Detecting Regime Shifts in Corporate Credit Spreads - WebMeets.com

Detecting Regime Shifts in 

Corporate Credit Spreads 

Olfa Maalaoui Chun 

Georges Dionne 

Pascal François 

First draft: June 2008. This draft: January, 2010 

Abstract 

Switching regimes in credit spreads are thought to correlate with macro 

factors. However, the states identified using the information in the entire 

yield curve of credit spreads have difficulties in matching the economic cycle. 

This paper applies a regime detection technique – previously never 

been used in finance – distinguishing between level and volatility regimes 

in credit spreads. We show that patterns of both regimes are surprisingly 

different although most breakpoints occur around economic downturns. 

Specifically, volatility regimes appear to be contemporaneously related to 

the NBER economic cycle while level regimes are more linked to a monetary 

policy cycle and tightening credit conditions. Both the fed funds rate 

and the Chief Officer Loan Survey support the long swings observed in 

credit spread levels during economic recessions. Furthermore, our results 

suggest that credit spreads contain predictive information about economic 

downturns and act in response to tightening standards. These results are 

supported by the widely used databases on corporate bond data including 

Warga, NAIC, and TRACE and thus covering the last three economic recessions. 

Key Words: Credit spread regimes, level regimes, volatility regimes, 

tightening standards, monetary policy cyle, economic cycle. 

JEL Classiffication: C1, C32, C61, E32, G11, G33 

Olfa Maalaoui Chun is from KAIST Graduate School of Finance. Georges Dionne and Pascal 

François are from HEC Montreal. The authors acknowledge financial support from the Institut 

de Finance Mathématiques de Montréal (IFM2), the Tunisian Ministry of Education, the 

Canada Research Chair in Risk Management, the Center for Research on e-finance, HEC Montreal, 

Copenhagen Business School and KAIST Graduate School of Finance. They thank Albert 

Lee Chun, Jan Ericsson, Kwangoo Kang, Nicolas Papageorgiou, and seminar participants at 

2009 CREDIT, 20th (EC)2 Conference, 4th International Conference on Asia-Pacific Financial 

Markets and KAIST Graduate School of Finance for helpful comments. We also thank Jens 

Dick-Nielsen for helpful comments on TRACE data and for providing us with the filter to clean 

the data. Corresponding author: Olfa Maalaoui Chun, olfa.maalaoui@gmail.com.

1 Introduction 

Understanding the dynamics of credit spreads is essential when pricing and 

hedging corporate bonds as well as the new generation of credit instruments 

such as credit derivatives and structured products. An important issue in the 

literature is how to assess the systematic factor in the credit risk premium. According 

to Collin-Dufresne, Goldstein, and Martin (2001), most of the changes 

in credit spreads can be explained by a common systematic factor and liquidity 

and supply/demand pressures may be a possible explanation for this factor. 

Thus, considering the forces driving this systematic factor in the bond market 

is a key in solving the credit spread puzzle, i.e. the finding that credit spreads 

on corporate bonds over Treasury bonds are larger than what can be explained 

by default risk (Elton, Gruber, Agrawal, and Mann, 2001). 

Systematic credit risk factors are usually thought to correlate with macroeconomic 

conditions suggesting a close link between the time series of credit 

spreads and the economic cycle. However, there is no enough evidence in the literature 

supporting this close connection. For example, Collin-Dufresne, Goldstein, 

and Martin, (2001) show that business climate and macro variables cannot 

explain systematic factors in credit spreads and Koopman and Lucas (2005) 

find weak evidence on the link between the GDP and the credit cycle (see also 

Koopman et al. 2006). Recently, Lown and Morgan (2006) show that high levels 

of credit may signal an incipient slowdown in the economy, beyond that structural 

models predict that high leverage widen credit spreads. 1 They also show a 

strong link between leverage and tightening in standards. As the tightening in 

standards following a sufficiently high level of the short rate acts as a response 

to tightening monetary actions, we may reasonably think that monetary policy 

actions are somehow related to the credit cycle. Moreover, by acknowledging 

that monetary actions and the role of the economic activity should be related 

through a Taylor (1993) rule setting for example both mechanisms may have 

a direct or indirect impact on credit spreads. Therefore, by linking monetary 

policy cycles with regimes extracted from credit spread levels, we are able to 

demonstrate a link between Federal Reserve policy and credit spreads beyond 

that already documented in the literature linking credit spreads with the short 

rate. Then, by linking the economic cycle with regimes extracted from credit 

1 Standards refer to any non-price lending terms specified in the typical bank business loan or 

line of credit measured for example by the Senior Loan Officer Opinion Survey on Bank Lending 

Practices. 

1

spread volatilities, we are also able to reconcile the previous literature suggesting 

a close link between the credit cycle and the economic cycle. 

Our main contribution to the literature is to show that regimes in credit 

spreads are closely related to both the economic cycle and the monetary policy 

cycle when the level and volatility regimes are identified separately. The 

finance literature is marked by the absence of a methodology that allows for 

separately identifying level and volatility regimes in time series. We propose a 

regime shift detection technique that is widely used in physical and biological 

sciences literature but has heretofore never been applied in a finance or economic 

context. We test for the presence of breakpoints in the level and volatility 

of credit spreads using the three distinct databases - Warga, NAIC, and 

TRACE. The recent literature has employed at most only one of these databases 

in studying the properties of credit spreads, our contribution lies in employing 

all three databases at the same time, and constructing for a time-series 

of credit spreads covering the last three recessions. 

As discussed earlier, the existing literature fails to uncover a strong link 

between the credit cycle and the economic cycle. Several studies focusing on 

credit spread determinants apply switching regime models in indentifying the 

systematic credit spread factor and attempt to link this to inflationary and/or 

volatility factors (David, 2008; Davies, 2004 and 2007; Dionne et al., 2007). In 

most of these studies the identified regimes do not completely account for the 

high levels and long swings of credit spreads observed during recessions. As further 

documented in Alexander and Kaeck, (2007), it remains unclear how the 

identified regimes can be connected to the economic cycle. In addition, when 

applied to data on credit spreads, most switching regime models identify two 

distinct regime types - a high mean - high variance regime and a low mean - low 

variance regime. Recently, Chen (2007, 2009) finds evidence suggesting that equity 

returns also follow two regimes - a high mean - low variance regime and a 

low mean - high variance regime. This suggests that with some financial variables, 

episodes of high levels may not necessarily be accompanied by episodes 

of high volatility. In this paper we show that high credit spread regimes only 

partially coincide with high volatility regimes. Interestingly, we find that high 

levels of credit spreads are accompanied by high volatility only around NBER 

recession dates. Consistent with recent empirical studies (Garzarelli, 2009; 

Mueller, 2009), we document that credit spread levels increase sometime before 

the onset of an official NBER recession but persists until long after the 

recession officially ends. However, no previous study has provided a rational 

2

explanation for this persistence in credit spread cycles. Our research supports 

the existence of a connection between volatility regimes in credit spreads and 

the economic cycle while, at the same time, justifying the long-lasting and persistent 

pattern of high credit spread cycles. Specifically, by disentangling level 

regimes from volatility regimes of credit spreads we are able to show a contemporaneous 

connection between volatility regimes and the economic cycle. In 

addition, we find that the prolonged duration of high credit spread regimes is 

linked to the adverse credit conditions which are in turn linked to monetary 

policy actions. In particular, level regimes are strongly related to the index of 

tightening loan standards published quarterly by the Federal Reserve Senior 

Loan Officer Opinion Survey. 2 

Our approach to modeling regime shifts has been used to detect regime 

shifts in ecosystems (see Rodionov, 2004, 2005, and 2006 (for a complete review)). 

The technique has the advantage of letting the data speak and reveal 

possible shift points in real time. It signals statistically significant shifts in the 

level and the volatility of time series separately. Detected shifts are then submitted 

to a battery of structural statistical tests that confirm or reject the onset 

of a new regime. In contrast to existing studies on credit spreads with regime 

switching, the methodology used in this paper does not require any prior assumptions 

about the number of regimes. 

We apply this method to the time series of credit spreads obtained from the 

comprehensive NAIC database providing transaction prices on U.S. corporate 

bonds rated from AA to BB over the 1994-2004 period. Since this dataset only 

covers one economic recession, we check whether our results hold outside the 

period considered and repeat the analysis using two additional databases – 

Warga and TRACE. The Warga database provides only quoted prices on U.S. 

corporate bonds and covers the period from 1987 to 1996. The TRACE database 

is available since July 2002 and provides high frequency transaction prices on 

U.S. corporate bonds. 

Not all trades reported to TRACE were disseminated 

initially (i.e. from July 2002). The full dissemination of trades started from 

October 2004. Thus, our dataset from TRACE covers the period from October 

2004 to March 2009. By including these databases, we are able to test our 

model over a longer sample period covering the last three economic recessions. 3 

2 Lown and Morgan (2006), and Muller (2009) also use this survey as a proxy for credit conditions. 

3 Previous studies using NAIC database include Campbell and Taksler, (2003) and Davydenko 

and Strebulaev, (2004). Studies using Warga database include Elton, Gruber, Agrawal 

and Mann, (2001) and Collin-Dufresne, Goldstein, and Martin, (2001) and those using TRACE 

3

The rest of the paper is organized as follows. Section 2 presents details 

about the data cleaning procedure and the regime shift detection technique. 

