Securitization and Asset Prices · 2014-09-04 · Introduction Securitization and Financial Markets I Securitization volume has increase substantially. I Increase in securitization

Securitization and Asset Prices

Yunus Aksoy (Birkbeck, London) Henrique S. Basso (Banco de Espana)

IX Annual Seminar on Risk, Financial Stability and Banking

14th August 2014

Introduction

Jan−93 Jan−94 Feb−95 Feb−96 Mar−97 Apr−98 Apr−99 May−00 Jun−01 Jun−02 Jul−03 Aug−04 Aug−05 Sep−06 Oct−07 Oct−08 Nov−09 Dec−100

50

100

150

200

abs mbs all (in billion USD)

Figure: Securitization in the United States

Aksoy and Basso (IX Annual Seminar on Risk, Financial Stability and Banking) 14th August 2014 2 / 43

Introduction


1

2

3

4

5

6

7

8

Figure: Ratio of ABS Securitization to Total Market Cap (Wilshire 5000)


Introduction


0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Non−financial corporates Household Commercial Banks Shadow Banks and Sec Brokers

Figure: (Growth of Assets in Different Sectors)


Introduction

Securitization and Financial Markets

I Securitization volume has increase substantially.

I Increase in securitization and asset holdings of FI seem to correlate.

I These financial institutions hold more diverse portfolio of assets.

I Securitization through FI’s portfolio choice may affect asset classesother than credit.

I Closely related to work by Tobias Adrian and co-authors. By lookingat securitization activity and portfolio choice we attempt to shed lighton the mechanism.


Introduction

This paper

I Empirically: Assess the link between the volume of securitization andasset prices (Equity and Bonds)

Question 1 - Does securitization affect risk premia in various assetclasses?

I Theoretically: Look at the importance of financial intermediaries (in abroad sense) portfolio decisions to explain the link betweensecuritization and risk premia. Explore the importance of asymmetricinformation and banks as marginal price setters (see also He andKrishnamurthy (2013), Adrian, Etula, and Muir (2013)).

Question 2 - Can we provide a rationale/mechanism behind thiseffect?


Introduction

What We Find

I Empirically: volume of securitization impact risk premia (monthlyfrequency). Increase in securitization (particularly asset backedsecurities) lead to lower term spread, bond and equity premium.

I Theoretically: develop a model of securitization as a source of fundingto finance the bank’s purchase of diversified asset portfolio, providinga rationale for the empirical results. The main driver of the volume ofsecuritization is the degree asymmetric information.

I Mechanism: securitization relaxes cash and risk constraints faced bythe banks allowing expansion of bank’s balance sheet holding equityconstant (leverage).


Empirical Analysis

Benchmark Estimation

I Focus on dynamics of asset prices. Use Campbell, Chan, and Viceira(2003) VAR plus measure of securitization volume. The monthlymoving average specification is given by

zt = B(L)ut ,

z′t =[$i ′

t , x′2t

]with $i

t the securitization measure and i = {dabst ,dmbst}

I x2t = [spreadt , rpdt , xbrt , xrt , realr 3mt ].

I Identification - securitization volume is the month in which auction oftranches took place. The volume of asset involved is normally decidedin advanced to ensure ratings is done and SPV is properly set up.Hence, securitization can only respond to asset prices with a lag.


Empirical Analysis

Data

I Dealogic: US asset (dabs) and mortgage (dmbs) backed securities (inannualized monthly log differences).

Dealogic Sample

I Equity Premium (xr from Fama-French): excess return on the marketover the risk free rate (3 months T-Bill rate).

I Bond Premium (xbr) over 2,3,4 and 5 years horizons.(based onFama-Bliss Discount Bonds as reported by CRSP)

I Yield spreads (spread): difference between the five year governmentbond rate and 3 months T-Bill in percentages per annum.

