Multiperiod Corporate Default Prediction - A Forward Intensity Approach

Duan, Jin‐Chuan; Sun, Jie; Wang, Tao

doi:10.2139/ssrn.1791222

Cited by 70 publications

(98 citation statements)

References 31 publications

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“…This intensity determines the rate of occurrence of an event within the period of time (t, t + dt] given information set i, t available at time t. Quite recently, Duan et al (2012) introduced the notion of forward intensity which to some extent plays a similar role to forward rate in finance. However, as stated in Duan et al (2012) the forward intensity can only be calculated with the exact knowledge of the underlying Markov process. Motivated by this work, we discuss forward intensity defined for any s ≥ t by λ i (t, s) ds…”

Section: Forward Intensitymentioning

confidence: 99%

Distributional Properties of the Mixture of Continuous-Time Absorbing Markov Chains Moving at Different Speeds

Surya

2018

Stochastic Systems

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Abstract. This paper analyses the mixture of two continuous-time Markov chains with absorption on the same state space moving at different speeds, where the mixture occurs at a random time. Variety of associated distributional properties of the Markov mixture process are discussed, for example the transition matrix, the distribution of its lifetime and the corresponding forward intensity. Identities are given explicitly in terms of the Bayesian updates of switching probability and the intensity matrices of the underlying Markov chains despite the fact that the mixture process is not Markovian. They form nonstationary functions of time and duration and have two appealing features: the ability to capture heterogeneity and path dependence when conditioning on the available information (either full or partial) about the past history of the process until current time. In particular, the unconditional lifetime distribution forms a generalized mixture of phase-type distributions. The distribution has dense and closure properties under finite convex mixtures and finite convolutions. When the underlying Markov chains move at the same speed, in which case the mixture process reduces to a simple Markov chain, the heterogeneity and path dependence are removed, and the lifetime has the usual phase-type distribution, Neuts [Neuts MF (1975) Probability distributions of phase-type.

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Section: Forward Intensitymentioning

confidence: 99%

Distributional Properties of the Mixture of Continuous-Time Absorbing Markov Chains Moving at Different Speeds

Surya

2018

Stochastic Systems

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“…Duffie et al (2007) first proposed a doubly stochastic Poisson model with time-varying covariates and then forecast the evolution of covariate processes using Gaussian panel vector autoregressions. The model was further developed by Duan et al (2012), who applied a pseudo-likelihood method to derive the forward intensity rate of the doubly stochastic Poisson processes at different time horizons.…”

Section: The Modelmentioning

confidence: 99%

“…We use the pseudo-likelihood function derived by Duan (2012) to estimate the forward default intensity. The details of the derivation of its large sample properties can be found Appendix A in Duan's (2012) paper.…”

Section: The Modelmentioning

confidence: 99%

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Analysing the Determinants of Credit Risk for General Insurance Firms in the UK

2016

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThis paper estimates a reduced-form model to assess the credit risk of General Insurance (GI) non-life firms in the UK. Compared to earlier studies, it uses a much larger sample including 30 years of data for 515 firms, and also considers a much wider set of possible determinants of credit risk. The empirical results suggest that macroeconomic and firm-specific factors both play important roles. Other key findings are the following: credit risk varies across firms depending on their business lines; there is default clustering in the GI industry; different reinsurance levels also affect the credit risk of insurance firms. The implications of these findings for regulators of GI firms under the coming Solvency II are discussed.

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“…The model is a doubly stochastic Poisson intensity model, which is extended as predictive intensity model in Duan et al (2011).…”

Section: Introductionmentioning

confidence: 99%

Credit Risk Analysis on Euro Government Bonds-Term Structures of Default Probabilities

Kariya

Yamamura

Tanokura

et al. 2015

Asia-Pac Financ Markets

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In this paper, we make a comprehensive credit risk analysis on government bonds (GBs) of Germany, France, Italy, Spain and Greece over the period 2007.4-2012.3, where interest rate (IR) differential, GB price differential, default probability (DP) and credit default swap (CDS) are considered. First, applying the GBpricing model in Kariya (Quantitative methods for portfolio analysis: MTV approach. Springer, Berlin, 1993) to these GB prices, we derive the term structures of interest rates (TSIRs) and discuss on the Maastricht convergence condition for the IR-differentials among these states relative to the German TSIRs and make some observations on some divergent tendencies. The results are associated with the business cycles and budgetary condition of each state. In the second part, to substantiate this viewpoint, we first make credit risk price spread analysis on price differentials and derive the term structures of default probabilities (TSDPs) of the French, Italian, Spanish and Greek GBs relative to the German GBs, where the corporate bond (CB) model proposed in Kariya

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Multiperiod Corporate Default Prediction - A Forward Intensity Approach

Cited by 70 publications

References 31 publications

Distributional Properties of the Mixture of Continuous-Time Absorbing Markov Chains Moving at Different Speeds

Distributional Properties of the Mixture of Continuous-Time Absorbing Markov Chains Moving at Different Speeds

Analysing the Determinants of Credit Risk for General Insurance Firms in the UK

Credit Risk Analysis on Euro Government Bonds-Term Structures of Default Probabilities

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