Section 3 describes the data and the methodology used to obtain yield curves for 

credit spreads. Section 4 discusses empirical results and economic implications. 

Robustness checks are presented in Section 5. Section 6 reviews the relevant 

literature in motivating the theoretical underpinnings of why credit spreads 

should be related to monetary policy actions as well as the economic cycle. The 

conclusion is in Section 7. 

2 Regime shift detection technique 

The applied method is based on sequential Student’s t-tests for shifts in the 

mean and on sequential F-tests for shifts in the variance. For each new observation 

in the data, we test the null hypothesis for possible regime shifts 

whether in the mean or in the variance of credit spreads. Potential shifts are 

then confirmed if subsequent data in the new regime pass a last confirmation 

test. This procedure is similar to the Sequential T-test Analysis of Regime 

Shifts (STARS) method developed by Rodionov (2004). It also incorporates the 

extension of Rodionov (2005 and 2006), in that it overcomes problems related 

to the way test statistics deteriorate toward the ends of time series and also accounts 

for outliers, serial correlation, and any hidden noise process in the data. 

The latter takes the form of a stationary positive autoregressive process and its 

dynamics resembles to a process with long swings above and beyond its mean. 

If the data generating process contains such type of noise process, then any 

long falling and rising episodes observed in the data may be driven from the 

noise process or from the data or from both. To ensure that detected regimes 

are only driven from the data we carefully filter our data using a prewhitening 

procedure. 

Using the filtered data, we perform the regime detection technique on two 

stages. In a first stage, we detect shifts in the level of credit spreads using 

the mean detection technique. After removing level regimes, we apply, in a 

second stage, the variance detection technique to the residuals. We describe 

these steps here. 

include Han and Zhou, (2008), and Dick-Nielsen, Feldhütter, and Lando, (2009). 

4

2.1 The prewhitening procedure 

Consider that credit spread series are described by a structural time series 

fY t ; t = 1; 2; :::; ng that can be seen as the sum of a trend f t and an error term " t : 

Y t = f t + " t ; (1) 

where " t are independently and normally distributed with zero mean and variance 

2 . If the data contains two distinct regimes with the mean of the current 

regime being 1 and the mean of the new regime being 2 then the trend f t 

satisfies: 

f t = 

( 

1 ; t = 1; 2; :::; c 1; 

2 ; t = c; c + 1; :::; n: 

(2) 

This means that we should detect a statistically significant breakpoint at 

time t = c. The direct approach to regime shift detection is to formulate the 

null hypothesis H 0 : 1 = 2 = : By assuming that the data does not contain 

two distinct regimes, the Student’s t test should reject the null at the required 

probability level . Working with relatively short time series, it is hard to 

draw any definitive conclusion about the underlying process based on the data 

alone. Indeed, we can reject the null not because credit spread series contain 

different regimes but because they contain a noise process that behaves like a 

process with different regimes. This is known in the corresponding literature 

as a red noise process. A stationary red noise process is usually modelled by a 

first order autoregressive process (AR1). When the data contains such process 

its dynamics satisfies Equation (3) rather than Equation (2): 

Y t = Y t 1 + 0 + " t ; (3) 

where 0 = (1 ) . For the process to be stationary, it is necessary for the AR1 

parameter to satisfy the condition jj < 1. With > 0, the process is a red 

noise. Each realization of a red noise process creates extended intervals or runs 

where the time series will remain above or below its mean value (Kendall and 

Stuart, 1966; Rudnick and Davis, 2003). These intervals can be misinterpreted 

as different regimes. Therefore, it is necessary to either recalculate the significance 

level by taking into account the serial correlation or use a prewhitening 

procedure, which consists in estimating accurately the AR1 coefficient (^) using 

the appropriate method: If red noises are present in the data, we remove them 

5

y using the difference (Y t ^Y t 1 ) : 

Another misspecification problem arises when the time series contain simultaneously 

true distinct regimes and a red noise, that is, if the underlying 

model takes the following form: 

Y t = Y t 1 + f 0 t + " t (4) 

where f 0 t = f t f t 1 . Equation (4) looks like a combination of equations (2) and 

(3). In this case, using all the available data to estimate would be misleading. 

A possible solution to this problem is to use subsampling. 

This consists on 

reestimating the correlation coefficient using smaller periods of time. 

example, if regime shifts occur at a regular interval of size m (say months), the 

subsampling procedure requires a subsample size n being less than or equal to 

(m + 1) =3 (see Rodionov, 2006). In other words, the size of subsamples should 

be chosen so that the majority of them do not contain change points. Using 

subsampling procedure, the estimate of can be chosen as the median value 

among the estimates for all subsamples. 4 

The difficulty with the prewhitening procedure is to obtain an accurate estimate 

of the AR1 coefficient for short subsamples of size n since the traditional 

techniques such as the Ordinary Least Squares (OLS) and the Maximum Likelihood 

Estimation (MLE) lead to biased estimates for . For this purpose, two 

alternative methods are proposed in Rodionov (2006): the MPK (Marriott-Pope 

and Kendall) and the IP4 (Inverse Proportionality with 4 corrections) techniques. 

The MPK technique is based on the formula of the bias in the OLS 

estimate of AR1 (Marriott and Pope, 1954 and Kendall, 1954). The IP4 technique 

is based on the assumption that the bias is approximately proportionate 

to the size of the sample (Orcutt and Winokur, 1969, and Stine and Shaman, 

1989). Both methods perform better than the OLS and are similar to one another 

for n 10. Rodionov (2006) shows that, based on Monte Carlo estimations, 

IP4 substantially outperforms MPK for shorter subsamples. As we have 

relatively short samples, we use the IP4 technique to estimate the autoregressive 

coefficient. After the AR1 coefficient is accurately estimated and the red 

noise is removed, the filtered time series Z t = f 0 t + " t can be processed with the 

regime shift detection method. 

4 For our empirical application, when the initial cut-off length equals 6 months we set n equal 

to 3:months which is the minimum subsample size required to estimate the coefficient of each 

subsample. In all cases, our sample estimate of is the median among subsamples estimates. 

For 

6

2.2 Shifts in the mean 

Let Z 1 ; Z 2 ; Z 3 ; :::; Z i be the filtered credit spread series with new data arriving 

regularly. When a new observation arrives, a Student’s t 

test for the mean 

is performed to check whether this new observation represents a statistically 

significant deviation from the mean value of the current regime. The difference 

between mean values of two subsequent regimes that would be statistically 

significant at the level mean according to the Student’s t 

test is given by: 

q 

diff = t 2m 

2 2s 2 m=m; (5) 

where m is the initial cut-off length of regimes similar to the cut-off point in lowpass 

filtering; t 2m 2 

is the value of the two-tailed t distribution with (2m 2) 

degrees of freedom at the given probability level mean . At this stage, the sample 

variance s 2 m is assumed to be the same for both regimes and equal to the 

average variance over the m month intervals in the time series fZ t g : 

The initial current regime contains the initial m observed values and the initial 

new regime contains the subsequent m observed values. The sample mean 

of the current regime Z cur is known but the mean value of the new regime Z new 

is unknown: At the current time t cur = t m + 1; the current value Zi 

cur qualifies h 

for a shift to the new regime if it is outside the critical threshold Z # crit; Z " crit , 

where: 

( 

Z " crit = Z cur + diff; 

Z # crit = Z cur 

diff: 

Here, Z " crit is the critical mean if the shift is upward, and Z # criti 

is critical h 

mean if the shift is downward. If the current value Z cur is inside Z # crit; Z " crit 

range, then it is assumed that the current regime has not changed and the 

null hypothesis H 0 about the existence of a shift in the mean at time t cur is 

rejected. In this case, the value Z cur is included in the current regime and the 

test continues with the next value at t cur = t m + 2. However, if the current 

value Z cur is greater than Z " crit or less than Z # crit, the month t cur is marked as a 

potential change point c, and the subsequent data are used to confirm or reject 

this hypothesis. 

The testing consists in calculating the Regime Shift Index 

(RSI) that represents a cumulative sum of normalized anomalies relative to 

the critical mean Z crit : 

(6) 

7

RSI = 1 jX 

 

Z i Z crit ; j = tcur ; t cur + 1; :::; t cur + m 1: (7) 

ms m 

i=t cur 

 

If anomalies Z i Z crit are of the same sign as the one at the time of a 

regime shift (i.e. positive if the shift is upward and negative if the shift is downward), 

it would increase the confidence that the shift did occur. The converse is 

true if anomalies have opposite signs. Therefore, if at any time during the testing 

period from t cur to t cur +m 1 the RSI turns negative, when Z crit = Z " crit, or 

positive, when Z crit = Z # crit, the null hypothesis about the existence of a shift in 

the mean at time t cur is rejected. In this case, the value Z cur is included in the 

current regime, the RSI takes the value of zero and the test continues for the 

next value at t cur = t m + 2. Otherwise, the time t cur is declared a change point 

c and is significant at least at the probability level mean : Then, the current 

regime includes the value observed at t m + 1 and the test will continue further. 

