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The Nonlinear Dynamics of Corporate Bond Spreads: Regime-Dependent Effects of their Determinants

Henning Fischer and Oscar Stolper ORCID logo

Abstract

This paper studies the behavior of corporate bond spreads during different market regimes between 2004 and 2016. Applying a Markov-switching vector autoregressive (MS-VAR) model, we document that the dynamic impact of spread determinants varies substantially with market conditions. In periods of high volatility, systematic credit risk—rather than interest rate movements—contributes to driving up spreads. Moreover, while market-wide liquidity risk is not priced when volatility is low, it becomes a crucial factor during stress periods. Our results challenge the notion that spreads predominantly capture credit risk and suggest it must be reassessed during periods of financial distress.

JEL Classification: C32; C34; C58; G12

Corresponding author: Oscar Stolper, Behavioral Finance Research Group, University of Marburg, Am Plan 1, 35032 Marburg, Germany, E-mail:

Acknowledgments

We thank Peter Winker, the editor, and two anonymous referees for their very helpful comments and suggestions. Moreover, we are grateful to Michel Bartoschik for excellent research assistance.

Appendix

Figure A1a: 
Impulse of corporate bond spread to shocks during (low-volatility) Regime 1
– Alternative causal ordering –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 1, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET → ILLIQUID is imposed. The shaded areas indicate the respective 99% bootstrap confidence interval calculated following Hall (1992).

Figure A1a:

Impulse of corporate bond spread to shocks during (low-volatility) Regime 1

– Alternative causal ordering –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 1, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET → ILLIQUID is imposed. The shaded areas indicate the respective 99% bootstrap confidence interval calculated following Hall (1992).

Figure A1b: 
Impulse of corporate bond spread to shocks during (high-volatility) Regime 2
– Alternative causal ordering –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 2, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET → ILLIQUID is imposed. The shaded areas indicate the respective 99% bootstrap confidence interval calculated following Hall (1992).

Figure A1b:

Impulse of corporate bond spread to shocks during (high-volatility) Regime 2

– Alternative causal ordering –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 2, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET → ILLIQUID is imposed. The shaded areas indicate the respective 99% bootstrap confidence interval calculated following Hall (1992).

Figure A2: 
Underlying regimes in corporate bond index spreads
– Extended sample –

Notes: The upper graph plots the smoothed probability of being in Regime 2, 



Pr

(


s
t

=

2
|


Y
T


)



$\mathit{Pr}\left({s}_{t}=2\vert {\boldsymbol{Y}}_{T}\right)$


, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) and using the variables introduced in section 3.2 for the extended sample period 1997–2016. The bottom graph presents the time series of the corporate bond index spread, with the shaded areas indicating periods when Regime 2 prevails, i.e. the smoothed probability of being in Regime 2 is 0.5 or greater.

Figure A2:

Underlying regimes in corporate bond index spreads

– Extended sample –

Notes: The upper graph plots the smoothed probability of being in Regime 2, Pr ( s t = 2 | Y T ) , based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) and using the variables introduced in section 3.2 for the extended sample period 1997–2016. The bottom graph presents the time series of the corporate bond index spread, with the shaded areas indicating periods when Regime 2 prevails, i.e. the smoothed probability of being in Regime 2 is 0.5 or greater.

Figure A3a: 
Impulse of corporate bond spread to shocks during (low-volatility) Regime 1
– Extended sample –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 1, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2 for the extended sample period 1997–2016. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering ILLIQUID → YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET is imposed. The shaded areas indicate the respective 99% bootstrap confidence interval calculated following Hall (1992).

Figure A3a:

Impulse of corporate bond spread to shocks during (low-volatility) Regime 1

– Extended sample –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 1, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2 for the extended sample period 1997–2016. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering ILLIQUID → YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET is imposed. The shaded areas indicate the respective 99% bootstrap confidence interval calculated following Hall (1992).

Figure A3b: 
Impulse of corporate bond spread to shocks during (high-volatility) Regime 2
– Extended sample –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 2, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2 for the extended sample period 1997–2016. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering ILLIQUID → YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET is imposed. The shaded areas indicate the respective 99% bootstrap confidence interval calculated following Hall (1992).

Figure A3b:

Impulse of corporate bond spread to shocks during (high-volatility) Regime 2

– Extended sample –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 2, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2 for the extended sample period 1997–2016. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering ILLIQUID → YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET is imposed. The shaded areas indicate the respective 99% bootstrap confidence interval calculated following Hall (1992).

Figure A4: 
Noise measure against alternative illiquidity measures

Notes: The upper (bottom) graph plots the noise measure together with the TED spread (LIBOR-OIS spread). In both graphs each time series is normalized such that the respective maximum value is unity.

Figure A4:

Noise measure against alternative illiquidity measures

Notes: The upper (bottom) graph plots the noise measure together with the TED spread (LIBOR-OIS spread). In both graphs each time series is normalized such that the respective maximum value is unity.

