Dynamic Effects of Idiosyncratic Volatility and Liquidity in Corporate

Bond Markets

Madhu Kal imipa l l iSubhankar Nayak

M. Fabricio PerezWilfrid Laurier University

Waterloo, Canada

CIGI, Oct 3, 2011

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AgendaMotivation

and Introductio

n

Literature and Data

Results

Summary

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Figure 1 Monthly Industrial portfolio indices based on ratings (1994-2007)

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Figure 2 Monthly Industrial portfolio indices based on maturity (1994-2007)

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Figure 3Monthly Industrial bond spreads, and aggregate bond and stock market variables (1994-2007)

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Research Question Does idiosyncratic risk subsume information in

liquidity in explaining the time-series of corporate bond prices?

We examine this question by studying the relative impact of idiosyncratic volatility and

liquidity on corporate bond yield spreads over time, and

empirically disentangling both effects.

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Objective To identify principal drivers between volatility and

liquidity in determining corporate bond spreads.

Shed more light on the interaction and potential substitution effects between volatility and liquidity on bond pricing over time.

Why is this important? -IFinancial crisis 2007-9 Three phases of the credit crisis:

2006- Jul 2007 Delinquencies in MBS and related instruments

Aug 2007–Aug 2008 Marked by liquidity crisis and lending between banks

ceased Sept 2008-2009

Induced by Lehman crisis demonstrating an inconsistent and haphazard application of government intervention polices,

And evidence of shoddy underwriting and lack of due diligence that further hampers the working of the securitization market

Why is this important? –I contd.Credit and liquidity spreads: Subprime crisis (Brunnermeier, 2008)

ABCP crisis,Sachsen, Northern Rock

Fitch Bear Stearns

Funding costs

Credit spreads

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Why is this important?-II Volatility and illiquidity are both critical

An increase in idiosyncratic equity volatility increases the ex-ante probability of firm default (or “distance-to-

default” as in the KMV-Moody’s model), thereby depressing corporate bond prices and inflating bond spreads (Merton, 1974) - Campbell and Taksler

(2003)

While the corporate debt constitutes a significant proportion of capital structure of firms (over 80%), the underlying market remains highly illiquid Ignoring non-default sources of risk such as illiquidity can lead to

structural models overpricing bonds, and resulting in the so-called “credit puzzle” (Covitz and Downing, 2007; Driessen, 2005 )

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?Idiosyncraticvolatility

Bond marketLiquidity

Bond spreads

Why is this important?-II contd.

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Why is this important?–II contd. While higher idiosyncratic equity volatility can

imply higher ex-ante bond spreads, it is not obvious from the Campbell and Taksler (2003) results whether higher spreads are attributable to higher equity volatility, lower bond liquidity, or both.

Time-series: Negative firm-specific news events leading to

high underlying equity volatility can result in higher bond spreads as well as lower bond liquidity over time.

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Why is this important? -III Debt issuers need to evaluate the

cumulative merit of volatility as well as liquidity effects while pricing and timing their bond issues.

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Why is this important? -IV Besides, Campbell and Taksler (2003) employ

an aggregate liquidity measure as opposed to individual bond liquidity measures. aggregate debt turnover (defined as daily average of the total

volume of dealer transactions in U.S. Government securities relative to marketable debt)

aggregate bond spreads

The Campbell and Taksler study is limited to the 1995-99 period, when the market experienced very high growth induced by the high-tech bubble.

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Questions we ask 1. Can idiosyncratic volatility and bond liquidity explain the

credit spreads? Collin-Dufresene et al. (JOF, 2001) framework

2. Are volatility and liqudity effects on bond spreads conditional on the underlying regimes?

3. What are the dynamic effects of Idiosyncratic volatility and liquidity on corporate bond spreads? Granger Casualty and VAR tests/impulse reaction functions

4. What are the information shares of Idiosyncratic volatility and liquidity in explaining corporate bond spreads? Variance decomposition tests

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Contributions Our work is unique in that it focuses on a

large sample of corporate bonds over an extended period, using an exhaustive list of volatility and liquidity variables, and provides a comprehensive study of the volatility and liquidity effects over time.