2.3 Shifts in the variance 

The procedure for detecting regime shifts in the variance is similar to the one 

for the mean, except that it is based on the F test instead of the Student’s 

t test. We now work with the residuals f i g left in the data after means of the 

detected regimes are removed (i.e. we assume that the mean value of the time 

series is now zero). The F test consists in comparing the ratio of the sample 

variances for two successive regimes with their critical value: 

s 2 cur 

s 2 new 

where F ( 1 ; 2 ; var ) is the value of the F 

F ( 1 ; 2 ; var ) ; (8) 

distribution with 1 and 2 degrees 

of freedom and a significance level var : In our application 1 = 2 = m 

1: The sample variance s 2 cur is the sum of squares of i , where i spans from 

the previous shift point in the variance (which is the first point of the current 

regime) to t cur 1: At the current time t cur , the variance s 2 new is unknown. 

For the new regime to be statistically different from the current regime, the 

variance s 2 new should be equal or greater than the critical variance s 2" 

crit 

, if the 

current variance is significantly increasing. However, if the current variance is 

significantly decreasing, the variance s 2 new should be equal or less than s 2# 

crit . 

8

( 

s 2" 

crit = s2 curF m; 2 

var 

; 

s 2# 

crit = s2 cur=F m; 2 

var 

: 

(9) 

If at any time t cur , the current value of cur satisfies the following conditions, 

2 cur > s 2" 

crit when the shift is up or 2 cur < s 2# 

crit 

when the shift is down, this time 

is marked as a potential shift point, and subsequent values cur+1 ; cur+2 ; ::: are 

used to verify this hypothesis. The verification is based on the Residual Sum of 

Squares Index (RSSI) defined as: 

RSSI = 1 jX 

2 i s 2 

crit ; j = tcur ; t cur + 1; :::; t cur + m 1: (10) 

m 

i=t cur 

If at any time during the testing period from t cur to t cur + m 

1; the index 

turns negative, when s 2 crit = s2" crit ; or positive, when s2 crit = s2# crit 

; the null hypothesis 

about the existence of a shift in the variance at time t cur is rejected, 

and the value cur is included in the current regime. Otherwise, the time t cur is 

declared a change point c: 

2.4 Handling outliers 

Due to outliers, the average may not be representative for the mean value of 

the regimes, and this may significantly affect the results of the regime shift 

detection. Ideally the weight for the data value should be chosen such that it is 

small if that value is considered as an outlier. In order to reduce the effect of 

outliers, we use the Huber’s weight function which is calculated as: 

weight = min (1; h= [diff=]) (11) 

where h is is the Huber parameter and [diff=] is the deviation from the expected 

mean value of the new regime normalized by the standard deviation 

averaged for all consecutive sections of the cut-off length in the series. The 

weights are equal to one if [diff=] is less than or equal to the value of h. 

Otherwise, the weights are inversely proportional to the distance from the expected 

mean value of the new regime. Once the timing of the regime shifts is 

fixed, the mean values of the regimes are assessed using the following iterative 

procedure. First, the arithmetic mean is calculated as the initial estimate of the 

mean value of the regime. Then a weighted mean is calculated with the weights 

determined by the distance from that first estimate. The procedure is repeated 

9

one more time with the new estimate of the regime mean. Since we expect that 

most shifts occur around recessions, the choice of the Huber parameter may be 

critical because most significant picks in credit spread rates around this period 

could be considered as outliers. Thus, we repeat the procedures for a range of 

values of h from 1 to 10 (see robustness analysis in Section 5). 

3 Data 

3.1 Corporate bond data 

The NAIC database. The National Association of Insurance Commissioners 

database (NAIC) have supplanted the Lehman brothers database and unlike 

this database, NAIC provides transaction rather than quoted price data for 

US corporate bonds. The database is available since 1994 and reports trades 

made by American insurance companies, which are major investors in corporate 

bond markets. Insurers reporting their trades in the NAIC database are 

of three types including Life insurance companies, property and casualty insurance 

companies, and Health Maintenance Organizations. The database fairly 

reflects the trading activity in the bond market from 1994. Our sample period 

covered by the NAIC database spans from January 1994 to December 2004. 

The same transaction data in NAIC database may be reported twice when it 

involves two insurance companies on the buy and sell side. In this case, only 

one side is included in the sample. 

The Lehman Brothers database. Commonly named Warga database due to 

its founder Arthur Warga. We include the data from Warga database to cover 

the 1991 recession. This data provides the information on monthly prices (quote 

and matrix prices) of U.S. corporate bonds from January 1987 to December 1996 

(Warga, 1998). We consider in this sample only bonds included in the Lehman 

Brothers’ bond indexes and having quoted rather than matrix prices. 

The TRACE database. Data from TRACE database became available only 

from July, 1, 2002. The database is provided by the Financial Industry Regulatory 

Authority (FINRA); formerly named National Association of Security 

Dealers (NASD). TRACE reports the information about almost all trades in the 

secondary over-the-counter market for corporate bonds accounting for 99% of 

the total trading volume. However, not all the trades reported to TRACE were 

disseminated from July 2002. The dissemination process occurred on three 

phases over almost two years in order to test the effect of enhancing price 

10

transparency on the bond market. The final dissemination phase started in 

October 2004 and from this date all trades are disseminated. Thus, our data 

from TRACE covers the period from October 2004 to March 2009. Dick-Nielsen 

(2009) analyzed the reports in TRACE and found many duplicates and other 

special features that we should account for when filtering the data. For example, 

when a trade occurs without being disseminated in the following 5 to 15 

minutes, it is reported twice to indicate the time of the trade and the time of 

the report. We, thus, employ the filter suggested in Dick-Nielsen (2009) to clean 

this sample. 

Bond characteristics. Characteristics of corporate bonds are obtained from 

the Fixed Investment Securities Database (FISD). The FISD database, provided 

by LJS Global Information Systems, Inc. includes descriptive information 

about US issues and issuers (bonds characteristics, industry type, characteristics 

of embedded options, historical credit ratings, bankruptcy events, auction 

details, etc.). Our three samples (NAIC, Warga, and TRACE) are restricted to 

fixed-rate US dollar bonds in the industrial sector. We exclude bonds with embedded 

options such as callable, putable or convertible bonds. We also exclude 

bonds with remaining time-to-maturity below 1 year. With very short maturities, 

small price measurement errors lead to large yield deviations, making 

credit spread estimates noisy. Bonds with more than 15 years of maturity are 

discarded since the swap rates that we use as a benchmark for risk-free rates 

have maturities below 15 years. We finally exclude bonds with over-allotment 

options, asset-backed and credit enhancement features and bonds associated 

with a pledge security. Issuers credit ratings are reported by four rating agencies: 

Fitch Rating, Duff and Phelps Rating, Moody’s Rating and Standard and 

Poor’s Rating. We include all bonds whose average Moody’s credit rating lies 

between AA and BB except for our sample from Warga database. The number 

of reported prices for BB-rated corporate bonds in Warga is not sufficient 

to extract the Nelson-Siegel-Svensson yield curve requiring at least 6 reports 

for each month. Triple-A credit spreads are not used because we find them 

negative for some periods. For example, with NAIC database, we find that the 

average credit spread for medium term AAA-rated bonds is higher than that of 

A-rated bonds. These same remarks are also noticed by Campbell and Taksler 

(2003) using the same database. We filter out observations with missing trade 

details and ambiguous entries (ambiguous settlement data, negative prices, 

negative time to maturities, etc.). 

The benchmark for risk-free rates. Hull, Predescu, and White (2004) have 

11

addressed the question of which benchmark to use and why They suggest that 

Treasury bond yields are contaminated by liquidity, taxation, and regulation 

and may not be fairly used as completely risk-free rates (More details can be 

found in Hull, Predescu, and White, (2004), page 12.). Following empirical 

analysis, they find that swap rates less 10 basis points are a better benchmark 

for risk-free rates than Treasury bond yields. Thus, we follow Hull, Predescu, 

and White, (2004) in our choice of the risk-free benchmark. Swap rates are 

collected from DataStream. Since US swap rates are available from April 1996, 

the sample from Warga starts from this date instead of January 1996. To obtain 

smoothed yield curves for corporate bonds and swaps we use the Nelson-Siegel- 

Svensson algorithm. 

Summary statistics. Table 1 provides descriptive statistics for credit spreads. 

Overall, credit spreads are consistent with bonds rating structures. The level 

of credit spreads from NAIC, Warga and TRACE, are comparable to previous 

studies including, respectively, Campbell and Taksler (2003), Elton et al. (2001) 

and Dick-Nielsen et al. (2009). It is worth noting that the two samples from 

NAIC and TRACE data demonstrate a much more coverage of credit rating categories 

than the sample from Warga, which is concentrated in very high-quality 

issues. For example, in Warga dataset, the percentage of AA, A, BBB spreads is 

respectively 3.41%, 18.21%, and 27.27% while in NAIC dataset, we account for 

10% of AA spreads, 40.59% of A spreads, 38.45% of BBB spreads and 10.95% of 

BB spreads. 