I Monthly real price dividend ratio (rpd) that is calculated using thelog difference in real dividends and real stock prices (S&P CompositeStock Price Index)

I Real short term rates (realr 3m): 3 months T-Bill rate and the CPIinflation.

I Sample period: January 1993 - November 2007.


Empirical Analysis

Term Spread

2 4 6 8 10−1.5

−1

−0.5

0

0.5

1

1.5x 10

−3 Bond Premium

2 4 6 8 10

−2

−1

0

1

2

x 10−5 Equity Premium

2 4 6 8 10−1.5

−1

−0.5

0

0.5

1

1.5x 10

−4

2 4 6 8 10−1.5

−1

−0.5

0

0.5

1

1.5x 10

−3

2 4 6 8 10

−2

−1

0

1

2

x 10−5

2 4 6 8 10−1.5

−1

−0.5

0

0.5

1

1.5x 10

−4

ABSShock

MBSShock

Figure: Benchmark: ABS and MBS

Extra


Empirical Analysis

Introducing ControlsNew vector of variables - z′t =

[$i ′

t , xa2t , xb′2t

]with

xb2t = [spreadt , rpdt , xbrt , xrt , realr 3mt ]

I Market volatility measure (vix): a measure for risk; use the CBOEVolatility Index (S&P 500 market index option prices) as provided byBloomberg.

I Changes in expected Earnings per Share (deps): the twelve monthsforward weighted average expected earnings per share based on S&P500 composite (I/B/E/S).

I Expected Future Economic Conditions (E5Y ): an index that isderived from a five years forward looking question on confidence fromthe Michigan Index of Consumer Expectations

I Credit Spread index (GZ ) - Gilchrist and Zakrajsek (2012): controllingfor the evolution of credit supply/ credit market conditions.

I Conditional Heteroscedastic Discount Factor (CPFactor) (Cochraneand Piazzesi (2005))


Empirical Analysis

Figure: Augmented Models - Results

0 2 4 6 8 10 12 14 16 18 20−0.1

−0.05

0

0.05

0.1

Horizon

Per

cent

Term Spread

benchmark vix e5y deps gz cp

0 2 4 6 8 10 12 14 16 18 20−3

−2

−1

0x 10

−3

Horizon

Per

cent

Bond Excess Return

0 2 4 6 8 10 12 14 16 18 20−0.01

−0.005

0

0.005

0.01

Horizon

Per

cent

Equity Excess Return

(a) Impulse Response to Asset-backed Securities

Other


Theoretical Framework

Model Overview

We build a partial equilibrium model focusing on two key decisions of abank

1. Securitization decision (design and sale of synthetic securities F ,whose value is a function of basket of credit assets [Yi ]

ni=1) following

DeMarzo and Duffie (1999) closely. Security Design: Risky debt -F ∗(Y ) = min(d ,Y n).

2. Portfolio decision - which assets (credit basket, equity, bonds) to holdand external funding to use.

See Timeline



Securitization Volume (Sale) in a nutshell

I Main issue - asymmetric information. Banks know more about [Yi ]ni=1

and thus F than final investor. Yi = Xi + Zi , Xi ∈ [X0,X1] isbank/SPV information, Zi is the remaining risk the bank faces.

I If SPV tries to sell all synthetic assets F created ⇒ investor concludelemons, pay low price

I Thus, if information is good, SPV is not willing to sell everything atsuch low price, hence decide to keep some tranches in balance sheet

I As SPV is willing to hold stuff ⇒ signal to investor that assets aregood, investor bid higher price

I Equilibrium fully revealing ⇒ banks/SPV hold exact amount (1-q)such that investors bid true value of asset based on information.

Detail Securitization



Portfolio Decision in a nutshell

I Banks will select a portfolio of assets (Qy ,QB ,QE ,Υ) to maximizeexpected returns (profit) s.t.

