Specification Analysis of Structural Credit Risk Models

Huang, Jing-Zhi; Zhou, Hao

doi:10.2139/ssrn.1105640

Cited by 28 publications

(12 citation statements)

References 39 publications

(1 reference statement)

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“…We set α = 60%. Huang and Zhou (2008) find that in a sample of firms the average payout rate is 2.14%, and, more specifically, the average for BB-rated firms is 2.15% and for A-rated firms is 2.02%. Given the small variation across different ratings, we use δ = 2% throughout the paper.…”

Section: Debt Structurementioning

confidence: 86%

Rollover Risk and Credit Risk

Xiong

2011

SSRN Journal

101

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Our model shows that deterioration in debt market liquidity leads to an increase in not only the liquidity premium of corporate bonds but also credit risk. The latter effect originates from firms' debt rollover. When liquidity deterioration causes a firm to suffer losses in rolling over its maturing debt, equity holders bear the losses while maturing debt holders are paid in full. This conflict leads the firm to default at a higher fundamental threshold. Our model demonstrates an intricate interaction between the liquidity premium and default premium and highlights the role of short-term debt in exacerbating rollover risk.THE YIELD SPREAD OF a firm's bond relative to the risk-free interest rate directly determines the firm's debt financing cost, and is often referred to as its credit spread. It is widely recognized that the credit spread reflects not only a default premium determined by the firm's credit risk but also a liquidity premium due to illiquidity of the secondary debt market (e.g., Longstaff, Mithal, and Neis (2005) and Chen, Lesmond, and Wei (2007)). However, academics and policy makers tend to treat both the default premium and the liquidity premium as independent, and thus ignore interactions between them. The financial crisis of 2007 to 2008 demonstrates the importance of such an interactiondeterioration in debt market liquidity caused severe financing difficulties for many financial firms, which in turn exacerbated their credit risk.In this paper, we develop a theoretical model to analyze the interaction between debt market liquidity and credit risk through so-called rollover risk: when debt market liquidity deteriorates, firms face rollover losses from issuing new bonds to replace maturing bonds. To avoid default, equity holders need to bear the rollover losses, while maturing debt holders are paid in full. This

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Section: Debt Structurementioning

confidence: 86%

Rollover Risk and Credit Risk

Xiong

2011

SSRN Journal

101

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“…Using a maximum likelihood estimation procedure, they found that the asset process for both individual stocks and the indexes of the S&P-500 and the NASDAQ with jumps having double-exponential distribution performs better than the case with normally distributed jumps or without jumps. There are a number of works that study the double-exponential jump diffusion model; see Kou (2002), Kou and Wang (2003), (2004), Huang and Huang (2003), and Huang and Zhou (2008), to name only a few. Intuitively, jumps of the firm's value process are triggered if unexpected information or events are revealed, and there might be a variety of information.…”

Section: Introductionmentioning

confidence: 99%

Pricing the Zero-Coupon Bond and its Fair Premium Under a Structural Credit Risk Model with Jumps

Dong

Wang²,

2011

Journal of Applied Probability

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In this paper we consider a structural form credit risk model with jumps. We investigate the credit spread, the price, and the fair premium of the zero-coupon bond for the proposed model. The price and the fair premium of the bond are associated with the Laplace transform of default time and the firm's expected present market value at default. We give sufficient conditions under which the Laplace transform and the expected present market value of a firm at default are twice continuously differentiable. We derive closedform expressions for them when the jumps have a hyperexponential distribution. Using the closed-form expressions, we obtain numerical solutions for the default probability, the credit spread, and the fair premium of the bond.

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“…5 An alternative framework to the latent factor model employed in this paper is to use a structural model approach in which credit spreads are determined by a number of structural factors suggested by theory such as interest rates, leverage and asset volatility (Collin-Dufresne and Goldstein 2001; Leland and Toft 1996;Longstaff and Schwartz 1995). Structural models are widely used in credit risk modelling; yet empirical tests suggest that structural models typically cannot accurately explain credit spreads (Eom et al 2004;Huang and Zhou 2008). 6 Relatedly, the empirical literature finds that changes in credit spreads are even harder to explain by structural factors than levels Zhang et al 2009).…”

Section: Removing Common Shocksmentioning

confidence: 99%

Analyzing Credit Risk Transmission to the Non- Financial Sector in Europe: A Network Approach

Groß

Siklos

2019

SSRN Journal

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We use a factor model and elastic net shrinkage to model a high-dimensional network of European CDS spreads. Our empirical approach allows us to assess the joint transmission of bank and sovereign risk to the non-financial corporate sector. Our findings identify a sectoral clustering in the CDS network, where financial institutions are in the center and non-financial entities as well as sovereigns are grouped around the financial center. The network has a geographical component reflected in different patterns of real-sector risk transmission across countries. Our framework also provides dynamic estimates of risk transmission, a useful tool for systemic risk monitoring. AbstractWe use a factor model and elastic net shrinkage to model a high-dimensional network of European CDS spreads. Our empirical approach allows us to assess the joint transmission of bank and sovereign risk to the non-financial corporate sector. Our findings identify a sectoral clustering in the CDS network, where financial institutions are in the center and non-financial entities as well as sovereigns are grouped around the financial center. The network has a geographical component reflected in different patterns of real-sector risk transmission across countries. Our framework also provides dynamic estimates of risk transmission, a useful tool for systemic risk monitoring. JEL Classification: C32, C38, C55, F3, G01, G15

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Specification Analysis of Structural Credit Risk Models

Cited by 28 publications

References 39 publications

Rollover Risk and Credit Risk

Rollover Risk and Credit Risk

Pricing the Zero-Coupon Bond and its Fair Premium Under a Structural Credit Risk Model with Jumps

Analyzing Credit Risk Transmission to the Non- Financial Sector in Europe: A Network Approach

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