investor to the trader,
m
Md
MdT L = T
n
m + nI
and
nI
Md
MdT I = T
:
n
m + nI
Since investors and lenders have all of the bargaining power in their matches
with traders, a
^3 = c^3 and a
~3 = c~3 , which implies that WT = c. Hence, the payo¤
to the trader is una¤ected by government policy . In fact, c^3 = min fc; ag = a,
which is the trade allocation in a trader-investor match in a world without
default. Note, however, that c~3 and a
~3 can be a¤ected by government policy.
11
Finally, the payo¤ to the investor, WI , is
WI
=
) M IT (f (^
c3 )
(1
MdIT (f (^
c3 )
=
MdIT a
a
^3 + a) + 1
) M IT + MdIT (f (^
c3 )
(1
M IT a +
a
^3 + a) + 1
a
^3 ) + a:
Although the behavior of the investor is una¤ected by government policy— since
a
^3 = c^3 = min fa; cg = a— his payo¤ is a¤ected since the matching probability
MdIT is a function of .
In order to evaluate various government policies, we must understand how
the behavior of a lender— which is simply his choice of c~1 — is in‡uenced by
changes in . The lender’s problem is a straightforward generalization of the
problem (1) in section 3, to take account of government policy, .
max c1 + (1
c1
) u (c1 ) +
MdLT minfc1 ; c + (c1
c)g + 1
MdLT
c1 + (1
) c1
(G1)
The …rst-order condition characterizing c~1 is as follows,
(i) If (1
) u0 (c) +
> 1, then c~1 > c,
0
where (1
) u (~
c1 ) +
= 1;
(ii) If (1
) u0 (c) +
) MdLT < 1, then c~1 > c,
+ (1
0
where (1
(2)
) u (~
c1 ) +
+ (1
)
(iii) Otherwise, c~1 = c.
MdLT
(3)
= 1;
Proposition 1 demonstrates how loan size, c~1 , for the contract (~
c1 ; c~2 ) is
a¤ected by a change in the government policy variable .
Proposition 1 c~1 is weakly increasing in .
Proof. If c~1 < c, then from (3), we have
(1
(1
@~
c1
=
@
Since
MdLT
=
(
and
@
MdLT
@
=
(
T
n
m+nI
)@
(
T
n n
m+nI )2
(4)
m + nI < n T
;
m + nI > n T
if
if
1
I
MdLT =@
:
) u00 (c1 )
if
if
m + nI < n T
;
m + nI > n T
(5)
we get that @~
c1 =@ > 0. If c~1 c, then from (2), @~
c1 =@ = 0.
The intuition behind this proposition is straightforward. Having access to
the date-3 matching process is valuable for the lender. Suppose that c~1 < c.
12
One can interpret an increase in as providing the lender with better insurance
against borrower default in the sense that an increase in increases the probably
that lender will be able to exchange good a— which he values “a little”— for good
c— which he values “a lot”— if the borrower defaults. Since the cost associated
with borrower default declines as increases, the lender has an incentive to
increase his date 1 loan, c~1 , and the collateral a
~1 = c~1 that he holds. Suppose
now that c~1
c. In this situation, the lender has no incentive to increase
his date-1 loan size c~1 when increases since, independent the lender being is
matched or not at date 3, the value of an additional unit collateral, conditional
on the borrower defaulting, is unchanged and equal to < 1.
The government seeks to maximize total social surplus, S, which is given by
S = nB WB + nL WL + nI (WI
a) + nT (WT
c) :
The assumed bargaining conventions imply that the expression for total social
surplus can be simpli…ed to
S = nL WL + nI (WI
a) ;
which means we only need to focus on the behavior of and payo¤s to lenders
and investors.
We now characterize how government policy a¤ects total social surplus.
Since c^3 = { min fa; cg, the surplus to investors is
WI
a = (1
) M IT + MdIT (f ({)
{) ;
(6)
and government policy a¤ects the investor’s surplus only through the matching
probability, MdIT .
Proposition 2 The investor’s payo¤ is weakly decreasing in .