I (i) a cash constraintI (ii) a risk constraint (akin to Value-at-risk)

I Variance Covariance matrix given. As the bank increases Qy creditmark-up decreases. As the bank increases QB/ QE price ofbond/equity increases. As the bank increases Υ, cost of fundingincreases.

Detail Portfolio Problem



Figure: Model with and without Securitization - Mechanism

Assets0 Assets0Assets0 Υ ΥΥ

Γ0 Γ0Γ0

qFqFCash Assets1

1

(a) Balance Sheet ExpansionPay-off Credit

−Xn

Xn

Y nY nY n

Pay-out Synthetic Sec. (F )

PF

(PF−d)

dd

Combined Portfolio Pay-off

(PF−Xn)

1

(b) Risk Profile



Figure: Benchmark Model

0 0.05 0.1 0.15 0.2−0.4

−0.39

−0.38

−0.37

−0.36

−0.35

−0.34

−0.33Bond Premium − %Change

Information Asymmetry0 0.05 0.1 0.15 0.2

−0.36

−0.35

−0.34

−0.33

−0.32

−0.31

−0.3Equity Premium − %Change

Information Asymmetry

Effect of Allowing Banks to Securitize Credit Assets (Model with Sec vs Model No Sec)

0 0.05 0.1 0.15 0.2−0.014

−0.013

−0.012

−0.011

−0.01

−0.009

−0.008Credit Spread − %Change


0 0.05 0.1 0.15 0.20.94

0.95

0.96

0.97

0.98

0.99

1Share of Securitized Asset Sold − q


0.36

0.365

0.37

0.375

0.38

0.385

0.39

0.395

0.4Balance Sheet − %Change


0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45Ratio of Funding Sources to Total BalanceSheet


ϒ − No Sec ϒ − with Sec Vol of Sec



Figure: Model with and without Securitization - Mechanism

Assets0 Assets0Assets0 Υ ΥΥ

Γ0 Γ0Γ0

qFqFCash Assets1

1

(a) Balance Sheet ExpansionPay-off Credit

−Xn

Xn

Y nY nY n

Pay-out Synthetic Sec. (F )

PF

(PF−d)

dd

Combined Portfolio Pay-off

(PF−Xn)

1

(b) Risk Profile



Figure: Higher Face Value of Synthetic Security

0 0.05 0.1 0.15 0.2−0.55

−0.5

−0.45

−0.4

−0.35

−0.3

−0.25

−0.2



−0.65

−0.6

−0.55

−0.5

−0.45

−0.4

−0.35

−0.3

−0.25

−0.2

Equity Premium − %Change


0 0.05 0.1 0.15 0.20.2

0.4

0.6

0.8

1



0.4

0.5

0.6

0.7

0.8

0.9



Higher Face Value of Synthetic Security (d) Benchmark

Go to Sensitivity Analysis


Conclusion

Discussion

I Pooling and tranching of assets held in the balance sheet of financialcompanies not only creates liquidity, alleviating their cash constraint,but also relaxes their risk constraint. That allow banks to expandasset holdings, depressing risk premia.

I As a result, although the payoff outlook of assets has remain thesame, as do their risk profile, the compensation to undertaking risk inthe economy decreases. As Rajan (2005) pointed out risks are stillthere.

I Securitization volumes are related to degree of informationasymmetry. Thus, our results indicate the key variables to understandthe link between financial intermediation and asset prices is thedegree of information asymmetry.


Conclusion

Discussion II

I Our results highlight the importance of looking at financialintermediation, as well as final investors, to increase ourunderstanding of the dynamic movements in asset prices. (He andKrishnamurthy (2013) and Adrian, Etula, and Muir (2013) point toimportance of financial institutions).

I In 2005/2006 commentators pointed out the yield curve conundrum.Although short-term rates increased substantially, long-term ratesremained unchanged (fall in term premium). General view - somecombination of diminished macroeconomic and financial marketvolatility.