Figure A5: 
Underlying regimes in corporate bond index spreads
– TED spread as illiquidity proxy –

Notes: The upper graph plots the smoothed probability of being in Regime 2, 



Pr

(


s
t

=

2
|


Y
T


)



$\mathit{Pr}\left({s}_{t}=2\vert {\boldsymbol{Y}}_{T}\right)$


, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) and using the variables introduced in section 3.2. In this specification, the noise measure is replaced by the alternative illiquidity proxy TED spread. The bottom graph presents the time series of the corporate bond index spread, with the shaded areas indicating periods when Regime 2 prevails, i.e. the smoothed probability of being in Regime 2 is 0.5 or greater.

Figure A5:

Underlying regimes in corporate bond index spreads

– TED spread as illiquidity proxy –

Notes: The upper graph plots the smoothed probability of being in Regime 2, Pr ( s t = 2 | Y T ) , based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) and using the variables introduced in section 3.2. In this specification, the noise measure is replaced by the alternative illiquidity proxy TED spread. The bottom graph presents the time series of the corporate bond index spread, with the shaded areas indicating periods when Regime 2 prevails, i.e. the smoothed probability of being in Regime 2 is 0.5 or greater.

Figure A6: 
Underlying regimes in corporate bond index spreads
– LIBOR-OIS spread as illiquidity proxy –

Notes: The upper graph plots the smoothed probability of being in Regime 2, 



Pr

(


s
t

=

2
|


Y
T


)



$\mathit{Pr}\left({s}_{t}=2\vert {\boldsymbol{Y}}_{T}\right)$


, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) and using the variables introduced in section 3.2. In this specification, the noise measure is replaced by the alternative illiquidity proxy LIBOR-OIS spread. The bottom graph presents the time series of the corporate bond index spread, with the shaded areas indicating periods when Regime 2 prevails, i.e. the smoothed probability of being in Regime 2 is 0.5 or greater.

Figure A6:

Underlying regimes in corporate bond index spreads

– LIBOR-OIS spread as illiquidity proxy –

Notes: The upper graph plots the smoothed probability of being in Regime 2, Pr ( s t = 2 | Y T ) , based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) and using the variables introduced in section 3.2. In this specification, the noise measure is replaced by the alternative illiquidity proxy LIBOR-OIS spread. The bottom graph presents the time series of the corporate bond index spread, with the shaded areas indicating periods when Regime 2 prevails, i.e. the smoothed probability of being in Regime 2 is 0.5 or greater.

Figure A7a: 
Impulse of corporate bond spread to shocks during (low-volatility) Regime 1
– TED spread as illiquidity proxy –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 1, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2. In this specification, the noise measure is replaced by the alternative illiquidity proxy TED spread. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering ILLIQUID → YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET is imposed. The shaded areas show the respective 99% bootstrap confidence interval calculated following Hall (1992).

Figure A7a:

Impulse of corporate bond spread to shocks during (low-volatility) Regime 1

– TED spread as illiquidity proxy –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 1, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2. In this specification, the noise measure is replaced by the alternative illiquidity proxy TED spread. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering ILLIQUID → YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET is imposed. The shaded areas show the respective 99% bootstrap confidence interval calculated following Hall (1992).

Figure A7b: 
Impulse of corporate bond spread to shocks during (high-volatility) Regime 2
– TED spread as illiquidity proxy –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 2, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2. In this specification, the noise measure is replaced by the alternative illiquidity proxy TED spread. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering ILLIQUID → YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET is imposed. The shaded areas show the respective 99% bootstrap confidence interval calculated following Hall (1992).

Figure A7b:

Impulse of corporate bond spread to shocks during (high-volatility) Regime 2

– TED spread as illiquidity proxy –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 2, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2. In this specification, the noise measure is replaced by the alternative illiquidity proxy TED spread. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering ILLIQUID → YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET is imposed. The shaded areas show the respective 99% bootstrap confidence interval calculated following Hall (1992).

Figure A8a: 
Impulse of corporate bond spread to shocks during (low-volatility) Regime 1
– LIBOR-OIS spread as illiquidity proxy –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 1, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2. In this specification, the noise measure is replaced by the alternative illiquidity proxy LIBOR-OIS spread. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering ILLIQUID → YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET is imposed. The shaded areas show the respective 99% bootstrap confidence interval calculated following Hall (1992).

Figure A8a:

Impulse of corporate bond spread to shocks during (low-volatility) Regime 1

– LIBOR-OIS spread as illiquidity proxy –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 1, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2. In this specification, the noise measure is replaced by the alternative illiquidity proxy LIBOR-OIS spread. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering ILLIQUID → YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET is imposed. The shaded areas show the respective 99% bootstrap confidence interval calculated following Hall (1992).

Figure A8b: 
Impulse of corporate bond spread to shocks during (high-volatility) Regime 2
– LIBOR-OIS spread as illiquidity proxy –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 2, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2. In this specification, the noise measure is replaced by the alternative illiquidity proxy LIBOR-OIS spread. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering ILLIQUID → YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET is imposed. The shaded areas show the respective 99% bootstrap confidence interval calculated following Hall (1992).