Proof. Note that
@MdIT
=
@
(
0
(
mnT
m+nI )2
if
if
nT >
nT
m + nI
m + nI
and, since M IT = min nI ; nT =nI , @M IT =@ = 0. Therefore, @WI =@ =
@MdIT =@ (f (a) a) or
(
0
if nT >
m + nI
@WI
T
=
:
(7)
mn
(f (a) a) if nT
m + nI
@
(
m+nI )2
This proposition accords with intuition. If the measure of traders is relatively large— in the sense that nT >
m + nI — then increasing access to the
date-3 matching process for lenders has no e¤ect on the investors’ surpluses
since investors are matched with probability one at date 3. If, however, the
13
number of traders is not relatively large— in the sense that nT
m + nI —
then increasing access to the date-3 matching process to lenders will reduce
the probability that investors are matched with traders and, hence, reduces the
payo¤s to lenders.
Turning to lenders, since, c~1 = c~2 = a
~2 = a
~1 and a
~3 = min f~
c1 ; cg, the
surplus function for a lender can be simpli…ed to
WL = mLB
c~1 + (1
) u (~
c1 ) +
c~1 +
MdLT a
~3 (1
) :
(8)
To assess how its policy a¤ects total social surplus, the government must understand how WL is a¤ected by a change in .
Proposition 3 The lender’s payo¤ is strictly increasing in .
Proof. The derivative of (8) with respect to
@WL
@
= mLB
mLB
@~
c1
[ 1+
@
(
(1
)
) u0 (~
c1 )] + mLB
+ (1
@
MdLT
a
~3
@
is
)
(1
)
@~
a3
MdLT
@
The …rst line of (9) is equal to zero. When c~1 < c, this is implied by (??),
recognizing that @~
c1 =@ = @~
a3 =@ . When c~1
c, (3) implies that @~
c1 =@ =
@~
a3 =@ = 0. Therefore
(
)
@ MdLT
@WL
LB
= m
(1
)
a
~3
@
@
(
a
~3
if
m + nI < nT
= mLB
(1
)
nI nT
a
~ if
m + nI > nT
(
m+nI )2 3
> 0
The intuition behind proposition 2 is straightforward. Holding c~1 constant,
an increase in increases the chance that the lender will be able to participate
in the date-3 matching process. This unambiguously increases the surplus of
the lender because, in the event of a borrower default, the value of either part
or all of the lender’s collateral a increases from a to a. As well, if c~1 < c, then,
holding the date-3 matching probability constant, an increase in optimally
increases c~1 and, by construction, the lender’s collateral holdings. Since an
increase in c~1 is an optimal response to an increase in , the lender’s surplus
must also increase.
Propositions 2 and 3 identify the trade-o¤ that the government faces when
choosing its policy. An increase in (weakly) lowers the probability that an
investor will be matched with a trader and, hence, (weakly) lowers the level of
(productive) investment. But an increase in strictly increases the probability
14
+
(9)
that a lender will be matched with a trader, in the event of a borrower default,
and this enhances the “liquidity” of a lender’s collateral. (Collateral becomes
more “liquid” in the sense that it can be converted into the consumption good
with a higher probability.) To assess a government policy that changes the value
of , one simply has to compare the “investment e¤ect” with the “liquidity
e¤ect.” Generally speaking, the net e¤ect can go either way as the magnitudes
of the two e¤ects depend upon model parameters.
Consider …rst the situation where nT > m + nI . One can interpret this
situation as one where the date-3 market is “very liquid”–having the capacity always to match both investors and lenders with probability one. In this
situation, the optimal government policy is clear.
Proposition 4 When nT > m + nI , then the optimal government policy
provides an exemption from a bankruptcy stay for all lenders.
Proof. From (7) and (9), when nT >
m + nI
@WL
@WI
@S
= nL
+ nI
=m
@
@
@
(1
)a
~3 > 0;
for all . Hence, the government should choose “as high as possible,” i.e.,
= 1.