I Our work provide a different channel. The period of 2003 - 2006 weobserved a sharp and consistent increase in the volume of ABStransacted in the US. That would have allow banks to increase theirasset allocations, leading to a downward pressure on risk premia.


Conclusion

Figure: ABS issuance versus Bond Premium

−0.02

−0.01

0

0.01

0.02

0.03Bo

nd P

rem

ium

Jan93 Jan94 Feb95 Feb96 Mar97 Apr98 Apr99 May00 Jun01 Jun02 Jul03 Aug04 Aug05 Sep06 Oct07 Oct08 Nov09 Dec100

20

40

60

80

100

abs

Bond Premium abs (in billion USD)


Conclusion

Going Forward

I Looking at retention, price discount and tranching strategies (settingd), can we link these to volume?

I Extend the model to general equilibrium, embedding into DSGE.

I Study information acquisition process to understand better theunderlying mechanism


Appendix

Pricing Date Deal Total

Value $ (Face)

Issuer Collateral Type

Maturity Date

Years to Maturity

Tranche Value $ (Face)

Bank Parent Bookrunner Parent

Deal Bank Parent Issue Type Coupon Yield to

Maturity

22-May-90 1,250,000,000

Standard Credit Card Master Trust Series 1990-5

Credit-card receivables 10-Jun-95 5.01389 1,250,000,000

JPMorgan; UBS; Goldman Sachs; Deutsche Bank; BNP Paribas; Nomura; RBS; Daiwa Securities; Credit Suisse; Citi; Bank of America Merrill Lynch Citi

JPMorgan; UBS; Goldman Sachs; Deutsche Bank; BNP Paribas; Nomura; RBS; Daiwa Securities; Credit Suisse; Citi; Bank of America Merrill Lynch Fixed rate 9.375 9.686

22-May-90 155,000,000

Standard Credit Card Master Trust Series 1990-5

Credit-card receivables 10-Jun-95 5.01389 155,000,000 Citi Citi Citi Fixed rate 9.625 10.000

19-Jun-90 1,000,000,000

First Chicago Master Trust II Series 1990 - A

Credit-card receivables 15-Jun-95 4.96389 1,000,000,000

Mizuho; JPMorgan; UBS; Goldman Sachs; Deutsche Bank; Nomura; RBS; SG Corporate & Investment Banking; Credit Suisse; Citi Credit Suisse

Mizuho; JPMorgan; UBS; Goldman Sachs; Deutsche Bank; Nomura; RBS; SG Corporate & Investment Banking; Credit Suisse; Citi Fixed rate 9.25 9.485

20-Jun-90 155,000,000

Standard Credit Card Master Trust 1 Series 1990-6

Credit-card receivables 10-Jul-97 7.03333 155,000,000

Credit Suisse; Citi Credit Suisse

Credit Suisse; Citi Fixed rate 9.625 9.994

20-Jun-90 1,095,000,000

Standard Credit Card Master Trust 1 Series 1990-6

Credit-card receivables 10-Jul-97 7.03333 1,095,000,000

Mizuho; JPMorgan; UBS; Goldman Sachs; BNP Paribas; Nomura; ING; Daiwa Securities; Credit Suisse; Citi; Credit Suisse

Mizuho; JPMorgan; UBS; Goldman Sachs; BNP Paribas; Nomura; ING; Daiwa Securities; Credit Suisse; Citi; Fixed rate 9.375 9.679

(a) Dealogic Sample

Back


Appendix

Term Spread

2 4 6 8 10−5

0

5x 10

−3 Bond Premium

2 4 6 8 10−5

0

5x 10

−5 Equity Premium

2 4 6 8 10−5

0

5x 10

−3

2 4 6 8 10−5

0

5x 10

−3

2 4 6 8 10−5

0

5x 10

−5

2 4 6 8 10−5

0

5x 10

−3

2 4 6 8 10−5

0

5x 10

−3

2 4 6 8 10−5

0

5x 10

−5

2 4 6 8 10−5

0

5x 10

−3

ABS Shock

Brokers Shock

Shadow Shock

Figure: Quaterly VAR - ABS vs Asset Growth

Back


Appendix

What is left?