Figure A8b:

Impulse of corporate bond spread to shocks during (high-volatility) Regime 2

– LIBOR-OIS spread as illiquidity proxy –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 2, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2. In this specification, the noise measure is replaced by the alternative illiquidity proxy LIBOR-OIS spread. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering ILLIQUID → YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET is imposed. The shaded areas show the respective 99% bootstrap confidence interval calculated following Hall (1992).

Figure A9: 
Fitch Ratings’ 1-Year-Ahead PD index (North America) against corporate bond index spreads.

Notes: The upper graph plots Fitch Ratings’ 1-Year-Ahead Probability of Default (PD) index for North America (in basis points); the bottom graph plots the corporate bond index spreads (SPREAD, in percentage points).

Figure A9:

Fitch Ratings’ 1-Year-Ahead PD index (North America) against corporate bond index spreads.

Notes: The upper graph plots Fitch Ratings’ 1-Year-Ahead Probability of Default (PD) index for North America (in basis points); the bottom graph plots the corporate bond index spreads (SPREAD, in percentage points).

Figure A10: 
Underlying regimes in corporate bond index spreads
– Fitch Ratings’ 1-Year-Ahead PD index (North America) as default risk proxy –

Notes: The upper graph plots the smoothed probability of being in Regime 2, 



Pr

(


s
t

=

2
|


Y
T


)



$\mathit{Pr}\left({s}_{t}=2\vert {\boldsymbol{Y}}_{T}\right)$


, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) and using the variables introduced in section 3.2. In this specification, the VIX is replaced by Fitch Ratings’ 1-Year-Ahead Probability of Default Index for North America (PD) as an alternative proxy for aggregate default risk. The bottom graph presents the time series of the corporate bond index spread, with the shaded areas indicating periods when Regime 2 prevails, i.e. the smoothed probability of being in Regime 2 is 0.5 or greater.

Figure A10:

Underlying regimes in corporate bond index spreads

– Fitch Ratings’ 1-Year-Ahead PD index (North America) as default risk proxy –

Notes: The upper graph plots the smoothed probability of being in Regime 2, Pr ( s t = 2 | Y T ) , based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) and using the variables introduced in section 3.2. In this specification, the VIX is replaced by Fitch Ratings’ 1-Year-Ahead Probability of Default Index for North America (PD) as an alternative proxy for aggregate default risk. The bottom graph presents the time series of the corporate bond index spread, with the shaded areas indicating periods when Regime 2 prevails, i.e. the smoothed probability of being in Regime 2 is 0.5 or greater.

Figure A11a: 
Impulse of corporate bond spread to shocks during (low-volatility) Regime 1
– Fitch Ratings’ 1-Year-Ahead PD index (North America) as default risk proxy –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 1, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2. In this specification, the VIX is replaced by Fitch Ratings’ 1-Year-Ahead Probability of Default Index for North America (PD) as an alternative proxy for aggregate default risk. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering ILLIQUID → YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET is imposed. The shaded areas show the respective 99% bootstrap confidence interval calculated following Hall (1992).

Figure A11a:

Impulse of corporate bond spread to shocks during (low-volatility) Regime 1

– Fitch Ratings’ 1-Year-Ahead PD index (North America) as default risk proxy –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 1, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2. In this specification, the VIX is replaced by Fitch Ratings’ 1-Year-Ahead Probability of Default Index for North America (PD) as an alternative proxy for aggregate default risk. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering ILLIQUID → YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET is imposed. The shaded areas show the respective 99% bootstrap confidence interval calculated following Hall (1992).

Figure A11b: 
Impulse of corporate bond spread to shocks during (high-volatility) Regime 2
– Fitch Ratings’ 1-Year-Ahead PD index (North America) as default risk proxy –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 2, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2. In this specification, the VIX is replaced by Fitch Ratings’ 1-Year-Ahead Probability of Default Index for North America (PD) as an alternative proxy for aggregate default risk. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering ILLIQUID → YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET is imposed. The shaded areas show the respective 99% bootstrap confidence interval calculated following Hall (1992).

Figure A11b:

Impulse of corporate bond spread to shocks during (high-volatility) Regime 2

– Fitch Ratings’ 1-Year-Ahead PD index (North America) as default risk proxy –

Notes: This graph plots orthogonalized impulse response functions (IRF) of the corporate bond index spread SPREAD (in percentage points) to a one standard deviation shock to the respective variable of the regime-specific VAR system during Regime 2, based on the estimation of the two-state MS-VAR(2) model as specified in Equation (1) using the variables introduced in section 3.2. In this specification, the VIX is replaced by Fitch Ratings’ 1-Year-Ahead Probability of Default Index for North America (PD) as an alternative proxy for aggregate default risk. The structural innovations are identified using a triangular Cholesky factorization of the residuals’ regime-specific covariance matrix, for which the causal ordering ILLIQUID → YC_LEVEL → YC_SLOPE → VIX → SPREAD → STOCK_RET is imposed. The shaded areas show the respective 99% bootstrap confidence interval calculated following Hall (1992).

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Supplementary Material

The online version of this article offers supplementary material (https://doi.org/10.1515/jbnst-2020-0002).

Received: 2020-01-10
Accepted: 2020-10-07
Published Online: 2021-01-12
Published in Print: 2021-04-27

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