Consider now the interesting case where the date-3 market is illiquid from
the investor’s perspective in the sense that nI > nT . For this case, again using
(7) and (9), we obtain
@S
=
@
(
1
2
m + nI )
[(1
)a
~3 ( )
(f ({)
{)] :
(10)
When nI > nT , the optimal government policy is determined by comparing the
value of f ({) {— which is proportional to the investment e¤ect— with that of
(1
)a
~3 ( )— which is proportional to the liquidity e¤ect— for various values
of . More formally,
Proposition 5 Suppose nI > nT . If
(1
)a
~3 (0) > (f ({)
{) ;
then the optimal government policy provides an exemption from a bankruptcy
stay of all lenders, i.e., = 1. If
(1
)a
~3 (1) < (f ({)
{) ;
then is the optimal government policy requires a bankruptcy stay for all lenders,
i.e., = 0.
Proof. Since a
~3 = min f~
c1 ; cg, Proposition 1 implies that @~
a3 =@
0. If
(1
)a
~3 (0) > (f ({) {), then from (10) @W=@ > 0 for all 2 [0; 1], and
15
setting = 1 is optimal. If (1
)a
~3 (1) < (f ({) {), then from (10) @W=@ <
0 for all 2 [0; 1], and setting = 0 is optimal.
Note the message of the proposition: Even though the date-3 market is
illiquid from the perspective of investors— even if lenders are not permitted to
participate— it may be optimal for the government to exempt defaulted lenders
from a bankruptcy stay. This will happen when, intuitively speaking, the “liquidity value” of allowing lenders to have access to traders is greater than the
“investment value”associated with investor-trader matches. It is true that when
= 1, lenders will displace economy-wide investment when nI > nT . However,
the value of the liquidity, (1
)a
~3 , generated by lenders exceeds that of the
displaced investment.
The next proposition shows that when nI > nT nothing is gained by considering policies with interior 2 (0; 1):
Proposition 6 When nI > nT , either it is optimal to provide an exemption
from the bankruptcy stay to all lenders, = 1, or it is optimal to impose a
bankruptcy stay on all lenders, = 0.
Proof. Consider the derivative of surplus in formula 10. If there is a strict
interior maximum ^, then this expression must be positive for values of just
below ^ and negative for values of just above ^: But since a
~3 ( ) is a non
decreasing function, this is impossible.
When nI > nT , the optimal government policy is either imposes a bankruptcy stay on all lenders or an exemption from a bankruptcy stay for all lenders.
Although a partial exemption, i.e., 2 (0; 1), is permitted, it is never optimal,
except for the knife edge case where S 0 ( ) = 0 for all 2 [0; 1].10 But even in
this knife-edge case, = 1 or = 0 is an optimal policy. In spite of the relative
illiquidity of the date-3 market, i.e., nI > nT , an exemption from a bankruptcy
stay for all lenders is optimal when the liquidity value associated with providing
access to the date 3 market for lenders is greater than the displacement of real
investment opportunities.
The …nal case in terms of date-3 market liquidity to consider is the intermediate case where nI < nT and nL + nI > nT . In other words, there is enough
activity in the date 3 market to provide goods to all investors, but not enough
to provide goods to all lenders as well. Let
be such that
nL + n I = n T
— in other words the value of which exhausts the supply of traders. Clearly,
it would never be optimal for the government to choose a < . Optimal government policy here somewhat mirrors the case where nI > nT , except now the
lower bound of optimal government policy is
instead of = 0. In particular
Proposition 7 When nI < nT and nL + nI > nT an optimal government
policy either provides an exemption from a bankruptcy stay for all lenders, = 1,
or imposes a bankruptcy stay on fraction 1
of lenders.
Proof. The proof follows those of Propositions 5 and 6.
1 0 Among
other things, this knife-edge case requires that c~1 > c.
16
In other words with the Leontief matching technology the essential question
is whether there is greater social value from a match by an investor or a match to
ful…ll liquidity needs of the lenders. If the investments are more valuable, then
…re sales should be discouraged to the extent that they crowd out this investment. If the liquidity needs are more valuable, then they should be encouraged
through complete exemptions from automatic stays.
5
Final Remarks
This paper has deals with speci…c costs and bene…ts of the exemption from the
automatic stay associated with repos in bankruptcy. The bene…t we focus on
is the improvement in insurance arising from the ability of the lender to dispose
of his collateral quickly, and the cost is the disruption of the market for the
collateral and investments goods. The desirability of extending the automatic
stay to repos depends on the relative importance of these costs and bene…ts.