[Insert Table 1 here] 

3.2 Credit spread curve 

To obtain credit spread curves for different ratings and maturities, we use the 

extended Nelson-Siegel-Svensson specification (Svensson, 1995): 

" # 

1 exp( 

T 

 

R(t; T ) = 0t + 1t 

) 

1t T 

1t 

+ 3t 

" 

1 exp( 

T 

2t 

) 

T 

2t 

exp( 

+ 2t " 1 exp( T 

1t 

) 

T 

1t 

exp( 

# 

T 

) 

1t 

# 

T 

) 

2t 

+ " t;j ; (12) 

with " t;j N(0; 2 ): R(t; T ) is the continuously compounded zero-coupon 

rate at time t with time to maturity T: 0t is the limit of R(t; T ) as T goes to 

12

infinity and is regarded as the long term yield. 1t is the limit of the spread 

R(t; T ) 

0t as T goes to infinity and is regarded as the long to short term 

spread. 2t and 3t give the curvature of the term structure. 1t and 2t measure 

the rate at which the short-term and medium-term components decay to zero. 

Each month t we estimate the parameters vector t = ( 0t ; 1t ; 2t ; 3t ; 1t ; 2t ) 0 

by minimizing the sum of squared bond price errors over these parameters. We 

weigh each pricing error by the inverse of the bond’s duration because longmaturity 

bond prices are more sensitive to interest rates: 

XN t 

b t = arg min 

t 

i=1 

w 2 i 

P NS 

it P it 

2 

; wi = 

1=D i 

P N 

i=1 1=D ; (13) 

i 

where P it is the observed price of the bond i at month t, Pit 

NS the estimated price 

of the bond i at month t, N t is the number of bonds traded at month t, N is the 

total number of bonds in the sample, w i the bond’s i weight, and D i the modified 

Macaulay duration. The specification of the weights is important because it 

consists in overweighting or underweighting some bonds in the minimization 

program to account for the heteroskedasticity of the residuals. A small change 

in the short term zero coupon rate does not really affect the prices of the bond. 

The variance of the residuals should be small for a short maturity. Conversely, 

a small change in the long term zero coupon rate will have a larger impact on 

prices, suggesting a higher volatility of the residuals. 

Credit spreads for corporate bonds paying a coupon are the difference between 

corporate bond yields and swap rates with the same maturities. 

4 Results 

Observed credit spreads exhibit successive falling and rising episodes over time. 

These episodes can be observed in changes in the level and/or the volatility of 

credit spreads especially around periods of economic recession and financial 

crises. Figure 1 reports the time series of 10-year credit spreads obtained with 

different datasets (Panel A to Panel C). 

[Insert Figure 1 here] 

Closer inspection of Figure 1, Panel A, indicates that for most rating classes 

the rising episode in credit spread series starts around mid-2000 before the 

NBER beginning date of recession (March 2001) and takes several years to 

13

come down close up to its initial level before the recession. This rising episode 

drives the aggregate level of credit spreads from an average of 1% before the 

recession to a level between 3% and 8% bounded by AA and BB spreads during 

and after the NBER recession. While credit spread levels remain high after the 

NBER recession they take a downward slope from mid-2003. When applied to 

the 1990-1991 and 2007 recessions, the same scenario is observed using Warga 

and TRACE datasets, respectively. As shown in Figure 1, credit spreads start 

increasing well before NBER beginning dates of recessions (July 1990 and December 

2007 in Panel B and Panel C, respectively). 

Most of important shifts in credit spread levels occur around economic recessions. 

Table 2 delineates means of current and new regimes and locations and 

signs of detected shifts. Both, credit spreads for AA- and A-rated bonds in the 

NAIC dataset (Panel A) mark a positive shift in March 2001 (RSI>0) driving 

their levels from 0.875% and 1.058% to 3.795% and 3.935%, respectively. Levels 

of BBB and BB spreads around the recession shift up significantly in two 

times. First positive shifts occur in December 2000 preceding the beginning 

date of the recession. They drive credit spread levels from 1.513% and 3.747% 

to 3.119% and 6.065% for BBB and BB spreads, respectively. Then, second 

positive shifts follow up in September 2001 and July 2002 while the 2001 recession 

ended in November 2001. Although second shifts are lower in magnitude 

than first shifts they take credit spreads into new level regimes with means of 

4.905% and 7.782% for BBB and BB spreads, respectively. High level regimes 

last around 40 months for AA to BBB and 29 months for BB spreads. After 

first positive shifts, detected before the beginning of the 2001 recession, credit 

spreads remain high for more than two years. Then, first negative shits in BBB 

and BB spreads of May 2004 and May 2003 (RSI

date of recession. These first shifts are first warnings to the coming recession. 

Second positive shift are more important in terms of magnitude and occur 

three (A and BBB spreads) to four (AA spreads) months after the beginning 

of the NBER recession (Table 3, Panel B). Negative shifts bringing back credit 

spreads close up to their original levels before the recession are only detected by 

July 1994 hence up to two years following the end of the recession. From Figure 

2, Panel A, we have also detected similar negative shifts in October 1994 for A 

and BBB spreads using NAIC dataset. As NAIC data starts from January 1994, 

these negative shifts mark residual effects from the precedent recession. Then, 

using high frequency data from TRACE going back to October 2004 and forth 

to March 2009, all detected shifts are positive (Figure 2, Panel C). This means 

that first signals of recovery as of March 2009 as still to be felt. Interestingly, 

for investment grade bonds first positive shifts are detected at the NBER’s beginning 

date of recession, except for A spreads where the shift is detected one 

month later (Table 2, Panel C). For speculative grade bonds, the first positive 

shift is detected three months earlier. In all cases, second peaks are detected 

roughly one year later (between September and December 2008). 


Results in Figure 2 reveal interesting patterns in detected shifts across ratings. 

An interesting question would be how these patterns change across maturities 

for a fixed rating class. We plot our results using NAIC dataset in Figure 

3. We consider three maturities of 3-, 5-, and 10 years as benchmarks for short, 

medium, and long terms. Typically, we detect beginnings of high level regimes 

in credit spreads with lower ratings and shorter maturities before those with 

higher ratings and longer maturities. Most often, beginnings of high level 

regimes for higher ratings and longer maturities are detected contemporaneously 

with the official beginning dates of recessions. Then, during recessions, 

credit spread peaks are very high when ratings are low as riskier firms are 

more likely to default in economic downturns than safer firms. After ending 

dates of recessions, first negative shifts are typically detected with lower ratings 

and shorter maturities while their levels are still maintained high above 

those of higher ratings and come down gradually and slowly. This suggests that 

the recovery phase for riskier firms with weaker balance sheets resulting from 

bad times is slower. 


15

Regimes detected in credit spread residuals account for statistically significant 

shifts in credit spread volatilities after removing level regimes. 5 

Unlike 

level regimes, volatility regimes are detected inside and outside economic recessions. 

In particular, when the onset of any economic shock does not have 

the effect to increase credit spread levels sufficiently in order to drive them 

into a new level regime; the effect of the shock is left in the residuals and thus 

detected also outside recessions (Table 3). 


As shown in Table 3, most important shifts in volatilities are detected around 

recessions. Unlike level regimes, volatility regimes in most cases are short and 

occur at dates close to NBER dates of beginning and ending of recessions. In 

addition, similar to level regimes, shifts are detected earlier in the case of lower 

ratings. During the 2001 recession, high regimes of volatility start between November 

2000 (BB spreads) and February 2001 (AA spreads). They also end in 

September 2001 in three cases (AA to BBB spreads) and in January 2002 in the 

case of BB spreads. When applied to the 1990-1991 recession, a high volatility 

regime for BBB spreads is detected between February 1991 and August 1991 

and when applied to the 2007 recession, high regimes start between June 2007 

(BB spreads) and October 2007 (AA spreads). An illustrative example is provided 

in Figure 4. Thus, the duration of volatility regimes is close to the NBER 

economic recession while the duratio of level regimes are much longer. Moreover, 

as NBER announcements of beginning and ending dates of recessions are 

always delayed by many months, volatility regimes have the potential to forecast 

NBER periods of recessions. 


Another important finding with shifts in the volatility is that they are also 

detected outside recessions. For example, as shown in Table 4, Panel A, a high 

volatility regime is detected between March 1998 and February 1999 for A 

spreads. Another high regime spans from April 1997 and February 1999 for 

BBB spreads and a similar regime for BB spreads is detected between December 

1996 and December 1998. 

All these high regimes highlight diverse 

economic shocks occurring during the same period namely the Asian financial 

5 Since the volatility has a more straightforward economic sense than the variance we use the 

term volatility regime to designate the variance regime. As the volatility is simply the square 

root of the variance, our results are not affected. 

16

crises of July 1997 and the collapse of LTCM in October 1998. Hence, volatility 

regimes are not necessarily limited to recessions. 

In sum, our findings suggest that volatility regimes result from significant 

economic shocks to the economy including of course recessions. On the other 

hand, our results also suggest that level regimes being always high in recessions 

but never high outside recessions provide better warning for the upcoming 

recessions. Specifically, at the beginning of a recession, we detect a significant 

credit spread level effect as well as a significant credit spread volatility effect. 

Toward the official NBER ending date of recession, the volatility effect weakens 

or vanishes but the level effect is likely to strongly persist for many several 

other years. 