I Potential Explanation - Securitization creates a new contingent assethelping economy to get close to completing the market. Thatdecreases the risk premia in the economy. (see for instance Allen andGale (1994))

I Problem: if that were to be the case we should see together with thetrend in securitization, which include different original assets, to beaccompanied by a trend on premia. We do not see that.

I Also Simsek (2013) argues that under belief disagreement, new assetsmight improve insurance but also open opportunities for more risktaking (speculative trading).

I Hence, we try to look at the drivers of securitization and portfoliochoice of financial intermediaries to try and provide a rationale for ourempirical results.

Back


Appendix

-0.2

-0.15

-0.1

-0.05

0

0.05

0.1

0.15

1993

M01

19

93M

06

1993

M11

19

94M

04

1994

M09

19

95M

02

1995

M07

19

95M

12

1996

M05

19

96M

10

1997

M03

19

97M

08

1998

M01

19

98M

06

1998

M11

19

99M

04

1999

M09

20

00M

02

2000

M07

20

00M

12

2001

M05

20

01M

10

2002

M03

20

02M

08

2003

M01

20

03M

06

2003

M11

20

04M

04

2004

M09

20

05M

02

2005

M07

20

05M

12

2006

M05

20

06M

10

2007

M03

20

07M

08

2008

M01

20

08M

06

2008

M11

20

09M

04

2009

M09

20

10M

02

2010

M07

20

10M

12

2011

M05

20

11M

10

2012

M03

20

12M

08

Equity Premium (Fama-French)

Figure: Equity Premium


Appendix

Figure: Model Timeline

Bank sets up the SPV

Selects the design of

synthetic security (F )

given preference

for cash δ.

Payoff Information (X)

revealed. Bank selects

assets {[Yi]ni=1, B,E},

funding Υ. Security F

is created at SPV.

Assets are transacted.

SPV sells a fraction q of

synthetic securities, and

transfers cash to Bank.

Assets pay-out

Bank’s retained earnings

and preference for

cash are determined.

1 2 3 4

1

Back


Appendix

Theoretical Framework - Key features I

I Payoff of credit asset is given by Yi = Xi + Zi .

I Xi ∈ [X0,X1] is the bank’s private information about credit payoff.Zi = εi + η is the remaining risk the bank faces.

I A fourth asset, denoted F , whose payoff depend on Yi will be createdby the bank (SPV).

I Denote f0 = E [F | X = X0] and f = E [F | X ]

I Information asymmetry: final investor do not know X , but know thatit is ∈ [X0,X1].


Appendix

1) Securitization Decision

I We solve it backwards, first looking at the creation of SPV andissuance and then looking at the optimal design of F .

a) Issuance and marketing of synthetic securities F

b) Optimal Design of F


Appendix

a) Issuance Decision I

I Main issue - asymmetric information. Banks know more than finalinvestor.

I If bank tries to sell all assets created ⇒ investor conclude lemons, paylow price

I Thus, if information is good, bank is not willing to sell everything atsuch low price, hence decide to keep some F in balance sheet.

I As bank is willing to hold stuff ⇒ signal to investor that assets aregood, investor bid higher price

I Equilibrium fully revealing ⇒ banks hold exact amount such thatinvestors bid true value of asset based on information.


Appendix

1) a) Issuance Decision II

I Thus, the equilibrium in the market of F is essentially the result of asignalling game between the issuer and buyer of the security. Thegame ensures that true value of the security is revealed to buyers,based on the share of F held by the bank (SPV).

I The unique separating equilibrium is

q = (f /f0)−1/(1−δ) and PF = f0(q)δ−1 = f (1)

I The ratio f0/f implicitly determines the information advantage of thebank on the valuation of security f . Greater is this advantage, lowerq, the proportion of F sold to final investors.