The standard argument in favor of automatic stays in the bankruptcy
process is the destruction of value associated with an uncoordinated break-up of
the bankrupt …rm. When the assets being sold are …nancial instruments rather
than real assets, this justi…cation appears to be less important. Furthermore,
having the option to allow some …nancial contracts to avoid the automatic stay
seems to be desirable as a way of increasing the opportunities for ‡exibility in
a …rm’s borrowing and thereby reduce borrowing costs. (While other articles,
noted in the introduction, have emphasized the costs imposed on less favored
lenders, as a …rst pass, this is a justi…cation for the law to limit the use of this
favored treatment itself, not a justi…cation for the prohibition of the favored
treatment).
The …re sale cost applies not to the …rm itself (in which case initial contracting by the …rm with its various counterparties could ultimately resolve the
problem) but to other participants in the market for the collateral good. Thus
the importance of the cost depends on the e¤ect that the …rm’s bankruptcy has
on the market— roughly speaking, on the liquidity and depth of that market. In
this respect, the conclusions of our model correspond to the comments by Du¢ e
and Skeel (2012), which advocate the exemption from the bankruptcy stay only
when the market for the collateral is extremely liquid. (In our model, depth
can be associated with the excess of traders willing to take the collateral when
lenders attempt to dispose of it). We provide a simple comparison of these
costs with the bene…ts from the improved opportunities of borrowing through
increase of the liquidity of the loans provided to the …rm initially.
The externality that gives rise to a …re sale in our model is a direct result
of trade being mediated by over-the-counter markets. These markets generate
a search externality, where sellers of collateral can crowd out investors because
both of these parties are searching for the same thing: liquidity. If, alternatively, we model exchange as occurring on purely competitive markets, then the
externality disappears. In particular, the optimal bankruptcy policy would exempt the automatic stay because the exemption gives all agents an opportunity
17
to seek liquidity, and competitive markets ensure that liquidity ends up with
agents that place the highest value on it. Although this result is interesting, it is
not particularly helpful or relevant from a policy perspective, precisely because
repo markets are not competitive— they are over-the-counter. Given this, we
would argue that the externality that is central to our analysis is both appropriate and realistic. It also turns out that it is also extremely tractable to analyze,
although other forms of externality, for example through cash-in-the-market
pricing (Allen and Gale 2007), will yield similar results.
Finally, there may be ways to reduce the risk of …re sales that do not require
a change to the bankruptcy code. For example, repos could be cleared by a
central counter party, CCP. A CCP interposes itself between the borrower and
the lender, where the CCP holds the collateral and margins of the parties and
is legally responsible for making the second leg repo transactions. If the CCP
is well managed, it would have access to liquidity in the event of a borrower
default, and, as a result, could dispose of the defaulted borrower’s collateral in
a non-disruptive way. As well, CCP members— i.e., the borrowers and lenders—
can directly confront the externality identi…ed in our model by agreeing to a set
of rules regarding disposition of collateral that minimize the risk of …re sales. A
repo CCP, however, faces the same challenges that any CCP face in the event of
multiple simultaneous defaults by members. In terms of the repo CCP, it may
default on its promised payments if it does not liquidate a substantial portion
of its collateral. But such a liquidation could result in …re-sale prices.
6
References
Allen, F. and D. Gale, 2007, Understanding Financial Crises, Oxford University
Press.
Acharya, Viral V., V. Ravi Anshuman, and S. Viswanathan. 2012. “Bankruptcy Exemption of Repo Markets: Too Much Today for Too Little Tomorrow?” Manuscript.
Bolton, Patrick and Martin Oehmke. 2011. “Should Derivatives be Privileged in Bankruptcy?” NBER working paper 17599.
Du¢ e, Darell, and David Skeel. 2012. “A Dialogue on the Costs and Bene…ts
of Automatic Stays for Derivatives and Repurchase Agreements.” Rock Center
for Corporate Governance at Stanford University Working Paper No. 108.