Exhaustive data: We employ about 196,000 secondary trades of option-free corporate

bonds issued by 818 firms over the 14-year period, 1994- 2007 In addition, we employ a “ground-up” approach in building portfolio

indices

In essence, the critical difference between earlier papers on volatility and liquidity and our work is that prior papers exclusively focus on either of these variables, whereas we emphasize joint focus on both variables in bond pricing.

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AgendaMotivation

and Introductio

n

Literature and Data

Results

Summary

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Related Literature Equity volatility studies include

Campbell and Taksler (2003) Cremers et al. (2008a, b), Alexander and Kaeck (2008) and Zhang et al. (2009).

Corporate bond liquidity studies include Chen et al. (2007) and Houweling et al. (2005) and Mahanti et

al. (2008) Work on disentangling credit and liquidity risks from yield spreads

For e.g., Longstaff et al., (2005); Driessen, (2005); Covitz and Downing, (2007); Beber et al., (2009); Schwartz, (2010), Kalimipalli and Nayak (2011)

Modeling bond spreads For e.g. Collin-Dufresne et al. (2001), Avramov et al (2007), Davies (2008)), Van

Landschoot (2008) etc Literature on information spillovers between (a) determination of

bond spreads and (b) stocks and corporate bonds

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Data NAIC corp. bond data:

1994-2007:14 year period spanning 168 months Trades of insurance companies

FISD data CRSP and COMPUSTAT DATASTREAM (swap rates)

The final data file consists of NAIC straight bond trades linked to respective FISD based issue-specific bond variables and CRSP stock identifier variables for all firms that have public

equity outstanding

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Table 1 Summary statistics of underlying bonds used in the portfolio construction (1994-2007)

FIN IND UTL AA A BBB ≤BB TotalBy MaturityLong-Term 5,028 22,518 1,732 2,907 14,043 9,900 2,428 29,278> 15 years 0.97 1.37 1.39 0.58 0.8 1.55 4.11 1.3

Medium-Term 23,295 29,743 5,342 7,153 30,139 16,160 4,928 58,3807-15 years 0.57 0.93 0.74 0.24 0.46 0.88 3.04 0.77

Short-Term 44,636 52,621 10,953 11,987 55,796 30,655 9,772 108,2101-7 years 0.61 1.39 1.45 0.25 0.47 1.17 5.22 1.08

By Time Period

1994-1999 36,827 54,143 10,774 12,696 51,416 31,290 6,342 101,7440.47 0.62 0.46 0.2 0.37 0.7 1.99 0.55

2000-2004 30,315 43,227 6,413 7,813 41,384 22,064 8,694 79,9550.85 2.05 2.54 0.43 0.73 1.84 6.49 1.63

2004-2007 5,817 7,512 840 1,538 7,178 3,361 2,092 14,1690.41 1.28 1.2 0.32 0.36 0.91 3.3 0.92

Total 72,959 104,882 18,027 22,047 99,978 56,715 17,128 195,8680.62 1.26 1.23 0.29 0.52 1.15 4.44 1.02

By Industry By Rating

Source: NAIC

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Form portfolios eight bond portfolios:

two industries (Financials and non-Financials), two ratings (high and low), and two maturity (high and low) categories.

equally weighted Bond Spreads Underlying portfolio idiosyncratic volatility

Residuals from FF 3 (4) factor models Underlying portfolio liquidity:

Liquidity factor Details next slide

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Table 2. Bond Liquidity measures

Trade variables1. Trade size 2. Annual trading frequency

Price Impact variables3. Bond liquidity index 1:4. Bond liquidity index 2:

We employ a factor approach (Bai and Ng, 2002) to combine the four bond liquidity series for each portfolio.

volumetotal10 prices8

8 maximum price minimum price10 total volumeaverage price

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Table 3: Summary statistics

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AgendaMotivation

and Introductio

n

Literature and Data

Results

Summary

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Table 5: Monthly Collin-dufresne et al. bond-spread regressions(1994-2007)

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Table 5: contd.

