Liquidity tail risk and credit default swap spreads

Irresberger, Felix; Weiß, Gregor N. F.; Gabrysch, Janet; Gabrysch, Sandra

doi:10.1016/j.ejor.2018.02.030

Cited by 13 publications

(6 citation statements)

References 78 publications

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“…The utilization of six time-varying copula models allows us to compare dynamic dependence patterns between sample countries. Recent papers in quantitative and mathematical finance have shown that copula models are flexible enough for modeling nonlinear dependence and tail risk (e.g., Grundke and Polle, 2012;Al Janabi et al, 2017;Irresberger et al, 2018). When fitting copula functions, we particularly suggest a new forcing variable to better model the dependence parameter of the 90-degree rotated Clayton and 270-degree rotated Clayton copulas.…”

Section: Introductionmentioning

confidence: 99%

“…Recent papers in quantitative and mathematical finance have shown that copula models are flexible enough for modelling nonlinear dependence and tail risk (e.g. Al Janabi, Arreola Hernandez, Berger, & Nguyen, 2017; Grundke & Polle, 2012; Irresberger, Wei, & Gabrysch, 2018). When fitting copula functions, we particularly suggest a special forcing variable to better model the dependence parameter of the 90° rotated Clayton and 270° rotated Clayton copulas.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic dependence and extreme risk comovement: The case of oil prices and exchange rates

Bing-yue

Nguyen

et al. 2020

Int. J Fin Econ

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This article aims at investigating the dynamic dependence and extreme risk comovement of oil price and exchange rates in seven oil‐importing and seven oil‐exporting countries. For this purpose, we use six representative time‐varying copula models and four kinds of tail dependences to assess the downside and upside conditional value‐at‐risk measures (CoVaRs). Our findings indicate that the dependence of crude oil returns and exchange rates is negative for most pairs, that is, the rise (fall) in oil prices was accompanied by the appreciation (depreciation) of foreign currency against the US dollar. The oil price–exchange rate dependences in oil exporters is slightly larger than in oil importers, even though the dependence is weak in general. More interestingly, we find strong evidence of significant risk comovement between crude oil returns and exchange rates through the analysis of lower–upper and upper–lower tail dependence as well as downside and upside CoVaRs. This comovement particularly showed asymmetric effects. Finally, we sum up some implications for investors, policymakers and exchange rate regulators from a holistic perspective of empirical findings.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic dependence and extreme risk comovement: The case of oil prices and exchange rates

Bing-yue

Nguyen

et al. 2020

Int. J Fin Econ

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“…More recently, Escobar et al (2012) have offered a multivariate extension of the CreditGrades model under the assumption of stochastic variance and correlation among the companies' assets. Credit Default Swap is a topic of interest in much recent operational research literature (Guarin et al, 2011;Tomohiro, 2014;Cont and Minca, 2016;Chalamandaris and Vlachogiannakis, 2018;Koutmos, 2018;Irresberger et al, 2018), but in practice, models for traded CDS spreads tend not to be popular among portfolio managers. Although the aforementioned models provide very close approximations (e.g.…”

Section: Introductionmentioning

confidence: 99%

Credit spread approximation and improvement using random forest regression

Mercadier

Lardy²

2019

European Journal of Operational Research

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Credit Default Swap (CDS) levels provide a market appreciation of companies' default risk. These derivatives are not always available, creating a need for CDS approximations. This paper offers a simple, global and transparent CDS structural approximation, which contrasts with more complex and proprietary approximations currently in use. This Equity-to-Credit formula (E2C), inspired by CreditGrades, obtains better CDS approximations, according to empirical analyses based on a large sample spanning 2016-2018. A random forest regression run with this E2C formula and selected additional financial data results in an 87.3% out-of-sample accuracy in CDS approximations. The transparency property of this algorithm confirms the predominance of the E2C estimate, and the impact of companies' debt rating and size, in predicting their CDS.

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“…In actuarial science, for example, Eckert and Gatzert (2018) model extreme losses using copulas to account for tail dependence ("ruin probability"). Coefficients of (usually lower) tail dependence have also been used in asset pricing by Rodriguez (2007); Okimoto (2008); Garcia and Tsafack (2011); Meine et al (2016); Chabi-Yo et al (2018); Irresberger et al (2018) to study the risk premia for crash events. Finally, at the macro level, De Jonghe (2010) and Patton (2017, 2018) employ measures of tail dependence to estimate the probability of a crash in the financial system.…”

Section: Introduction and Resultsmentioning

confidence: 99%