French, K R, M N Baily, J Campbell, J H Cochrane, D W Diamond, D
Du¢ e, A K Kashyap, F S Mishkin, R G Rajan, D S Scharfstein, R J Shiller,
H S Shin, M J Slaughter, J C Stein and R M Stulz. 2010. The Squam Lake
Report: Fixing the Financial System. Princeton University Press.
Garbade, Kenneth D. 2006, “The Evolution of Repo Contracting Conventions in the 1980s,”Federal Reserve Bank of New York, Economic Policy Review,
May 2006 Volume 12, Number 1, pp. 27.42.
McAndrews, James J., and William Roberds. 2003. “Payment Intermediation and the Origins of Banking,” manuscript.
18
Mills, Jr., David C. and Robert R. Reed. 2012. “Default Risk and Collateral
in the Absence of Commitment,” manuscript.
Roe, Mark. 2011. “The Derivatives Players’Payment Priorities as Financial
Crisis Accelerator.” Stanford Law Review 63.
Shleifer, Andrei and Robert Vishny. 2011. “Fire Sales in Finance and Macroeconomics,” Journal of Economic Perspectives, Winter, 2011.
19
Working Paper Series
A series of research studies on regional economic issues relating to the Seventh Federal
Reserve District, and on financial and economic topics.
Why Has Home Ownership Fallen Among the Young?
Jonas D.M. Fisher and Martin Gervais
WP-09-01
Why do the Elderly Save? The Role of Medical Expenses
Mariacristina De Nardi, Eric French, and John Bailey Jones
WP-09-02
Using Stock Returns to Identify Government Spending Shocks
Jonas D.M. Fisher and Ryan Peters
WP-09-03
Stochastic Volatility
Torben G. Andersen and Luca Benzoni
WP-09-04
The Effect of Disability Insurance Receipt on Labor Supply
Eric French and Jae Song
WP-09-05
CEO Overconfidence and Dividend Policy
Sanjay Deshmukh, Anand M. Goel, and Keith M. Howe
WP-09-06
Do Financial Counseling Mandates Improve Mortgage Choice and Performance?
Evidence from a Legislative Experiment
Sumit Agarwal,Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet,
and Douglas D. Evanoff
WP-09-07
Perverse Incentives at the Banks? Evidence from a Natural Experiment
Sumit Agarwal and Faye H. Wang
WP-09-08
Pay for Percentile
Gadi Barlevy and Derek Neal
WP-09-09
The Life and Times of Nicolas Dutot
François R. Velde
WP-09-10
Regulating Two-Sided Markets: An Empirical Investigation
Santiago Carbó Valverde, Sujit Chakravorti, and Francisco Rodriguez Fernandez
WP-09-11
The Case of the Undying Debt
François R. Velde
WP-09-12
Paying for Performance: The Education Impacts of a Community College Scholarship
Program for Low-income Adults
Lisa Barrow, Lashawn Richburg-Hayes, Cecilia Elena Rouse, and Thomas Brock
Establishments Dynamics, Vacancies and Unemployment: A Neoclassical Synthesis
Marcelo Veracierto
WP-09-13
WP-09-14
1
Working Paper Series (continued)
The Price of Gasoline and the Demand for Fuel Economy:
Evidence from Monthly New Vehicles Sales Data
Thomas Klier and Joshua Linn
WP-09-15
Estimation of a Transformation Model with Truncation,
Interval Observation and Time-Varying Covariates
Bo E. Honoré and Luojia Hu
WP-09-16
Self-Enforcing Trade Agreements: Evidence from Time-Varying Trade Policy
Chad P. Bown and Meredith A. Crowley
WP-09-17
Too much right can make a wrong: Setting the stage for the financial crisis
Richard J. Rosen
WP-09-18
Can Structural Small Open Economy Models Account
for the Influence of Foreign Disturbances?
Alejandro Justiniano and Bruce Preston
WP-09-19
Liquidity Constraints of the Middle Class
Jeffrey R. Campbell and Zvi Hercowitz
WP-09-20
Monetary Policy and Uncertainty in an Empirical Small Open Economy Model
Alejandro Justiniano and Bruce Preston
WP-09-21
Firm boundaries and buyer-supplier match in market transaction:
IT system procurement of U.S. credit unions
Yukako Ono and Junichi Suzuki
Health and the Savings of Insured Versus Uninsured, Working-Age Households in the U.S.
