Illiquidity or Credit Deterioration: A Study of Liquidity in the US - - PowerPoint PPT Presentation

Illiquidity or Credit Deterioration: A Study of Liquidity in the US Corporate Bond Market during Financial Crisis

Nils Friewald · WU Vienna Rainer Jankowitsch · WU Vienna Marti Subrahmanyam · New York University

Italian Treasury

Tuesday, June 28th 2011

nyustern_logo.jpg (JPEG Image, 182x161 pixels) http://www.nyucareerstudy.org/nyustern_logo.jpg 1 of 1 03/15/2010 09:08 AM

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 2/28

Liquidity is an important price factor

◮ The financial crisis has shown that credit and liquidity risk are key

determinants of asset pricing.

◮ It is important to understand their (relative) effects and how they

change during periods of crisis.

◮ It is also relevant to ask if there are interactions between these

important factors.

◮ The most affected financial markets were over-the-counter markets,

which makes research challenging.

◮ The US corporate bond market is an ideal laboratory for testing as

detailed transaction data (since 2004) are available.

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 3/28

Dramatic increase of average US corporate bond yield spread

2 4 6 8 10 Spread in % Jul 2005 Jul 2006 Jul 2007 Jul 2008 GM/Ford Crisis Normal Period Sub−prime Crisis

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 4/28

What are we doing in the paper?

◮ We employ a wide range of liquidity proxies (bond characteristics,

trading activity variables and liquidity measures) to explain yield spread (changes) while controlling for credit risk.

◮ We examine three different regimes in our sample period which

allows as to compare liquidity effects during two periods of crisis (GM/Ford crisis, sub-prime crisis) with a more normal period in between.

◮ We analyze investment vs. speculative grade bonds to provide

evidence whether liquidity is priced differently in these sub-segments.

◮ We use panel regressions and Fama-MacBeth regressions to analyze

liquidity effects.

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 5/28

Relevant papers on liquidity Impact of liquidity on asset prices

◮ Amihud and Mendelson (JFE, 1986) → liquidity priced ◮ Amihud, Mendelson and Pedersen (FTF, 2006) → overview

Evidence for corporate bond markets

◮ Longstaff, Mithal and Neis (JOF, 2005) → reduced-form models ◮ Huang and Huang (WP, 2003) → structural models ◮ Nashikkar, Subrahmanyam and Mahanti (forthcoming JFQA) →

reduced-form models with bond-level liquidity

Bond characteristics and trading activity

◮ Fisher (JPE, 1959) → first paper on liquidity effects in bonds ◮ Elton, Gruber, Agrawal and Mann (JOF, 2001) → explain part of the

bond yield spread with credit and other factors

◮ Edwards, Harris and Piwowar (JOF, 2007) → analysis of bond liquidity

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 6/28

Relevant papers on liquidity Liquidity measures

◮ Roll (JOF, 1984) → Roll measure ◮ Amihud (JFM, 2002) → Amihud measure ◮ Chen, Lesmond and Wei (JOF, 2007) → LOT measure ◮ Mahanti, Nashikkar, Subrahmanyam, Chacko and Mallik (JFE,

2008) → latent liquidity

◮ Jankowitsch, Nashikkar and Subrahmanyam (JBF, 2011) → price

dispersion measure

Liquidity studies covering the financial crisis

◮ Bao, Pan and Wang (JOF, 2011) → focus on Roll measure ◮ Dick-Nielsen, Feldh¨

utter and Lando (forthcoming JFE) → various

proxies

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 7/28

The three hypotheses that we test Hypothesis 1

Liquidity is an important price factor in the US corporate bond market.

◮ Amihud and Mendelson (1986) show that investors demand a

premium for holding illiquid assets where there is a clientele effect.

◮ Duffie et al. (2007) find that liquidity premia are driven by

transaction costs due to search frictions, inventory holding costs and bargaining power.

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 8/28

The three hypotheses that we test Hypothesis 2

Liquidity effects are more important in periods of financial distress.

◮ Duffie et al. (2007) demonstrate that in periods of crisis, liquidity is

more important because inventory holding and search costs are higher, and asymmetric information becomes more relevant.

◮ Archarya et al. (2009) point out that banks face more stringent

capital requirements when holding illiquid assets and access to liquidity is difficult.

◮ Sadka (2010) finds that during crises investors may have shorter

horizons, e.g. to meet VaR requirements and margin calls.