5 Robustness analysis 

5.1 Model initial Parameters 

We test whether the choice of initial parameters has a significant effect on the 

number and the location of detected shifts for credit spread levels and residual 

variances. The key set of parameters is (m; ; h) ; where m is the initial 

cut-off length, is the significance level for detected shifts, and h is the Huber 

parameter controlling for outliers. We repeat the analysis and report changes 

in the number and the location of shifts when initial parameters (m; ; h) take 

different reasonable values: The base case is in box where m = 12; = 5%; 

h = 2: This allows us to increase the initial cut-off length to 18 months and 

to decrease it to 6 months. In order to remove autocorrelations from the data 

we need to work with a subsample size of at least three months. Thus, a minimum 

initial cut-off length of six months is reasonable. For higher initial cut-off 

lengths, the serial correlation is estimated for subsamples of size n equal to 

the integer part of (m + 1) =3. For each choice of the parameter m we may also 

vary the significance level between 5% and 10% and the Huber parameter 

h = 1; 2; 3; 5; 10. We report new detected shifts in Table 4, Panel A: Specifically, 

we report the triplet (shifts unchanged, shifts added, shifts dropped). Shifts 

unchanged for the new parameter set (m; ; h) count the number of shifts detected 

in the same locations or in +/- one month around the same location of the 

shift detected in the base case. Shifts added count the number of shifts added 

located outside their base case locations and similarly, shifts dropped count the 

number of shifts dropped from base case locations. 

17


Overall, our results are robust and they can be summarized as follows. 

First, data values that are higher than h standard deviations are considered 

as outliers and are weighted inversely proportional to their distance from the 

mean value of the new regime: weight = min (1; h=diff) : If the cut-off length 

m = 12 and the confidence level = 5%, the critical difference between the 

regimes is diff = 0:85 which leads to a weight = 1. As the cut-off length increases, 

the weight equals its limit value of one and the results remain the same 

for different values of h since all the data values have equal weights. As shown 

in Table 4, Panel A, when m 12, the number and the location of the shifts in 

the mean remain unchanged for different values of h. However, for shorter cutoff 

lengths and smaller Huber parameters, for example m = 6 and h = 1, values 

higher than one standard deviation will be weighted using weight = 0:78 at the 

5% level. This has the effect to increase the length of the current regime, as 

the diff increases for small cut-off lengths, and decreases the number and the 

magnitude of the shifts in the mean. Second, as the cut-off length increases, the 

degree of freedom also increases, which translates into smaller diff and higher 

values of the RSI for the regimes of m months or longer. However, the regimes 

shorter than the cut-off length can pass the test only if the magnitude of the 

shift is high. For example, for 3-year A credit spreads, when the cut-off length 

increases from 6 months to 18 months, at least 4 shifts remain unchanged. This 

proves that the shifts for the mean value of 3-year A spreads are determined 

correctly. On the other hand, the lower the probability level, the higher the diff 

and the lower the RSI value which leads to a lower number of shifts. Third, 

the number and the location of shifts for the residual variance are more sensitive 

to changes in the confidence level and initial cut-off lengths. This explains 

the movements in the triplet of the variance between shifts added and shifts 

dropped for different confidence levels and initial cut-off lengths. 

5.2 Effects of autocorrelation in the data 

Table 4, Panel B, reports results for shifts detected without removing serial 

correlation from the data. As in Panel A, the initial set of parameter in Panel 

B is the same (m = 12; = 5%; h = 2) but when the prewhitening procedure is 

not applied. For each new set of parameter (m; ; h) we also report changes in 

the number and the location of detected shifts relative to the base case in box. 

Our results suggest that data prewhitening reduces the magnitude and the 

18

number of detected shifts in credit spread levels. Using Monte Carlo technique, 

Rodionov (2006) finds the evidence suggesting that the prewhitening procedure 

is a more conservative way of detecting regime shifts but has the advantage 

of reducing the number of false alarms. In other words, prewhitening the 

data before applying the regime shift detection technique ensures that detected 

shifts come from significant breaks in the data and do not come from any noise 

processes hidden in the data. Thus, most often, the number of shifts added 

in the triplet is higher in Panel B than in Panel A. Finally, the prewhitening 

procedure seems not to very much affect shifts in the residual variance as they 

often remain unchanged for different parameter choices. 

5.3 Effect of the benchmark choice for the risk-free curve 

As noticed in Hull, Predescu, and White (2004) bond traders tend to measure 

the risk-free rate using yields on Treasury zero coupon bonds. It would be 

interesting to test whether our results still hold if we use yields on Treasury 

bonds instead of swap rates as risk-free rates to obtain credit spread yields. 

Similarly, the new risk-free curve is obtained using the Nelson-Siegel-Svensson 

algorithm. We repeat the analysis using the sample from the NAIC database. 

By replacing the benchmark for the risk-free curve, we roughly shift our 

credit spread curves by a constant. Shifts detected using new credit spread 

curves are almost similar to shifts reported in Table 4, Panel A with very few 

exceptions. Thus, our results remain robust to the choice of the benchmark 

curve. 

6 Economic conditions, monetary policy actions and 

credit spreads 

In this section we address the following question: why credit spreads shift up 

before the NBER beginning dates of recessions and remain very high several 

years after the NBER ending dates of recession The answer to this question 

has to do with adverse credit conditions and monetary policy actions. 

Through the use of open market operations, the central bank can directly 

impact the level of short maturity yields. If changes in short rates do not filter 

into yields of all maturities this would create arbitrage opportunities forcing 

long maturity yields to adjust to innovations in the short rates. Thus, by altering 

short rates and controlling the liquidity in the financial system, monetary 

19

policy actions are transmitted to the entire bond yield curve. In addition, shifts 

in the Fed policy affect not only short rates per se but also the financial position 

of borrowers. While a tightening monetary policy stance has the direct effect to 

raise interest rates and interest expenses, indirectly, it reduces firm’s revenues 

and net worth (or equivalently firm balance sheets). 

In their seminal work, Bernanke and Gertler, (1989) show how, “in principle, 

the effects of a real shock (a shock to productivity for example) on financial 

conditions could lead to persistent fluctuations in the economy, even if the initiating 

shock had little or no intrinsic persistence”. A key concept to this result 

is the external finance premium (i.e. the difference between the cost of external 

and internal financing). Based on the theoretical prediction, the external 

finance premium facing a borrower (or equivalently, the cost of capital) should 

depend inversely on the borrower’s creditworthiness, measured by net worth, 

liquidity, leverage, and current and future expected cash flows. The inverse 

relationship of the cost of capital and the creditworthiness of borrowers creates 

a channel (credit channel) through which otherwise short-lived economic 

shocks may have long-lasting effects. A tightening monetary policy increases 

the external finance premium due to an increase in the agency costs of lending. 

According to the financial accelerator theory, this has the effect of weakening 

firm balance sheets resulting from bad times and amplifying the downturn 

through higher investment fluctuations and cyclical persistence. Therefore, after 

a tightening monetary policy, much of the decline in firm balance sheets 

occurs with a lag (Bernanke and Gertler, 1989). In addition, in the short run, 

monetary policy actions have the effect of causing cyclical fluctuations in output 

and employment (reflected for example in the real GDP) while, in the long 

run, the effect is primarily on price levels and firm balance sheets. Fluctuations 

in price levels and firm balance sheets affect the volatility of firm assets 

and under a Merton framework, a rise in the volatility of firm assets increases 

the firm’s default probability, thus expending credit spreads. 6 

Firm balance sheets are not the only factor behind the slow recovery phase. 

Several mitigating factors could be involved. Typically, following an economic 

recession, firms are highly reluctant to make new capital investments or build 

inventories due to the uncertainty about the likely near-term evolution of the 

economy. During the 2001 recession for example, the repeated accounting scandals 

and the perceived high geopolitical risk marked by the War with Iraq and 

6 Increased volatility of assets raises the price of the put option, thus reducing the value of 

corporate debt to bondholders and expanding credit spreads. 

20

the event of September 11 reduced investors’ confidence and increased risk 

aversion in the bond market. As the uncertainty diminishes, investment should 

increase; the demand for new capital at lower cost increases; the restructuring 

activity starts and firm balance sheets improve. 

The observed credit spreads in the bond market during the last three recessions 

highlight the long lasting effects of credit market conditions following 

economic shocks. Credit spreads increased sharply during the last two recessions 

(2001 and 2007) and their levels remained very high, above those seen 

in the 1990-1991 recession as shown in Figure 1 and Table 2. In addition, according 

to NBER dates, the 2001 recession lasts only eight months (March to 

November 2001) while credit spread levels remain high for up to two years – 

especially for longer maturity bonds. As credit spreads begin to increase prior 

to the NBER beginning dates of recession and begin to decrease (around mid- 

2003) after ending dates of the recession we can think that economic recessions 

are not driving credit cycles. When applied to the 2007 and 1990-1991 recessions, 

we are able to distinguish a high level of credit spreads before beginning 

dates of recessions and when applied to the 1990-1991 recession, we are also 

able to distinguish high levels of credit spreads after the ending date of the 

recession. 

So, the question now is what drives and keeps the high levels of credit 

preads around recessions Bernanke and Lown (1991) suggest that, under tight 

monetary policy, a liquidity crunch begets a credit crunch and Jiminez, Ongena, 

Peydró, and Saurina (2007) suggest that tight monetary policy increase liquidity 

crunch and weaken firm balance sheets by reducing firm borrowing capacity 

(demand for new loans) and credit availability (supply for new loans). This suggests 

that tightening credit conditions affect the course of the credit cycle. It 

also suggests that a liquidity crunch may be the principal factor that contorts 

the shape of credit spreads beyond that already documented in Collin-Dufresne, 

Goldstein and Martin (2001), and Dick-Nielsen, Feldhütter, and Lando (2009), 

for example, linking liquidity to credit spreads. As liquidity is affected by the 

healthiness of firm balance sheets and by supply and demand pressures in the 

credit market, we may reasonably think that monetary policy actions and credit 

spread regimes are related. 