Appendix

1) b) Optimal Design of F

DeMarzo and Duffie (1999) and DeMarzo (2005) show that optimalmonotone security design is a standard debt contract, thusF ∗(Y ) = min(d ,Y n) for a constant d .

I Assume that the bank/SPV would like to maximize the volume ofsecuritized assets (cash preference)

I but in order to close the informational gap in a fully revealingequilibrium, it is forced to retain some synthetic assets in the portfoliowhen the signal is good (higher information asymmetry).

I Thus, it is optimal for the SPV to select a security that is as payoffinsensitive as possible for the range of signals where asymmetry is atits highest, thus making the retention a more powerful signal.

I Standard debt has this property since f does not change significantlyas X increases in the range X > d .


Appendix

Figure: Choice of d (Synthetic Security Payoff for the Final Investor and Optimald)

X0

X0 X1X1 dd

Y nY n

d−PF (d)d−PF (d)

−PF (d)

AB

Pay-off of Synthetic Security Pay-off of Synthetic Security

−PF (d)

1

Back


Appendix

2) Portfolio Decision I

I Banks select the portfolio composition (Qy ,QB ,QE ,Υ) to maximizethe expected profits E [ΠB | X nΓ0]

ΠB |X n = Qy (Y n−E [Y n|X n])+µ(X n,Qy )Qy

+QB(VB−PB)+QE (VE−PE )−qQy (F−PF )−RF Υ (2)

I subject to a cash constraint

QyE [Y n | X n] + QBPB + QEPE 6 Γ0 + qQyPF + Υ (3)

I and a risk constraintσΠB 6 χΓ0. (4)

I due to the diversification of idiosyncratic risks (εi ), the only source ofrisk of the basket of credit comes from the aggregate uncertainty (η).


Appendix

2) Portfolio Decision II

Market Structure

I The cost of bank borrowing is given by RF = R + κ(Υ/Γ0).

I Bank mark-up on loans µ(X n,Qy ) = µ+ µ1(X n − X0)/X0 − µ2Qy .

I Price of Equity and Bonds

PB = αB + βBQB (5)

PE = αE + βEQE . (6)

I The standard deviation of the portfolio returns is given by

σΠB |X n =

(Q2

yσ2+Q2

Bσ2B+Q2

Eσ2E+2QyQBσyB+2QyQEσyE+

2QBQEσEB−2qQ2yσFY−2qQyQBσFB−2qQyQEσFE+q2Q2

yσ2F

)1/2

.


Appendix

Parametrization

Table: Parameter Values - Benchmark Model

Return Variance Bank$E 0.03+1/0.95 σY 0.1 δ 0.983$B 1/0.95 σB 0.02 Γ0 5αB $B - 0.02 σE 0.06 χ 0.07αE $B +0.01 ρEY -0.49βB

1Γ0

0.025 ρEB -0.57

βE1

Γ00.025 ρBY -0.4

R 0.01κ 1

Γ00.05 Information

µ1 0.01 X0 1.02µ2

1Γ0

0.005 m 0.2

µ 0.04 X1 X0 + m

Back


Appendix

Sensitivity Analysis

Changing key parameter values used in the benchmark model.

I increase the standard deviation of all assets (σ, σB , σE ).

I decrease the price sensitivity of bank demand for credit, bonds, equityand bank borrowing, altering respectively (µ2, βB , βE and κ)

I alter the correlation structure of asset payoffs

1. reverse the sign of all correlation, so assets are all positively correlated,2. reverse only the credit and equity correlation, leaving government

bonds to be negatively correlated to equity and credit.

I decrease the risk aversion of banks (higher χ).