◮ Bao et al. (2011) and Dick-Nielsen et al. (2010) also show that

liquidity effects are more important during the sub-prime crisis.

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 9/28

The three hypotheses that we test Hypothesis 3

Liquidity effects are more important for bonds with high credit risk.

◮ Based on a regime switching model Archarya et al. (2009) show

that liquidity is substantially different between investment and speculative grade bonds.

◮ Chen et al. (2007) find evidence that in periods of crisis,

flight-to-quality effects are expected which result in lower price reactions for investment grade bonds.

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 10/28

Data sources Four different data sources

◮ Transaction data from TRACE ◮ Consensus market valuations from Markit ◮ Credit ratings from Standard & Poor’s ◮ Bond characteristics, swap and Treasury data from Bloomberg

Merged data sample

◮ Period from Oct 1, 2004 to Dec 31, 2008 ◮ 3,261 firms ◮ 23,703 bonds ◮ 691,016 bond-weeks ◮ 23.5 mln trades

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 11/28

Set of proxies to capture liquidity Bond characteristics

◮ Amount issued ↑ ◮ Coupon ↓ ◮ Age ↓ ◮ Maturity ↓

Trading activity variables

◮ Number of trades ↑ ◮ Trade volume ↑ ◮ Trading interval ↓

Liquidity measures

◮ Amihud measure ↓ ◮ Price dispersion measure ↓ ◮ Roll measure ↓ ◮ Zero-return measure ↓

↑↓ . . . expected effect on liquidity

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 12/28

Liquidity measures based on transaction data

• ●
• ●
• ●
• ●
• ●
• 09:00

11:00 13:00 15:00 17:00 94 96 98 100 Washington Mutual Inc − CUSIP 939322AE3 (Jan 15, 2008) Trade Time Price

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 13/28

Liquidity measures based on transaction data

• 09:00

11:00 13:00 15:00 17:00 94 96 98 100 Omnicom Group − CUSIP 681919AT3 (Jan 15, 2008) Trade Time Price

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 14/28

Liquidity measures based on transaction data Amihud measure

Amihudt = 1 Nt

Nt

• j=1

|rj| vj ,

◮ rj . . . return based on traded prices ◮ vj . . . traded volume ◮ Nt . . . number of observations

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 15/28

Liquidity measures based on transaction data Roll measure

Rollt = 2

• − Cov(∆pj, ∆pj−1).

Price dispersion measure

Price dispersiont =

• 1

Nt

j=1 vj Nt

• j=1

(pj − mt)2vj,

◮ pj . . . traded price ◮ vj . . . traded volume ◮ mt . . . market-wide valuation ◮ Nt . . . number of observations

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 16/28

Descriptive statistics

Q0.05 Q0.50 Q0.95 Mean SD Yield Spread (%) 0.52 1.92 7.67 2.87 2.95 Rating 1.30 7.03 15.46 8.00 4.14 Bond Amount Issued (bln) 0.00 0.20 1.25 0.32 0.50 Characteristics Coupon (%) 3.03 5.97 9.13 5.98 1.87 Maturity (yr) 0.45 5.20 24.87 7.62 7.63 Age (yr) 0.47 2.77 10.36 3.80 3.61 Trading Activity Volume (mln) 0.02 0.39 5.23 1.35 2.53 Variables Trades 1.45 2.46 8.44 3.47 4.50 Trading Interval (dy) 1.50 4.48 7.80 4.46 2.18 Liquidity Amihud (bp per mln) 0.68 38.33 260.68 78.38 137.21 Measures Price Dispersion (bp) 1.69 33.64 106.84 41.53 35.56 Roll (bp) 24.48 155.98 420.86 185.12 144.69 Zero-Return (%) 0.00 0.01 0.16 0.03 0.08

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 17/28

Liquidity effects in corporate bond yield spreads

Dependent variable: ∆Yield Spread; panel regression (1) (2) (3) (4) Eco.Sig. (bp) * Intercept 0.073∗∗∗ 0.073∗∗∗ 0.072∗∗∗ 0.072∗∗∗ Lagged ∆Yld.Spr. −0.285∗∗∗ −0.283∗∗∗ −0.282∗∗∗ −0.280∗∗∗ ∆Volume −0.020∗∗∗ −0.011∗∗∗ 1.8 ∆Trades 0.007∗∗∗ 0.005∗∗∗ 1.5 ∆Trading Interval 0.007∗∗∗ 0.007∗∗∗ 2.5 ∆Amihud 0.050∗∗∗ 0.048∗∗∗ 6.1 ∆Price Dispersion 0.074∗∗∗ 0.070∗∗∗ 3.4 ∆Roll 0.051∗∗∗ 0.051∗∗∗ 3.1 ∆Zero-Return −0.077∗∗∗ −0.070∗∗∗ 0.8 ∆Rating Dummies Yes Yes Yes Yes R2 0.074 0.077 0.084 0.086 Observations 691,016 691,016 691,016 691,016 *SD of yield spread changes is 75.6 bp