Maude Toussaint-Comeau and Jonathan Hartley
WP-09-22
WP-09-23
The Economics of “Radiator Springs:” Industry Dynamics, Sunk Costs, and
Spatial Demand Shifts
Jeffrey R. Campbell and Thomas N. Hubbard
WP-09-24
On the Relationship between Mobility, Population Growth, and
Capital Spending in the United States
Marco Bassetto and Leslie McGranahan
WP-09-25
The Impact of Rosenwald Schools on Black Achievement
Daniel Aaronson and Bhashkar Mazumder
WP-09-26
Comment on “Letting Different Views about Business Cycles Compete”
Jonas D.M. Fisher
WP-10-01
Macroeconomic Implications of Agglomeration
Morris A. Davis, Jonas D.M. Fisher and Toni M. Whited
WP-10-02
Accounting for non-annuitization
Svetlana Pashchenko
WP-10-03
2
Working Paper Series (continued)
Robustness and Macroeconomic Policy
Gadi Barlevy
WP-10-04
Benefits of Relationship Banking: Evidence from Consumer Credit Markets
Sumit Agarwal, Souphala Chomsisengphet, Chunlin Liu, and Nicholas S. Souleles
WP-10-05
The Effect of Sales Tax Holidays on Household Consumption Patterns
Nathan Marwell and Leslie McGranahan
WP-10-06
Gathering Insights on the Forest from the Trees: A New Metric for Financial Conditions
Scott Brave and R. Andrew Butters
WP-10-07
Identification of Models of the Labor Market
Eric French and Christopher Taber
WP-10-08
Public Pensions and Labor Supply Over the Life Cycle
Eric French and John Jones
WP-10-09
Explaining Asset Pricing Puzzles Associated with the 1987 Market Crash
Luca Benzoni, Pierre Collin-Dufresne, and Robert S. Goldstein
WP-10-10
Prenatal Sex Selection and Girls’ Well‐Being: Evidence from India
Luojia Hu and Analía Schlosser
WP-10-11
Mortgage Choices and Housing Speculation
Gadi Barlevy and Jonas D.M. Fisher
WP-10-12
Did Adhering to the Gold Standard Reduce the Cost of Capital?
Ron Alquist and Benjamin Chabot
WP-10-13
Introduction to the Macroeconomic Dynamics:
Special issues on money, credit, and liquidity
Ed Nosal, Christopher Waller, and Randall Wright
WP-10-14
Summer Workshop on Money, Banking, Payments and Finance: An Overview
Ed Nosal and Randall Wright
WP-10-15
Cognitive Abilities and Household Financial Decision Making
Sumit Agarwal and Bhashkar Mazumder
WP-10-16
Complex Mortgages
Gene Amromin, Jennifer Huang, Clemens Sialm, and Edward Zhong
WP-10-17
The Role of Housing in Labor Reallocation
Morris Davis, Jonas Fisher, and Marcelo Veracierto
WP-10-18
Why Do Banks Reward their Customers to Use their Credit Cards?
