Homogeneous semi-Markov reliability models for credit risk management*

D’Amico, Guglielmo; Janssen, Jacques; Manca, Raimondo

doi:10.1007/s10203-005-0055-8

Cited by 47 publications

(29 citation statements)

References 18 publications

(20 reference statements)

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“…Several authors have discussed the use of Markov processes techniques in health. The author cites only some of them, i.e., (Jackson et al, 2003;D'Amico et al, 2005;Foucher et al, 2005;Barbu and Limnios, 2009). In this part, Homogeneous Semi-Markov model are described by using the notation of Foucher et al (2005).…”

Section: The Transition-specific Semi-markov Modelmentioning

confidence: 99%

Multi-State Models of Hiv/Aids by Homogeneous Semi-Markov Process

Dessie¹

2014

American Journal of Biostatistics

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Multi-state stochastic models are useful tools for studying complex dynamics such as chronic diseases. The purpose of this study is to determine factors associated with the progression between different stages of the disease and to model the progression of HIV/AIDS disease of an individual patient under ART follow-up using semi-Markov processes. A sample of 1456 patients has been taken from a hospital record at Amhara Referral Hospitals, Amhara Region, Ethiopia, who have been under ART follow up from June 2006 to August 2013. The states of disease progression adopted in the multi-state model were defined based on of the following CD4 cell counts: ≥500(SI); 200 to 499(SII); <200(SIII); and Death (D). The first three states are named as good. Female patients were 1.6 times more likely to move from state 2 to state 1 than those of male patients (adjusted HR = 1.60, CI = 1.02-2.49). Patients, who is not drug addicted, were 2.49 times more likely to move from state 3 to state 2 than those of drug addicted(adjusted HR = 2.67, CI = 1.52-4.68). Patients with tuberculosis were 2.67 times more likely to move from state 3 to state 4 than those with no tuberculosis (adjusted HR = 2.67, CI = 1.52-4.68). On the other hand, the probability of staying in same state until a given number of month decreases with increasing time. Multi-state modeling is a powerful approach for studying chronic diseases and estimating factors associated with transitions between each stage of progression. The major predictors of the intensity of transitions between different states of HIV/AIDS patients were gender, age, drug addicted and TB status. The dynamic nature of the AIDS progression is confirmed with particular findings that there is more likely to be in worse state than better one unless interventions are made.

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Section: The Transition-specific Semi-markov Modelmentioning

confidence: 99%

Multi-State Models of Hiv/Aids by Homogeneous Semi-Markov Process

Dessie¹

2014

American Journal of Biostatistics

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“…This difference is explained only by the different starting time s. This means that really the non-homogeneity play an important role in the description of the rating dynamics. Additional results and indicators on the semi-Markov rating migration model can be applied as suggested in [8,10,11].…”

Section: Resultsmentioning

confidence: 99%

“…In [7] is presented the way to apply non homogeneous semi-Markov models to the credit risk migration problem. In that paper the credit risk problem is seen as a reliability model.…”

Section: The Dtnhsmrwp Spread Rating Modelmentioning

confidence: 99%

A Non-Homogeneous Semi-Markov Reward Model for the Credit Spread Computation

D’Amico

Janssen²,

Manca

2011

Int. J. Theor. Appl. Finan.

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In this paper, we present a model to describe the evolution of the yield spread by considering the rating evaluation as the determinant of credit spreads. The underlying rating migration process is assumed to be a non-homogeneous discrete time semi-Markov process. We calculate the total sum of mean basis points paid within any given time interval. From this information we show how it is possible to extract the time evolution of expected interest rates and discount factors.

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“…2 the states and transitions are presented; virtual transitions are allowed, meaning that, after a doctor visit, it is possible that the insured person remains in the same state. The State De is the absorbing state, this means that in the last column of the evolution equation solution there will be the distribution function of the mortality probabilities given the starting state (see D'Amico et al (2005)). …”

Section: The Disability Modelmentioning

confidence: 99%