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 18/28

Liquidity effects in corporate bond yield spreads

◮ Among all liquidity proxies, the Amihud and the price dispersion

measure are most important in terms of their t-statistics and economic significance.

◮ Among the trading activity variables, the volume and trading

interval are of particular importance.

◮ Liquidity measures are more relevant than trading activity variables

in terms of relative improvement in R2.

◮ A one SD move of all proxies in the direction of greater illiquidity

increases the yield spread by 19.2 bp (SD of spread change is 75.6 bp.)

◮ Liquidity effects explain about 14% of the explained market-wide

corporate yield spread variation.

◮ Hence, liquidity is an important price factor driving yield spread

changes.

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 19/28

Liquidity effects in periods of financial distress

Descriptive statistics GM/Ford Normal Sub-prime Yield Spread (%) 2.34 1.88 5.00 Rating 8.82 8.38 7.63 Traded Bonds (thd) 5.23 5.92 5.19 Market-Wide Trades (thd) 20.43 20.71 22.77 Market-Wide Volume (bln) 7.65 8.06 6.99 Amount Issued (bln) 0.43 0.45 0.54 Coupon (%) 6.26 6.24 6.23 Maturity (yr) 7.57 7.75 8.31 Age (yr) 3.91 4.36 4.76 Volume (mln) 1.51 1.44 1.53 Trades 4.48 4.06 5.33 Trading Interval (dy) 3.31 3.38 3.37 Amihud (bp per mln) 66.48 53.21 89.20 Price Dispersion (bp) 46.36 39.75 70.02 Roll (bp) 164.28 142.82 209.77 Zero Return (%) 0.02 0.02 0.03

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 20/28

Liquidity effects in periods of financial distress

Dependent variable: ∆Yield Spread; panel regression ... Liquidity Proxies × Liquidity Proxies × Liquidity Proxies GM/Ford Dummy Sub-prime Dummy ∆Volume −0.014∗∗∗ −0.003∗∗ 0.014∗∗∗ ∆Trades 0.003∗∗∗ 0.002∗∗∗ 0.004∗∗∗ ∆Trading Interval 0.003∗∗∗ −0.004∗∗∗ −0.007∗∗∗ ∆Amihud 0.033∗∗∗ 0.007∗∗∗ 0.027∗∗∗ ∆Price Dispersion 0.042∗∗∗ 0.005 0.053∗∗∗ ∆Roll Measure 0.008∗∗∗ −0.003 0.078∗∗∗ ∆Zero-Return Markit −0.050∗∗∗ 0.024 −0.082∗∗∗ ∆Rating Dummies Yes Observations 691,016 R2 0.101

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 21/28

Liquidity effects in periods of financial distress

Dependent variable: Yield Spread; Fama-MacBeth regression GM/Ford Crisis Normal Period Sub-prime Crisis Intercept 1.848∗∗∗ 1.444∗∗∗ 4.441∗∗∗ Amount Issued −0.254∗∗∗ −0.182∗∗∗ −0.325∗∗∗ Coupon 0.157∗∗∗ 0.114∗∗∗ 0.351∗∗ Maturity 0.011∗∗∗ 0.018∗∗∗ −0.060∗∗∗ Age 0.005∗∗ −0.004 −0.043∗∗∗ Volume 0.001 −0.011∗∗ 0.043∗∗ Trades 0.045∗∗∗ 0.032∗∗∗ 0.034∗ Trading Interval 0.007∗∗∗ 0.003∗∗ 0.006 Amihud 0.086∗∗∗ 0.072∗∗∗ 0.170∗∗∗ Price Dispersion 0.350∗∗∗ 0.274∗∗∗ 0.452∗∗∗ Roll 0.072∗∗∗ 0.081∗∗∗ 0.113∗ Zero-Return 0.237∗∗∗ 0.039 0.612 Rating Dummies Yes Yes Yes R2 0.591 0.602 0.497 Observations 3,815 3,845 3,187

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 22/28

Liquidity effects in periods of financial distress

◮ Trading during periods of crisis was focused on fewer bonds, with a

larger number of smaller size trades.

