Liquidity Risk In Corporate FIXED INCOME Bond Markets George - - PowerPoint PPT Presentation

FIXED INCOME

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George Chacko Harvard Business School & IFL

Liquidity Risk In Corporate Bond Markets

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Roadmap

Introduction Liquidity Risk Research

Motivation Liquidity Measurement Liquidity Factor Construction Empirical Results for Liquidity Risk Practical Implications of Liquidity Risk

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Capital Structure Arbitrage

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Worldcom 6.95 30Y Issuance Date: Aug-1998 Amount: $1.75 BB Callable

2 4 6 8 10 12 14 16

Jul-00 Oct-00 Jan-01 Apr-01 Jul-01 Oct-01 Jan-02 Apr-02

Spread over benchmark Treasury Strip (%)

Forecast Spread Actual Traded Spread

Baa2 Ba2 Caa

Capital Structure Arbitrage

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Corp Bond Market Liquidity

Issue Trading Frequency - Median bond trades less than once a quarter

100.00% 3.58% 13.40% 39.23% 24.33%

2000 4000 6000 8000 10000 12000 14000 16000 1 Trade/Week 1 Trade/M 1 Trade/Qtr > 1 Trade/Qtr No Trades Trading Frequency

Number of Issues (Total: 24170)

0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00% 100.00%

Cumulative Percent Issues

Source: State Street Global Markets

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Liquidity Trend in Bond Mkt

Average Trade Size Percentiles (millions of US dollars): YR94 YR95 YR96 YR97 YR98 YR99 YR00 YR01 YR02 YR03 YR04 MIN 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 10% 0.36 0.44 0.43 0.48 0.50 0.43 0.40 0.42 0.37 0.35 0.28 20% 0.75 0.83 0.84 0.94 0.97 0.82 0.72 0.73 0.67 0.66 0.55 30% 1.06 1.11 1.18 1.23 1.32 1.12 1.01 1.03 0.94 0.91 0.78 40% 1.43 1.50 1.63 1.68 1.78 1.54 1.38 1.43 1.22 1.16 1.03 50% 1.84 2.02 2.09 2.16 2.34 2.08 1.93 1.98 1.66 1.52 1.30 60% 2.30 2.63 2.71 2.85 3.10 2.88 2.56 2.65 2.21 1.97 1.65 70% 3.02 3.59 3.61 3.72 4.15 3.89 3.45 3.59 2.99 2.50 2.17 80% 4.10 4.99 4.97 5.06 5.56 5.31 5.02 5.12 4.30 3.46 2.88 90% 6.20 7.22 7.33 8.00 9.16 8.93 8.23 8.42 7.06 5.75 4.55 MAX 100.31 99.92 100.67 111.99 224.98 249.93 152.53 199.98 271.99 199.98 100.28

Source: State Street Global Markets

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TRACE Comparison

CUSIP 172967BC4 (CITIGROUP), 4/14/2004 -- 10/4/2002

99 101 103 105 107 109 111 113 115 4/14/2004 4/21/2004 4/28/2004 5/5/2004 5/12/2004 5/19/2004 5/26/2004 6/2/2004 6/9/2004 6/16/2004 6/23/2004 6/30/2004 7/7/2004 7/14/2004 7/21/2004 7/28/2004 8/4/2004 8/11/2004 8/18/2004 8/25/2004 9/1/2004 9/8/2004 9/15/2004 9/22/2004 9/29/2004

TRACE High (via Bloomberg) TRACE Low (via Bloomberg) TRACE 1MM+ High TRACE 1MM+ Low

Source: State Street Global Markets

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Limitations of Liquidity Measures

Conventional Measures of Liquidity:

Trading Volume Bid-Ask Spread

However, if securities are extremely illiquid,

conventional measures don’t work well

Rather than looking at actual trading, one solution is

to look at a security’s propensity to trade.

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Latent Liquidity

Latent liquidity: a quantitative measure of propensity to

trade for individual securities

Rationale:

For a bond dealer, it is easier to access a bond issue

if it is held in high-turnover portfolios

If a bond issue is held by high-turnover funds, it is

likely that security has a higher propensity to trade.

So, a security’s propensity to trade can be

constructed by looking at the aggregate trading characteristics of owners of that security

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Latent Liquidity Properties

Higher Liquidity Lower Liquidity

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Latent Liquidity Properties

Higher Liquidity Lower Liquidity

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Latent Liquidity Properties

Higher Liquidity Lower Liquidity

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Liquidity Risk Factor Construction

We sort the US corp bond universe into 3x3x3 = 27

buckets

Duration Credit Risk Latent Liquidity

We then form three portfolios:

HML Duration LMH Credit Risk LMH Latent Liquidity

These portfolios represent interest rate, credit, and

liquidity risk factors

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Liquidity Risk Factor Time Series

80 90 100 110 120 130 140 11/27/1993 4/11/1995 8/23/1996 1/5/1998 5/20/1999 10/1/2000 2/13/2002 6/28/2003

Date Liquidity Index

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Factor Regressions

With these factors, we can now do factor regressions

to compute individual security betas.

