Calibrating the CreditRisk+ Model at Different Time Scales and in Presence of Temporal Autocorrelation †

Giacomelli, Jacopo; Passalacqua, L.

doi:10.3390/math9141679

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…, where C ij is the credit limit granted on the i-th buyer to the j-th insured seller. Further discussions on credit insurance policy pricing are available in [23,27,28,42,43].…”

Section: Elements Of Credit Insurancementioning

confidence: 99%

The Rating Scale Paradox: An Application to the Solvency 2 Framework

Giacomelli

2023

Standards

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This work aims to identify the optimal rating scale for the rating system used by a credit insurance company subjected to the Solvency 2 regulatory framework. To do so, we apply and further develop a previously published result concerning the rating scale properties. The partition underlying a given rating scale must satisfy two needs of the rating model user: efficient information synthesis and stable semantics. Those needs cannot be addressed together in general. Nonetheless, it is possible to specify the partition as a linear combination of the two choices that meet one requirement each. We numerically show that, in general, the optimal combination is nontrivial under realistic assumptions and is mainly driven by the target return fixed by the company’s stakeholders and the debtors’ probability of default distribution.

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“…, where C ij is the credit limit granted on the i-th buyer to the j-th insured seller. Further discussions on credit insurance policy pricing are available in [23,27,28,42,43].…”

Section: Elements Of Credit Insurancementioning

confidence: 99%

The Rating Scale Paradox: An Application to the Solvency 2 Framework

Giacomelli

2023

Standards

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“…We consider the model's generalization recently proposed in [32], which has also been applied to credit and suretyship insurance [33]. The advantage of this choice is the possibility to calibrate the model by using the quarterly time series available and using it to estimate both the bid bond claim probabilities p 01 (t, h(i)) and the corresponding performance bond claim probabilities p 12 (t, h(i)), where h = 1, .…”

Section: Simulation Of Tenders From a Surety's Perspectivementioning

confidence: 99%

“…According to [32], the claim event in CreditRisk + framework can be modeled as Y i (t, t ) ∼ Bernoulli(p i (t, t )), where the parameter p i has an exponential dependency on the latent factors. Namely, under our set of assumptions, it holds that…”

Section: Simulation Of Tenders From a Surety's Perspectivementioning

confidence: 99%

“…Let us consider the time series {PL t ; NPL t } sg available in [29], from the first quarter of 2016 to the first quarter of 2021, to calibrate the CreditRisk + model. The generalized covariance estimator defined in [32], the decomposition technique introduced in [34], and the standard regularization technique described in [35] return the result in Table 2.…”

Section: Simulation Of Tenders From a Surety's Perspectivementioning

confidence: 99%

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Unsustainability Risk of Bid Bonds in Public Tenders

Giacomelli

Passalacqua

2021

Mathematics

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Public works contracts are commonly priced and awarded through a tender process. Each bidder joining the tender must underwrite a bid bond that guarantees their fitness as contractors in case of a win. The winning contractor also needs to underwrite a performance bond before entering the contract to protect the procuring entity against the performance risk arising during the execution phase. This study addresses the case when sureties refuse to issue the performance bond, despite having issued a bid bond to the same subject. A creditworthiness variation of the contractor during the tender or an excessive discount of the contract’s price may lead to this outcome. In that case, all the subjects involved are damaged. The surety who issued the bid bond has to indemnify the procuring entity. The contract award is nullified, which is financially harmful to both the contractor and the procuring entity. We show that sureties adopting a forward-looking risk appetite framework may prevent the demand for unsustainable performance bonds instead of addressing it by rejecting the bidders’ requests. The Solvency II regulatory framework, the Italian bidding law, and actual historical data available from the Italian construction sector are considered to specify a simplified model. The probability of unsustainable tender outcomes is numerically estimated by the model, together with the mitigating impact of a surety’s proper strategy.

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Improved Precision in Calibrating CreditRisk$${^+}$$ Model for Credit Insurance Applications

Giacomelli¹,

Passalacqua

2021

Mathematical and Statistical Methods for Actuarial Sciences and Finance

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Calibrating the CreditRisk+ Model at Different Time Scales and in Presence of Temporal Autocorrelation †

Cited by 7 publications

References 16 publications

The Rating Scale Paradox: An Application to the Solvency 2 Framework

The Rating Scale Paradox: An Application to the Solvency 2 Framework

Unsustainability Risk of Bid Bonds in Public Tenders

Improved Precision in Calibrating CreditRisk$${^+}$$ Model for Credit Insurance Applications

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