Furthermore, by acknowledging that after recessions, firms take several 

years to restructure their balance sheets, reduce their interest burdens, and 

increase liquidity, we are able to argument the long lasting high levels of credit 

spreads over the economic cycle. An additional factor that may have an impact 

21

on credit spread levels could be the NBER announcements of the official ending 

dates of recessions as they help in dissipating the uncertainty in the bond 

market and strengthening investors’ confidence about the future. As an example, 

during the 2001 recession, credit spreads start the downward slope around 

mid-2003 while in July 2003, NBER announced the end of the 2001 recession. 

In sum, the financial accelerator theory, along with the presence of the credit 

channel, highlight how credit market conditions can propagate by amplifying 

cyclical movements in the real economy or strengthening the influence of monetary 

policy. As Bernanke and Gertler (1990) show, disturbances in the financial 

sector have also the potential to initiate cycles. Thus, regardless of its origin, 

a rise in the external finance premium or a deterioration of borrowers’ balance 

sheets eventually results in slower growth and recovery. 

6.1 Monetary policy regimes and credit standards regimes 

A straightforward next step in our analysis would be to analyze the link between 

long lasting regimes in credit spreads and monetary policy actions. We 

first plot, in the Figure 5, the dynamics of fed funds rates against the time 

series of credit spreads. 


From Figure 5, fed funds rates appear as an inverse mirror to the dynamics 

of credit spreads. 7 

Since July 2000, the Fed starts cutting short rates from a 

high level of 6.54% to a low level of 0.98% reached in December 2003 (Figure 

5, Panel A). A low level around 1% is maintained until June 2004 from then 

on short rates start to increase systematically. From Panel C, we notice the 

intensity of the 2007 recession as the Fed starts cutting short rates from August 

2007 up to an exceptional low level of 0.18% in March 2009. Despite actions of 

the Fed focused on fostering economic growth credit spread levels continue to 

increase highlighting the high level of tightening in credit markets conditions. 

In addition, the Federal Reserve initiated, since 1964, a quarterly survey 

on bank lending practices seeking qualitative information on credit availability 

and demand, as well as evolving changes in bank lending practices in the three 

months preceding the survey date. The information obtained from the survey is 

7 Correlation coefficients between credit spreads and fed funds rates are very high especially 

with transaction price data. For example, for AA spreads, correlation coefficients obtained with 

data from NAIC, TRACE, and Warga are respectively -0.95, -0.70, and -0.50. 

22

critical to the Federal Reserve’s monitoring of bank lending practices and credit 

markets as the Fed relies on this information in formulating monetary policy 

actions. 8 

Lown, Morgan, and Rohatgi (2000) find that the Survey data are 

highly negatively correlated with aggregate commercial loan growth and with 

various measures of economic and business activity. Similar results are also 

reported in Lown and Morgan (2006). We use the Senior Loan Officer Survey 

(SLO – Survey) as a proxy for credit market conditions. Thus, we apply the 

regime detection technique to levels of credit spreads and contrast them with 

regimes obtained from fed funds rates and the SLO – Survey data. As survey 

results became available to the public from 1997, we report results from the 

Survey for periods over which we have available reports. 9 

The patterns in regime levels of credit spreads seem to be closely associated 

with credit market conditions and monetary actions of the Fed. Specifically, 

since April 2000, an average of about 44% of senior officers is reporting tightening 

standards for a period of 24 months (Table 5, Panel A). Thus, the information 

provided by the survey data gives a first warning to the onset of adverse 

economic conditions. Thereafter, credit spreads start expanding in response to 

tightening standards. While the 2001 recession ended in November 2001, an 

average of 14.4% of senior loan officers is still reporting tight credit conditions 

from April 2002 to December 2003 highlighting a slow recovery in credit markets 

after the NBER recession. During the same period, credit spread levels 

while still high on average are slowly coming back to their original levels before 

the recession. The dynamics between credit spread levels, credit standards 

and short rates is also verified when applied to the precedent and subsequent 

recessions (Table 5, Panel B and Panel C). During the 1990-1991 recession, an 

average of 48.616% of senior officers is reporting tight credit conditions from 

January 1997 to January 1991. Thereafter, the average tightening comes down 

to about 6% from January 1991 to July 1993. Following the onset of a tight 

8 The survey’s original questions dealt with perceived changes in business loan demand, willingness 

to make business loans, various non-rate aspects of business loan pricing, and willingness 

to extend consumer, mortgage, and certain other types of loans. The survey is conducted 

with the Senior Loan Officers of approximately sixty large domestic banks and twenty-four U.S. 

branches and agencies of foreign banks. Information from the survey is reported regularly to the 

Board of Governors and to the Federal Open Market Committee as an appendix to the Greenbook 

and in other internal briefing materials. It can also be downloaded from the Federal Reserve 

Board’s website. A detailed description of the Survey can be found in Lown and Morgan (2006). 

9 The data consists on the net percent tightening, i.e. the number of respondents reporting 

tightening standards less the number reporting easing divided by the total number reporting. 

Since the Survey is conducted quarterly, we consider that the information on a quarter remains 

unchanged for the three months of the quarter in order to obtain monthly series. 

23

egime in credit standards, credit spreads shift up to higher levels and the Fed 

having collected enough information about the current adverse economic condition 

start cutting short rates. Between December 1990 and January 1994, a 

new monetary policy regime drives short rates down by a half on average (from 

4.135% to 8.707%). 


As stated earlier, the 2007 recession seems to be far more severe than both 

precedent recessions. The increase in tightening in the three months preceding 

the survey date starts from October 2006. One year later, about 26% of respondents 

are reporting tightening standards and this net percentage increases to 

an average level as high as 65.2% from April 2008. On the other hand, fed 

funds rates attend an historical low level of 0.183% which is achieved through 

at least two successive easing monetary policy regimes. Credit spreads move 

to high regime levels around December 2007 for AA to BBB spreads and in 

September 2007 for BB spreads. 

Overall, our results first suggest a close link between regimes in credit 

spread levels and net percentages of tightening standards. Since 1987, tightening 

standards has always provided an earlier warning to upcoming adverse 

economic conditions. Then, since the Fed relies on the information provided 

by the survey data in formulating monetary policy actions and monitoring the 

financial market, monetary policy regimes are found to be very close to credit 

spread level regimes. Based on our results, we clearly discard the close connection 

between level regimes in credit spreads and NBER dates of recessions. 

While all high regimes are concentrated around economic recessions, typically, 

however, they start before recessions and end well after recessions, especially 

for bonds with lower ratings. An illustrative example is provided in Figure 6. 


In response to adverse economic conditions, the Fed intervenes by cutting 

short rates to ease the supply and demand for new loans and prevent the economy 

from a deep recession. During the 2001 recession (Figure 6, Panel A), we 

detect two distinct low regimes for the short rates. A first regime lowering short 

rates from an average level of 5.520% to 3.892% and lasting 12 months. This 

regime comes in response to the first regime of tightening standards of April 

2000. Then, since the economy did not recover after the end of the recession in 

24

November 2001, a second regime of low short rates starts in January 2002. In 

this regime short rates are maintained as low as 1.258% on average during two 

years. 

As of January 2004, an average of 20.55% of senior officers starts reporting 

easing in standards and this seems to provide the first signal of recovery. Since 

June 2004, most of credit spread levels regain their original level regimes before 

the recession and the first positive shift in short rates after January 2001 is 

detected from July 2004. 

Across three recessions, the long lasting of high credit spread levels highlights 

how changes in financial conditions may play a prominent role in the 

contraction and the recovery phases of business cycles. Adverse financial conditions 

have the effect to weaken firms and banks balance sheets. A financial 

system with a weak banking system and high levered firms has the potential to 

slow down the recovery phase. Specific aspects of the financial system may vary 

from cycle to cycle. For example, the recovery from the 1990-1991 recession was 

delayed by the "financial headwinds" arising from regional shortages of bank 

capital (Bernanke and Lown, 1991). In the 2001 recession, the recovery was delayed 

by repeated accounting scandals and the perceived high geopolitical risk 

following the War with Iraq and the event of September 11. As reported tightening 

standards are even higher during the current recession of 2007, we also 

expect more stickiness in high credit spread levels after the recession ends especially 

by accounting for residual effects of the subprime crisis on firm balance 

sheets. 

As reported in Figure 7, volatility regimes, contrarily to level regimes, do 

seem to be closely related to credit market conditions and hence to monetary 

policy regimes. Our previous results rather suggest a close link between volatility 

regimes and big economic shocks such as financial crises and of course recessions. 


7 Conclusion 

We use a new regime shoft detection technique for the same time to detect mean 

and volatility regimes separately. The technique is an exploratory rather than a 

confirmatory approach. The advantage of this is that contrarily to the existing 

literature modeling switching regimes in credit spread series, our methodology 

25

does not require any prior assumptions about the number of the regimes. 