We first compute credit, duration, and liquidity betas

for the US corp bond universe.

We then do a 5x3x3 sort of these securities based on

these betas – 5 liquidity portfolios, 3 credit portfolios, and 3 duration portfolios

Using these 45 portfolios, we then conduct a series of

tests to check the importance of the liquidity risk factor.

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Empirical Results

Liquidity Risk Alpha

L M/L M H/M H H - L CAPM

• 0.54%

0.71% 1.25% 1.94% 2.36% 2.90% Duration

• 0.36%

0.69% 1.31% 2.13% 2.78% 3.14% Duration, Credit

• 0.56%

0.63% 1.09% 1.68% 2.15% 2.71%

Alphas of Portfolios Sorted on Liquidity Betas

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Empirical Results

Contribution of Liquidity: 1 Incremental R2 of Liquidity Factor

Liquidity Portfolios H H/M M M/L L Credit H 5% 12% 18% 23% 30% Portfolios M 5% 13% 21% 25% 32% L 4% 13% 22% 26% 34%

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Empirical Results

Contribution of Liquidity: 2 Incremental R2 of Liquidity Factor

Liquidity Portfolios H H/M M M/L L Duration L 4% 14% 21% 27% 36% Portfolios M 3% 16% 20% 28% 37% H 6% 17% 23% 30% 39%

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Practical Implications

Convertible Arbitrage

Alpha DEF TERM Rm-Rf SMB HML UMD Liq. Adj.R2 0.0029

• 0.66
• 0.33

0.27 0.3859 1.39

• 1.43
• 1.21

3.65 0.0011

• 0.02

0.09

• 0.19

0.07 0.08

• 0.02

0.24 0.4897 0.59

• 0.13

1.1

• 2.45

2.45 1.28

• 0.09

2.93 0.0012

• 0.19

0.06 0.1 0.01 0.26 0.4565 0.67

• 2.58

1.82 1.54 0.24 3.47 0.0004

• 0.66
• 0.33

0.055 0.58

• 1.43
• 1.21

0.0026

• 0.02

0.08

• 0.15

0.07 0.08

• 0.03

0.1598 3.51

• 0.15

1.08

• 2.74

2.44 1.26

• 0.09

0.0035

• 0.17

0.06 0.09 0.01 0.1566 3.32

• 2.07

1.8 1.51 0.25

Benchmark Regressions

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Practical Implications

Treasury Yield Curve

Maturity Curvature Term Liquidity 0.5 2 3 5 1 3 7 10 2 7 9 16 3 13 16 27 5 29 37 56 7 38 46 73 10 21 64 97

Average Contribution of Factors to Bond Yields (RMSE)

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Practical Implications

Back to WorldCom

Worldcom 6.95 30Y Issuance Date: Aug-1998 Amount: $1.75 BB Callable

2 4 6 8 10 12 14 16

Jul-00 Oct-00 Jan-01 Apr-01 Jul-01 Oct-01 Jan-02 Apr-02

Spread over benchmark Treasury Strip (%)

Forecast Spread Actual Traded Spread

Baa2 Ba2 Caa

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Practical Implications

Credit vs. Liquidity Spread

1/1/01 -1/1/02: Change in credit spread is minimal

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Practical Implications

Credit vs. Liquidity Spread

Baa Index Ba Index

Source: State Street Global Markets

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Practical Implications

Liquidity-Driven Asset Allocation

Problem:

Allocate portfolio across a set of Moody’s Baa1 or

higher rated long duration securities.

Set: BLS, CAT, BA, CCE, IBM, D,ALL, WFC, PFE, SBC

Scenarios

Scenario 1 (Optimizing on Total Risk) Scenario 2 (Optimizing on Liquidity risk) Scenario 3 (Optimizing on Credit risk)

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Practical Implications

Optimizing on Liquidity Risk

Sub-Optimal Sharpe: 1.05 Sharpe 1: 1.69 Sharpe 2: 1.96

Source: State Street Global Markets

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Practical Implications

Optimizing on Credit Risk

Sub-Optimal Sharpe: 0.19 Sharpe 1: 0.72 Sharpe 2: 0.84

Source: State Street Global Markets