◮ We observe a flight-to-quality during the sub-prime crisis, which we

do not for the GM/Ford crisis.

◮ Liquidity is far more important in times of crisis, particularly during

the sub-prime crisis.

◮ The economic significance of the liquidity measures more than

doubles during the sub-prime crisis.

◮ Among the liquidity measures, the Amihud and price dispersion

measure are the most promising proxies.

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 23/28

Interaction effects between liquidity and credit ratings

2 4 6 8 10 Spread in % Jan 2005 Jan 2006 Jan 2007 Jan 2008 Investment Grade Speculative Grade

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 24/28

Interaction effects between liquidity and credit ratings

Dependent variable: ∆Yield Spread; panel regression Intercept 0.076∗∗∗ Lagged ∆Yld.Spr. −0.303∗∗∗ Lagged ∆Yld.Spr.×Spec. Grade Dummy 0.038∗∗∗ Liquidity Proxies × Liquidity Proxies

• Spec. Grade Dummy

∆Volume −0.004∗∗∗ −0.012∗∗∗ ∆Trades 0.006∗∗∗ 0.002 ∆Trading Interval 0.007∗∗∗ −0.010∗∗∗ ∆Amihud 0.046∗∗∗ 0.025∗∗∗ ∆Price Dispersion 0.080∗∗∗ 0.032∗∗∗ ∆Roll 0.060∗∗∗ −0.001 ∆Zero-Return −0.083∗∗∗ −0.009 ∆Spec. Grade Dummy 4.881∗∗∗ ∆Rating Dummies Yes R2 0.095 Observations 637,814

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 25/28

Interaction effects between liquidity and credit ratings

◮ In general, trading is focused on the investment grade segment. ◮ Higher trading activity in the GM/Ford crisis for speculative grade

bonds → shuffling of bonds due to clientele preferences.

◮ Lower number of trades and bonds are observed in the speculative

grade segment in the sub-prime crisis → flight-to-quality.

◮ The regression analysis shows that bonds with higher credit risk are

less liquid and react more strongly to liquidity changes.

◮ A one SD move in the direction of greater illiquidity increases the

yield spread by 13.8 bp for investment grade bonds vs. 37.6 bp for speculative grade bonds.

◮ We find a particularly strong reaction of speculative bonds in the

sub-prime crisis.

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 26/28

Conclusion

◮ Liquidity is an important risk factor for corporate bond pricing. ◮ Liquidity effects explain about 14% of the explained market-wide

corporate yield spread variation.

◮ During periods of crisis the economic impact of the liquidity

measures increases significantly (more than doubles in the sub-prime crisis.)

◮ More pronounced liquidity effects are seen in the speculative grade

segment, particularly in the sub-prime crisis.

◮ Results are relevant for pricing, risk management, and regulatory

policy.

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 27/28

Important events in the US corporate bond market

Mar 16: GM issues profit warning May 5: Downgrade of GM to BB and Ford to BB+ Jul 05 Oct 8: Delphi defaults Apr 05 Oct 05 Jan 06

GM/Ford Crisis

Jan 23: Ford announces 30,000 layoffs

Normal Period

Mar 05 Feb 06 Jul 07 Jul 07 Oct 08

Sub-Prime Crisis

Jul 17: At leat 90% loss of two Bear Stearns hedge funds specialized in sub-prime debt Aug 7: American Home Mortgage defaults Sep 15: Lehman Brothers defaults Sep 25: Washington Mutual defaults Jan 09 Jan 09 Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011

Motivation Literature Hypothesis Data Proxies Results Conclusion Appendix 28/28

Time-series and cross-sectional regression models Time-series regression model (Panel)

∆(Yield Spread)i,t = α0 + α1 · ∆(Yield Spread)i,t−1 + β · ∆(Rating Dummies)i,t + γ · ∆(Trading Activity Variables)i,t + λ · ∆(Liquidity Measures)i,t + ǫi,t

Cross-sectional regression model (Fama-MacBeth)

Yield Spreadi = α0 + α1 · Rating Dummiesi + β · Bond Characteristicsi + γ · Trading Activity Variablesi + λ · Liquidity Measuresi + ǫi

Marti Subrahmanyam Illiquidity or Credit Deterioration Tuesday, June 28th 2011