Liquidity Risk In Corporate
FIXED INCOME
Bond Markets
George Chacko
Harvard Business School & IFL
1
Roadmap
„
Introduction
„
Liquidity Risk Research
„
„
„
„
„
Motivation
Liquidity Measurement
Liquidity Factor Construction
Empirical Results for Liquidity Risk
Practical Implications of Liquidity Risk
2
Capital Structure Arbitrage
3
Spread over benchmark Treasury Strip (%)
Capital Structure Arbitrage
Worldcom 6.95 30Y
Issuance Date: Aug-1998 Amount: $1.75 BB Callable
16
Forecast Spread
Caa
Actual Traded Spread
Ba2
14
12
10
Baa2
8
6
4
2
0
Apr-02
Jan-02
Oct-01
Jul-01
Apr-01
Jan-01
Oct-00
Jul-00
4
Corp Bond Market Liquidity
Issue Trading Frequency Median bond trades less than once a quarter
16000
100.00%
100.00%
80.00%
Number of Issues
(Total: 24170)
12000
70.00%
10000
60.00%
50.00%
8000
39.23%
40.00%
6000
30.00%
24.33%
4000
20.00%
13.40%
2000
3.58%
Cumulative Percent Issues
90.00%
14000
10.00%
0
0.00%
1 Trade/Week
1 Trade/M
1 Trade/Qtr
> 1 Trade/Qtr
No Trades
Trading Frequency
5
Source: State Street Global Markets
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
100.31
99.92
100.67
111.99
224.98
249.93
152.53
199.98
271.99
199.98
100.28
MAX
6
Source: State Street Global Markets
Source: State Street Global Markets
9/29/2004
9/22/2004
9/15/2004
9/8/2004
9/1/2004
8/25/2004
8/18/2004
8/11/2004
8/4/2004
7/28/2004
7/21/2004
7/14/2004
7/7/2004
6/30/2004
6/23/2004
6/16/2004
6/9/2004
113
6/2/2004
115
5/26/2004
5/19/2004
5/12/2004
5/5/2004
4/28/2004
4/21/2004
4/14/2004
TRACE Comparison
CUSIP 172967BC4 (CITIGROUP), 4/14/2004 -- 10/4/2002
TRACE High (via Bloomberg)
TRACE Low (via Bloomberg)
TRACE 1MM+ High
TRACE 1MM+ Low
111
109
107
105
103
101
99
7
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.
8
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
9
Latent Liquidity Properties
Higher Liquidity
Lower Liquidity
10
Latent Liquidity Properties
Higher Liquidity
Lower Liquidity
11
Latent Liquidity Properties
Higher Liquidity
Lower Liquidity
12
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
13
Liquidity Risk Factor
Time Series
140
L iq u id ity In d ex
130
120
110
100
90
80
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
14
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.
15
Empirical Results
Liquidity Risk Alpha
Alphas of Portfolios Sorted on Liquidity Betas
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%
16
Empirical Results
Contribution of Liquidity: 1
Incremental R2 of Liquidity Factor
Liquidity Portfolios
Credit
H
Portfolios M
L
H
5%
5%
4%
H/M
12%
13%
13%
M
18%
21%
22%
M/L
23%
25%
26%
L
30%
32%
34%
17
Empirical Results
Contribution of Liquidity: 2
Incremental R2 of Liquidity Factor
Liquidity Portfolios
Duration L
Portfolios M
H
H
4%
3%
6%
H/M
14%
16%
17%
M
21%
20%
23%
M/L
27%
28%
30%
L
36%
37%
39%
18
Practical Implications
Convertible Arbitrage
Benchmark Regressions
Alpha
DEF
TERM
0.0029
-0.66
1.39
0.0011
0.59
Rm-Rf
SMB
HML
UMD
Liq.
Adj.R2
-0.33
0.27
0.3859
-1.43
-0.02
-1.21
0.09
-0.19
0.07
0.08
-0.02
3.65
0.24
0.4897
-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
0.67
-2.58
1.82
1.54
0.24
3.47
0.0004
-0.66
-0.33
0.58
0.0026
-1.43
-0.02
-1.21
0.08
-0.15
0.07
0.08
-0.03
3.51
-0.15
1.08
-2.74
2.44
1.26
-0.09
0.0035
-0.17
0.06
0.09
0.01
3.32
-2.07
1.8
1.51
0.25
0.4565
0.055
0.1598
0.1566
19
Practical Implications
Treasury Yield Curve
Average Contribution of Factors to Bond Yields (RMSE)
Maturity
0.5
1
2
3
5
7
10
Curvature
2
3
7
13
29
38
21
Term
3
7
9
16
37
46
64
Liquidity
5
10
16
27
56
73
97
20
Practical Implications
Spread over benchmark Treasury Strip (%)
Back to WorldCom
Worldcom 6.95 30Y
Issuance Date: Aug-1998 Amount: $1.75 BB Callable
16
Forecast Spread
Caa
Actual Traded Spread
Ba2
14
12
10
Baa2
8
6
4
2
0
Apr-02
Jan-02
Oct-01
Jul-01
Apr-01
Jan-01
Oct-00
Jul-00
21
Practical Implications
Credit vs. Liquidity Spread
1/1/01 -1/1/02: Change in credit spread is minimal
22
Practical Implications
Credit vs. Liquidity Spread
Baa Index
Ba Index
23
Source: State Street Global Markets
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)
24
Practical Implications
Optimizing on Liquidity Risk
Sub-Optimal Sharpe: 1.05
Sharpe 1: 1.69 Sharpe 2: 1.96
25
Source: State Street Global Markets
Practical Implications
Optimizing on Credit Risk
Sub-Optimal Sharpe: 0.19
Sharpe 1: 0.72 Sharpe 2: 0.84
26
Source: State Street Global Markets