Dynamic Dependence in Corporate Credit

Christoffersen, Peter; Jacobs, Kris; Jin, Xisong; Langlois, Hugues

doi:10.2139/ssrn.2314027

Cited by 25 publications

(16 citation statements)

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“…Researchers identified mechanisms that link CDS spreads through the expected future cash flows of reference firms and showed that comovement was largely related to observed economic variables (see Jorion and Zhang (2007), (2009), Ericsson et al (2009), Acharya et al (2015), Berndt et al (2008), Kim, Loretan, and Remolona (2010), and Azizpour, Giesecke, and Kim (2011)), whereas others argued that a significant fraction of correlations could be attributed to contagion (see Pu and Zhao (2012)). More recently, Christoffersen, Jacobs, Jin, and Langlois (2016) use a dynamic copula approach to illustrate a loss in diversification benefit after 2008. However, they provide little insight into the economic mechanism.…”

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confidence: 99%

What Drives the Commonality between Credit Default Swap Spread Changes?

Anderson¹

2017

J. Financ. Quant. Anal.

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This paper documents an increase in the comovement between credit default swap (CDS) spread changes during the 2007-2009 crisis and investigates the source of that increase. One possible explanation is that comovement increased because fundamental values became more correlated. However, I find that changes in fundamentals account for only 23% of the increase in covariance. The remaining increase is attributed to changes in liquidity and the market price of default risk. In contrast, counterparty risk played an insignificant role. Although both contributed, the increase in covariance was driven more by variation in exposures than factor variance-covariance.

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confidence: 99%

What Drives the Commonality between Credit Default Swap Spread Changes?

Anderson¹

2017

J. Financ. Quant. Anal.

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“…This implies that there are gains to be had by modelling linear dependence, as captured by covariances, using high frequency data. Second, copula methods have been shown to be useful for constructing flexible distribution models in high dimensions, see Christoffersen et al (2013), Oh and Patton (2016) and Creal and Tsay (2014). These two findings naturally lead to the question of whether high frequency data and copula methods can be combined to improve the modelling and forecasting of highdimensional return distributions.…”

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confidence: 99%

High-dimensional copula-based distributions with mixed frequency data

Patton

2016

Journal of Econometrics

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a r t i c l e i n f oArticle history: Available online xxxx JEL classification: C32 C51 C58 Keywords: High frequency data Forecasting Composite likelihood Nonlinear dependence a b s t r a c tThis paper proposes a new model for high-dimensional distributions of asset returns that utilizes mixed frequency data and copulas. The dependence between returns is decomposed into linear and nonlinear components, enabling the use of high frequency data to accurately forecast linear dependence, and a new class of copulas designed to capture nonlinear dependence among the resulting uncorrelated, low frequency, residuals. Estimation of the new class of copulas is conducted using composite likelihood, facilitating applications involving hundreds of variables. In-and out-of-sample tests confirm the superiority of the proposed models applied to daily returns on constituents of the S&P 100 index.

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“…This implies that there are gains to be had by modelling linear dependence, as captured by covariances, using high frequency data. Second, copula methods have been shown to be useful for constructing ‡exible distribution models in high dimensions, see Christo¤ersen, et al (2013), Oh and Patton (2013) and Creal and Tsay (2014). These two …ndings naturally lead to the question of whether high frequency data and copula methods can be combined to improve the modelling and forecasting of high-dimensional return distributions.…”

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confidence: 99%

“…9 To the best of our knowledge, combining the cKLIC with Vuong (1989) or Rivers and Vuong (2002) tests is new to the literature.…”

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High-Dimensional Copula-Based Distributions with Mixed Frequency Data

Hwan

Patton

2015

FEDS

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This paper proposes a new model for high-dimensional distributions of asset returns that utilizes mixed frequency data and copulas. The dependence between returns is decomposed into linear and nonlinear components, enabling the use of high frequency data to accurately forecast linear dependence, and a new class of copulas designed to capture nonlinear dependence among the resulting uncorrelated, low frequency, residuals. Estimation of the new class of copulas is conducted using composite likelihood, facilitating applications involving hundreds of variables. In-and out-ofsample tests con…rm the superiority of the proposed models applied to daily returns on constituents of the S&P 100 index.

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Dynamic Dependence in Corporate Credit

Cited by 25 publications

References 50 publications

What Drives the Commonality between Credit Default Swap Spread Changes?

What Drives the Commonality between Credit Default Swap Spread Changes?

High-dimensional copula-based distributions with mixed frequency data

High-Dimensional Copula-Based Distributions with Mixed Frequency Data

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