Our results reveal that level regimes in credit spreads are closely related to 

tightening standards regimes and monetary policy regimes. As the Fed relies 

on the information provided by Senior Loan Officers Survey data in monitoring 

the financial system and adverse economic conditions, we find monetary 

policy regimes closely related to level regimes of credit spreads. Both tightening 

standards and level regimes of credit spreads have important forecasting 

information about upcoming downturns. We also find that high level regimes 

have long lasting effects which is likely to be the consequence of a slow recovery 

phase. Typically, weakened firm balance sheets resulting from bad times 

take several years to restructure their balance sheets, reduce their interest expenses, 

and increase liquidity thus slowing the recovery phase and maintaining 

credit spread levels high. 

We also find that systematic shocks affect credit spread levels and volatilities 

in different manners. While level regimes appear to be closely related 

to monetary policy regimes and tightening regimes, volatility regimes are more 

contemporaneous to NBER economic cycles. Then, unlike level regimes, volatility 

regimes are also detected outside recessions signaling the presence of significant 

economic shocks even if those do not ] lead to an economic recession. 

Finally, volatility regimes are found to have the potential of predicting NBER 

recessions as they are detected well before announcements of beginning and 

ending dates of recessions. Our findings should have important implications 

for hedging credit risk and for the regulation of banks. 

Finally, we checked the pattern around recessions using different sample 

periods and this seems to be robust with the data.. 

26

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29

Table 1: Summary statistics on credit spreads. 

The table reports summary statistics on credit spreads for straight fixed-coupon corporate bonds 

in the industrial sector. Summary on different rating classes are reported when the data is 

available. Panel A reports NAIC transaction data from January 1994 to December 2004, Panel 

B, Warga quoted data from January 1987 to December 1996 and Panel C, TRACE high-frequency 

transaction data from October 2004 to December 2009. The benchmark for risk-free rates is 

the swap curve less 10 basis points fitted to all maturities using the Nelson-Siegel-Svensson 

algorithm. The spreads are given as annualized yields in basis points. 

All AA A BBB BB 

Panel A : NAIC Transaction Data from January 1994 to December 2004 

Mean 286 147 167 226 333 

Median 230 98 122 171 271 

St. Dev. 159 113 107 132 184 

5% quantile 109 20 49 84 126 

95% quantile 583 353 357 475 690 

Panel B : Warga Quoted Data from April 1987 to December 1996 

Mean 735 387 553 965 - 

Median 576 346 552 942 - 

St. Dev. 291 226 256 391 - 

5% quantile 118 73 171 361 - 

95% quantile 1385 789 979 1617 - 

Panel C : TRACE Transaction Data from October 2004 to December 2009 

Mean 269 192 184 309 393 

Median 202 164 138 239 315 

St. Dev. 176 116 125 171 186 

5% quantile 82 68 81 133 169 

95% quantile 618 425 503 631 722 

30

Table 2: Summary statistics for changing points in level regimes. 

We report the results of the regime shift detection technique applied to credit spread residuals 

with 10 remaining years to maturity. Panel A to Panel C refer to the data from NAIC, Warga, 

and TRACE datasets, respectively. The initial cut-off length is 6 months and all detected regimes 

are statistically significant at least at the 95% confidence level. The Regime Shift Index (RSI) 

provides the direction of detected shifts. 

Nber of Mean of Length of Date of Mean of Length of RSI 

Shifts current current Shift new new sign 

regime regime point regime regime 


AA 1 0.874 86 Mar-01 3.795 38 0.218 

2 3.795 38 May-04 2.867 8 -0.294 

A 1 2.162 10 Oct-94 1.058 76 -0.378 

2 1.058 76 Mar-01 3.935 39 0.323 

3 3.935 39 Jun-04 2.935 7 -0.323 

BBB 1 2.993 9 Oct-94 1.513 74 -0.784 

2 1.513 74 Dec-00 3.119 9 1.140 

3 3.119 9 Sep-01 4.905 32 1.165 

4 4.905 32 May-04 3.989 4 -0.502 

5 3.989 4 Sep-04 2.943 4 -0.441 

BB 1 3.747 83 Dec-00 6.065 19 1.981 

2 6.065 19 Jul-02 7.782 10 0.720 

3 7.782 10 May-03 5.738 16 -0.439 

4 5.738 16 Sep-04 3.875 4 -0.767 

31

Table 2 (Continued). 





AA 1 0.201 32 Dec-89 0.394 12 0.218 

2 0.394 12 Dec-90 0.542 43 0.294 

3 0.542 43 Jul-94 0.334 30 -0.041 

A 1 0.222 19 Nov-88 0.452 23 0.204 

2 0.452 23 Oct-90 0.807 45 1.249 

3 0.807 45 Jul-94 0.511 30 -0.256 

BBB 1 0.518 32 Dec-89 1.045 10 0.360 

2 1.045 10 Oct-90 1.683 8 1.015 

3 1.683 8 Jun-91 1.235 37 -0.395 

4 1.235 37 Jul-94 0.847 30 -0.258 


AA 1 0.676 38 Dec-07 1.574 12 1.721 

2 1.574 12 Dec-08 2.062 4 0.672 

A 1 0.975 39 Jan-08 2.187 8 1.022 

2 2.187 8 Sep-08 4.008 7 1.353 

BBB 1 1.479 38 Dec-07 3.120 10 0.209 

2 3.120 10 Oct-08 5.235 6 0.805 

BB 1 2.562 35 Sep-07 4.674 14 0.983 

2 4.674 14 Nov-08 6.924 5 1.048 

32

Table 3: Summary statistics for changing points in credit spread residuals. 

We report the results of the regime shift detection technique applied to credit spread residuals 


and TRACE datasets, respectively. The initial cut-off length is 6 months and all detected regimes 

are statistically significant at least at the 95% confidence level. The Residual Sum of Squares 

Index (RSSI) provides the direction of detected shifts. 

Nber of Variance Length of Date of Variance Length of RSSI 

Shifts of current new Shift of new new sign 



AA 1 0.212 10 Nov-94 0.064 75 -0.125 

2 0.064 75 Feb-01 0.415 7 2.456 

3 0.415 7 Sep-01 0.148 40 -0.269 

A 1 0.207 10 Nov-94 0.141 19 -0.159 

2 0.141 19 Jun-96 0.029 21 -0.009 

3 0.029 21 Mar-98 0.164 11 0.036 

4 0.164 11 Feb-99 0.048 23 -0.017 

5 0.048 23 Jan-01 0.408 8 2.176 

6 0.408 8 Sep-01 0.068 13 -0.245 

7 0.068 13 Oct-02 0.053 27 -0.005 

BBB 1 0.288 9 Oct-94 0.108 19 -0.177 

2 0.108 19 May-96 0.018 11 -0.008 

3 0.018 11 Apr-97 0.113 22 0.044 

4 0.113 22 Feb-99 0.053 22 -0.004 

5 0.053 22 Dec-00 0.485 9 3.391 

6 0.485 9 Sep-01 0.224 20 -0.179 

7 0.224 20 May-03 0.117 20 -0.027 

BB 1 0.525 10 Nov-94 0.208 25 -0.158 

2 0.208 25 Dec-96 0.288 24 0.062 

3 0.288 24 Dec-98 0.151 23 -0.078 

4 0.151 23 Nov-00 0.564 14 0.863 

5 0.564 14 Jan-02 0.209 23 -0.666 

6 0.209 23 Dec-03 0.488 13 0.021 

33

Table 3 (Continued). 

Nber of Variance Length of Date of Variance Length of RSSI 

Shifts of current new Shift of new new sign 



AA 1 0.019 113 Sep-96 0.006 4 -0.002 

A 1 0.020 116 Dec-96 0.009 1 -0.001 

BBB 1 0.053 13 May-88 0.028 33 -0.003 

2 0.028 33 Feb-91 0.215 8 0.037 

3 0.215 8 Aug-91 0.023 63 -0.021 


AA 1 0.056 36 Oct-07 0.093 16 0.021 

2 0.093 16 Feb-09 0.017 2 -0.026 

A 1 0.060 37 Nov-07 0.129 17 0.019 

BBB 1 0.133 35 Sep-07 0.289 19 0.014 

BB 1 0.629 21 Jul-06 0.161 11 -0.062 

2 0.161 11 Jun-07 0.505 22 0.078 

34

Table 4: Sensitivity analysis for model parameters. 

We consider the base case where m = 12; = 0:05; and h = 2: Then, we report changes in the 

number and the location of new detected shifts using each of the new parameter sets through the 

triplet (shifts unchanged, shifts added, shifts dropped). The parameter m is the cut-off length, 

is the significance level for detected shifts, and h is the Huber parameter controlling for outliers. 

The subsample size for serial correlation n is equal to the maximum between 3 months and the 

integer part of (m + 1)=3. The base case is in box. 