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Sigma-shock Analysis

Both liquidity and volatility shocks however have negligible impact on bond spreads (1 and 2 bps respectively) for high-rated bonds.

Overall we find that volatility has a first-order impact relative to liquidity on bond

spreads, especially for low-rated bonds, and the bond illiquidity significantly matters for pricing low-rated

and short-term bonds.

Aggregate Short -term

Low-rated

1σ shock of volatility

16 bps 18 bps 23 bps

1σ liquidity shock

6 bps 11 bps 9 bps

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Table 6:Additional variables

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Collin-Dufresne et al.(2001)puzzle Does idiosyncratic volatility and illiquidity

explain the systematic variation in bond spread residuals?

Principal component tests show: that the idiosyncratic volatility and illiquidity

variables mainly capture idiosyncratic or portfolio specific information, and

not the systematic variation that can address the Collin-Dufresne et al. (2001) puzzle

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Figure 4Underlying regimes in monthly Industrial bond spreads idiosyncratic volatility and bond liquidity (1994-2007)

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Table 7Monthly time-series regression tests with regime breaks for volatility and liquidity (1994-2007)

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Table 8Granger casualty tests for monthly bond spreads, volatility and liquidity

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VAR system

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Figure 5Generalized impulse response functions for monthly credit spread, volatility and liquidity portfolios

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Table 9Cholesky variance decomposition of volatility and liquidity effects on bond spreads

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Table 9: contd.

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AgendaMotivation

and Introductio

n

Literature and Data

Results

Summary

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Summary of Results: (1) First, both idiosyncratic volatility and liquidity

effects jointly and significantly matter only for the distress portfolios i.e. low rated and short-term

bonds; for other portfolios volatility subsumes liquidity.

The effect of 1σ shock of volatility (bond illiquidity) on aggregate bond spreads is 16 bps, (6 bps), and increases to 23 bps (9 bps) for low-rated bonds.

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Summary of Results: (2 & 3) Second, there is a differential impact of volatility and

liquidity on bond spreads, conditional on the underlying regime. For example, the bond illiquidity effects mainly come from

high-illiquidity regimes, while volatility effects are significant even for low-volatility

regimes.

Third, Granger-causality tests indicate a strong evidence for volatility and illiquidity influencing bond spreads for all distress portfolios. Bond Illiquidity seems to exogenously determined, whereas

spreads and volatility are mainly endogenously driven.

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Summary of Results: (4) Fourth, impulse response analysis shows that

there is a differential impact between how volatility and liquidity impact bond spreads.

While the liquidity shocks are quickly absorbed into bonds prices, volatility shocks are more persistent and have a long-term effect.

Liquidity shocks can have significant trivial long-term effects for distress portfolios.

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Summary of Results: (5) Finally, variance decomposition tests show

that liquidity can explain a significant residual

variance for the short-term portfolios, and that effect is concentrated in the high-spread,

high-volatility or high-illiquidity regime.

Volatility, however, has the dominant explanatory power for all the portfolios in both regimes, and under different orderings

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Collectively Our results imply that

the idiosyncratic volatility effect does not subsume the liquidity effect in explaining bond prices for distress portfolios,

unlike the findings in equity markets (Spiegel and Wang, 2005).

Our results, therefore, suggest that both volatility and liquidity effects are important for

bond pricing, and can have differential short- and long- run impact on

bond spreads conditioned on the underlying portfolios and regimes

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Practical implications Our findings can guide

fixed-income desks in building improved pricing models based on differential impact of volatility and liquidity;

bond traders better formulate their hedging and market timing strategies,

debt issuers better time their debt issuance in order to minimize the cost of borrowing, and

policy makers better address the impact of volatility and liquidity shocks on credit markets.