Sumit Agarwal, Sujit Chakravorti, and Anna Lunn
WP-10-19
3
Working Paper Series (continued)
The impact of the originate-to-distribute model on banks
before and during the financial crisis
Richard J. Rosen
WP-10-20
Simple Markov-Perfect Industry Dynamics
Jaap H. Abbring, Jeffrey R. Campbell, and Nan Yang
WP-10-21
Commodity Money with Frequent Search
Ezra Oberfield and Nicholas Trachter
WP-10-22
Corporate Average Fuel Economy Standards and the Market for New Vehicles
Thomas Klier and Joshua Linn
WP-11-01
The Role of Securitization in Mortgage Renegotiation
Sumit Agarwal, Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet,
and Douglas D. Evanoff
WP-11-02
Market-Based Loss Mitigation Practices for Troubled Mortgages
Following the Financial Crisis
Sumit Agarwal, Gene Amromin, Itzhak Ben-David, Souphala Chomsisengphet,
and Douglas D. Evanoff
WP-11-03
Federal Reserve Policies and Financial Market Conditions During the Crisis
Scott A. Brave and Hesna Genay
WP-11-04
The Financial Labor Supply Accelerator
Jeffrey R. Campbell and Zvi Hercowitz
WP-11-05
Survival and long-run dynamics with heterogeneous beliefs under recursive preferences
Jaroslav Borovička
WP-11-06
A Leverage-based Model of Speculative Bubbles (Revised)
Gadi Barlevy
WP-11-07
Estimation of Panel Data Regression Models with Two-Sided Censoring or Truncation
Sule Alan, Bo E. Honoré, Luojia Hu, and Søren Leth–Petersen
WP-11-08
Fertility Transitions Along the Extensive and Intensive Margins
Daniel Aaronson, Fabian Lange, and Bhashkar Mazumder
WP-11-09
Black-White Differences in Intergenerational Economic Mobility in the US
Bhashkar Mazumder
WP-11-10
Can Standard Preferences Explain the Prices of Out-of-the-Money S&P 500 Put Options?
Luca Benzoni, Pierre Collin-Dufresne, and Robert S. Goldstein
WP-11-11
Business Networks, Production Chains, and Productivity:
A Theory of Input-Output Architecture
Ezra Oberfield
Equilibrium Bank Runs Revisited
Ed Nosal
WP-11-12
WP-11-13
4
Working Paper Series (continued)
Are Covered Bonds a Substitute for Mortgage-Backed Securities?
Santiago Carbó-Valverde, Richard J. Rosen, and Francisco Rodríguez-Fernández
WP-11-14
The Cost of Banking Panics in an Age before “Too Big to Fail”
Benjamin Chabot
WP-11-15
Import Protection, Business Cycles, and Exchange Rates:
Evidence from the Great Recession
Chad P. Bown and Meredith A. Crowley
WP-11-16
Examining Macroeconomic Models through the Lens of Asset Pricing
Jaroslav Borovička and Lars Peter Hansen
WP-12-01
The Chicago Fed DSGE Model
Scott A. Brave, Jeffrey R. Campbell, Jonas D.M. Fisher, and Alejandro Justiniano
WP-12-02
Macroeconomic Effects of Federal Reserve Forward Guidance
Jeffrey R. Campbell, Charles L. Evans, Jonas D.M. Fisher, and Alejandro Justiniano
WP-12-03
Modeling Credit Contagion via the Updating of Fragile Beliefs
Luca Benzoni, Pierre Collin-Dufresne, Robert S. Goldstein, and Jean Helwege
WP-12-04
Signaling Effects of Monetary Policy
Leonardo Melosi
WP-12-05
Empirical Research on Sovereign Debt and Default
Michael Tomz and Mark L. J. Wright
WP-12-06
Credit Risk and Disaster Risk
François Gourio
WP-12-07
From the Horse’s Mouth: How do Investor Expectations of Risk and Return
Vary with Economic Conditions?
Gene Amromin and Steven A. Sharpe
WP-12-08
Using Vehicle Taxes To Reduce Carbon Dioxide Emissions Rates of
New Passenger Vehicles: Evidence from France, Germany, and Sweden
Thomas Klier and Joshua Linn
WP-12-09
Spending Responses to State Sales Tax Holidays
Sumit Agarwal and Leslie McGranahan
WP-12-10
Micro Data and Macro Technology
Ezra Oberfield and Devesh Raval
WP-12-11
The Effect of Disability Insurance Receipt on Labor Supply: A Dynamic Analysis
Eric French and Jae Song
WP-12-12
5
Working Paper Series (continued)
Medicaid Insurance in Old Age
Mariacristina De Nardi, Eric French, and John Bailey Jones
WP-12-13
Fetal Origins and Parental Responses
Douglas Almond and Bhashkar Mazumder
WP-12-14
Repos, Fire Sales, and Bankruptcy Policy
Gaetano Antinolfi, Francesca Carapella, Charles Kahn, Antoine Martin,
David Mills, and Ed Nosal
WP-12-15
6
@FedFRASER