Panel A: with prewhitening Panel B: without prewhitening 

Mean Variance Mean Variance 

m h A-3 A-10 A-3 A-10 A-3 A-10 A-3 A-10 

yrs yrs yrs yrs yrs yrs yrs yrs 

6 0.05 1 (4,1,0) (3,0,0) (2,1,2) (6,1,2) (4,2,0) (3,1,0) (1,0,2) (4,2,2) 

6 0.05 2 (4,1,0) (3,0,0) (2,1,2) (6,1,2) (5,1,0) (4,0,0) (2,0,1) (4,2,2) 

6 0.05 3 (4,1,0) (3,0,0) (2,0,2) (4,1,3) (5,1,0) (4,0,0) (2,0,1) (4,2,2) 

6 0.05 5 (4,1,0) (3,0,0) (2,0,2) (4,1,3) (5,1,0) (4,0,0) (2,0,1) (4,2,2) 

6 0.05 10 (4,1,0) (3,0,0) (2,0,2) (4,1,3) (5,1,0) (4,0,0) (2,0,1) (4,2,2) 

6 0.1 1 (4,4,0) (3,1,0) (2,3,2) (8,2,0) (5,4,0) (4,5,0) (1,0,2) (4,4,2) 

6 0.1 2 (4,3,0) (3,0,0) (2,2,2) (8,0,0) (5,4,0) (4,6,0) (2,0,1) (4,4,2) 

6 0.1 3 (4,3,0) (3,0,0) (2,2,2) (8,0,0) (5,4,0) (4,6,0) (2,0,1) (4,4,2) 

6 0.1 5 (4,3,0) (3,0,0) (2,2,2) (8,0,0) (5,4,0) (4,6,0) (2,0,1) (4,4,2) 

6 0.1 10 (4,3,0) (3,0,0) (2,2,2) (8,0,0) (5,4,0) (4,6,0) (2,0,1) (4,4,2) 

12 0.05 1 (4,0,0) (2,0,1) (4,0,0) (7,1,1) (5,0,0) (4,0,0) (2,0,1) (4,0,2) 

12 0.05 2 (4,0,0) (3,0,0) (4,0,0) (8,0,0) (5,0,0) (4,0,0) (3,0,0) (6,0,0) 

12 0.05 3 (4,0,0) (3,0,0) (4,0,0) (8,0,0) (5,0,0) (4,0,0) (3,0,0) (6,0,0) 

12 0.05 5 (4,0,0) (3,0,0) (4,0,0) (7,1,1) (5,0,0) (4,0,0) (3,0,0) (6,0,0) 

12 0.05 10 (4,0,0) (3,0,0) (4,0,0) (6,1,2) (5,0,0) (4,0,0) (3,0,0) (6,0,0) 

12 0.1 1 (3,2,1) (3,0,0) (3,2,1) (7,1,1) (5,2,0) (4,0,0) (2,1,1) (5,2,1) 

12 0.1 2 (4,1,0) (3,0,0) (3,1,1) (6,2,2) (5,2,0) (4,0,0) (3,1,0) (5,3,1) 

12 0.1 3 (4,1,0) (3,0,0) (3,1,1) (6,2,2) (5,2,0) (4,0,0) (3,1,0) (5,3,1) 

12 0.1 5 (4,1,0) (3,0,0) (3,1,1) (6,2,2) (5,2,0) (4,0,0) (3,1,0) (5,3,1) 

12 0.1 10 (4,1,0) (3,0,0) (3,1,1) (6,2,2) (5,2,0) (4,0,0) (3,1,0) (5,3,1) 

18 0.05 1 (3,0,1) (2,0,1) (3,1,1) (6,2,2) (4,0,1) (4,0,0) (2,2,1) (4,1,2) 

18 0.05 2 (3,0,1) (2,0,1) (3,0,1) (6,2,2) (4,0,1) (4,0,0) (2,2,1) (3,1,2) 

18 0.05 3 (3,0,1) (2,0,1) (3,0,1) (6,2,2) (4,0,1) (4,0,0) (2,2,1) (3,1,2) 

18 0.05 5 (3,0,1) (2,0,1) (3,0,1) (5,2,3) (4,0,1) (4,0,0) (2,2,1) (3,1,2) 

18 0.05 10 (3,0,1) (2,0,1) (3,0,1) (5,2,3) (4,0,1) (4,0,0) (2,2,1) (3,1,2) 

18 0.1 1 (3,1,1) (2,0,1) (4,2,0) (7,0,1) (4,0,1) (4,1,0) (2,1,1) (4,1,2) 

18 0.1 2 (3,1,1) (2,0,1) (4,1,0) (6,0,2) (4,0,1) (4,1,0) (2,1,1) (4,1,2) 

18 0.1 3 (3,1,1) (2,0,1) (3,1,1) (6,0,2) (4,0,1) (4,1,0) (2,1,1) (4,1,2) 

18 0.1 5 (3,1,1) (2,0,1) (3,1,1) (6,0,2) (4,0,1) (4,1,0) (2,1,1) (4,1,2) 

18 0.1 10 (3,1,1) (2,0,1) (3,1,1) (6,0,2) (4,0,1) (4,1,0) (2,1,1) (4,1,2) 

35

Table 5: Summary statistics for changing points in level regimes. 

We report results of the regime shift detection technique applied to time series of fed funds 

rates, and the Senior Officer Opinion Survey (SLO - Survey) data. Panel A to Panel C report 

shifts detected over time horizons of NAIC, Warga, and TRACE data, respectively. The initial 

cut-off length is 6 months and all detected regimes are statistically significant at least at the 

95% confidence level. The Regime Shift Index (RSI) provides the direction of detected shifts. 




Panel A : Data from January 1994 to December 2004 

Fed funds rates 1 4.422 14 Mar-95 5.52 70 0.909 

2 5.477 70 Jan-01 3.892 12 -1.737 

3 3.891 12 Jan-02 1.258 30 -1.281 

4 1.258 30 Jul-04 1.683 6 0.626 

SLO - Survey 1 -1.974 75 Apr-00 43.938 24 0.32 

2 43.938 24 Apr-02 14.4 21 -0.195 

3 14.4 21 Jan-04 -20.55 12 -0.209 

Panel B : Data from April 1987 to December 1996 

Fed funds rates 1 4.359 34 Aug-07 2.487 17 -0.524 

2 2.487 17 Jan-09 0.183 3 -0.236 

SLO - Survey 1 -15.775 24 Oct-06 0.95 12 0.137 

2 0.95 12 Oct-07 25.7 6 1.638 

3 25.7 6 Apr-08 65.2 12 1.378 

Panel C : Data from October 2004 to December 2009 

Fed funds rates 1 4.359 34 Aug-07 2.487 17 -0.524 

2 2.487 17 Jan-09 0.183 3 -0.236 

SLO - Survey 1 -15.775 24 Oct-06 0.95 12 0.137 

2 0.95 12 Oct-07 25.7 6 1.638 

3 25.7 6 Apr-08 65.2 12 1.378 

36

Figure 1: Times series of observed credit spreads. 

The figure plots the time series of 10-year observed credit spreads for US corporate bonds. Panel 

A to Panel C refer to the data from NAIC, Warga, and TRACE datasets, respectively. Shaded 

regions represent NBER periods of recessions. 

Panel A : NAIC credit spreads from January 1994 to December 2004 

Panel B : Warga credit spreads from April 1987 to December 1996 

Panel C : TRACE credit spreads from October 2004 to December 2009 

37

Figure 2: Level regimes of credit spreads. 

We plot results of the regime shift detection technique applied to levels of credit spreads with 

10 remaining years to maturity. Panel A to Panel C refer to the data from NAIC, Warga, and 

TRACE datasets, respectively. Shaded regions represent NBER periods of recessions. The initial 

cut-off length is 6 months and all detected regimes are statistically significant at least at the 95% 

confidence level. 




38

Figure 3: Maturity effects on credit spread regimes. 

We plot level regimes of credit spreads with remaining maturities of 3, 5, and 10 years. The 

data is from NAIC dataset and covers the period from January 1994 to December 2004. The 

shaded region represents the 2001 NBER recession. The initial cut-off length is 6 months and 

all detected regimes are statistically significant at least at the 95% confidence level. 

39

Figure 4: Volatility regimes for credit spreads. 

We plot the results of the regime shift detection technique applied to credit spread residuals 


and TRACE datasets, respectively. Shaded regions represent NBER periods of recessions. The 

initial cut-off length is 6 months and all detected regimes are statistically significant at least at 

the 95% confidence level. 




40

Figure 5: Credit spreads against fed funds rates. 

The figure plots the time series of 10-year credit spreads of US corporate bonds against the fed 

funds rates observed over the same period. Shaded regions represent NBER periods of recessions 

and Panel A to Panel C plot credit spreads from NAIC, Warga, and TRACE data, respectively. 




41

Figure 6: Regimes of credit spread levels, monetary policy and credit conditions. 

We plot level regimes detected for credit spread with 10 remaining years to maturity. Panel 

A to Panel C refer to the data from NAIC, Warga, and TRACE datasets, respectively. We also 

plot regimes detected using the fed funds rates and the Senior Officer Opinion Survey (SLO - 

Survey) data. Shaded regions represent NBER periods of recessions. The initial cut-off length 

is 6 months and all detected regimes are statistically significant at least at the 95% confidence 

level. 




42

Figure 7: Regimes of credit spread volatilities and credit conditions. 

We plot the results of the regime shift detection technique applied to credit spread residuals with 

10 remaining years to maturity. Panel A to Panel C refer to the data from NAIC, Warga, and 

TRACE datasets, respectively. We also plot regimes detected using the Senior Officer Opinion 

Survey (SLO - Survey) data. Shaded regions represent NBER periods of recessions. The initial 

cut-off length is 6 months and all detected regimes are statistically significant at least at the 

95% confidence level. 




43

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