Appendix

Figure: High Variance of Assets

0 0.05 0.1 0.15 0.2−0.4

−0.38

−0.36

−0.34

−0.32

−0.3



−0.36

−0.34

−0.32

−0.3

−0.28

−0.26

−0.24



0 0.05 0.1 0.15 0.20.26

0.28

0.3

0.32

0.34

0.36

0.38



0.91

0.92

0.93

0.94

0.95

0.96

0.97

0.98

0.99



High Variance of Assets Benchmark

BackAksoy and Basso (IX Annual Seminar on Risk, Financial Stability and Banking) 14th August 2014 38 / 43

Appendix

Figure: Low Price Sensitivity of Demand

0 0.05 0.1 0.15 0.2−0.4

−0.38

−0.36

−0.34

−0.32

−0.3

−0.28

−0.26



−0.36

−0.34

−0.32

−0.3

−0.28

−0.26

−0.24

−0.22



0 0.05 0.1 0.15 0.20.33

0.34

0.35

0.36

0.37

0.38

0.39



0.94

0.95

0.96

0.97

0.98

0.99



Asset Prices less Sensitive to Bank Demand Benchmark

Back


Appendix

Figure: Correlation Structure

0 0.05 0.1 0.15 0.2−0.8

−0.7

−0.6

−0.5

−0.4


0 0.05 0.1 0.15 0.2−0.4

−0.35

−0.3

−0.25

−0.2

−0.15

−0.1


0 0.05 0.1 0.15 0.20.3

0.32

0.34

0.36

0.38

0.4

0.42



Reverse Sign of All Correlations Benchmark Reverse the Sign of Credit−Equity Correlations

0 0.05 0.1 0.15 0.20.94

0.95

0.96

0.97

0.98

0.99



BackAksoy and Basso (IX Annual Seminar on Risk, Financial Stability and Banking) 14th August 2014 40 / 43

Appendix

Figure: Lower Bank Risk Aversion

0 0.05 0.1 0.15 0.2−0.42

−0.4

−0.38

−0.36

−0.34



−0.42

−0.4

−0.38

−0.36

−0.34

−0.32



0 0.05 0.1 0.15 0.20.36

0.38

0.4

0.42

0.44



0.94

0.95

0.96

0.97

0.98

0.99



Laxer Bank Risk Constraint Benchmark

Back


Appendix

References I

Adrian, T., E. Etula, and T. Muir (2013): “FinancialIntermediaries and the Cross-Section of Stock Returns,” Journal ofFinance, forthcoming.

Allen, F., and D. Gale (1994): Financial Innovation and RiskSharing. MIT Press, Cambridge, MA.

Campbell, J. Y., Y. L. Chan, and L. M. Viceira (2003): “Amultivariate model of strategic asset allocation,” Journal of FinancialEconomics, 67(1), 41–80.

Cochrane, J. H., and M. Piazzesi (2005): “Bond Risk Premia,”American Economic Review, 95(1), 138–160.

DeMarzo, P., and D. Duffie (1999): “A Liquidity-Based Model ofSecurity Design,” Econometrica, 67(1), 65–100.


Appendix

References II

DeMarzo, P. M. (2005): “The Pooling and Tranching of Securities: AModel of Informed Intermediation,” Review of Financial Studies, 18(1),1–35.

Gilchrist, S., and E. Zakrajsek (2012): “Credit Spreads andBusiness Cycle Fluctuations,” American Economic Review, 102(4),1692–1720.

He, Z., and A. Krishnamurthy (2013): “Intermediary Asset Pricing,”American Economic Review, 103(2), 732–70.

Rajan, R. G. (2005): “Has financial development made the worldriskier?,” Proceedings, (Aug), 313–369.

Simsek, A. (2013): “Speculation and Risk Sharing with New FinancialAssets,” The Quarterly Journal of Economics, 128(3), 1365–1396.


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Securitization and Asset Prices · 2014-09-04 · Introduction Securitization and Financial Markets I Securitization volume has increase substantially. I Increase in securitization