One-Year Change Methodologies for Fixed-Sum Insurance Contracts

Dacorogna, Michel M.; Ferriero, Alessandro; Krief, David

doi:10.3390/risks6030075

Cited by 3 publications

(7 citation statements)

References 8 publications

(14 reference statements)

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“…There are many ways one can think of testing this. We present here a method developed recently (see Dacorogna et al (2018a)) that can also be applied for other validation procedures. It consists of designing simple stochastic models to reach the ultimate claim value that can then be used to simulate sample paths to test the various methods for computing the one-year change risk.…”

Section: Testing the One-year Change Of Pandc Reservesmentioning

confidence: 99%

“…Here we present results, obtained in Dacorogna et al (2018a), for two simple stochastic processes to reach the ultimate risk and for which we have derived explicit formulae:…”

Section: Testing the One-year Change Of Pandc Reservesmentioning

confidence: 99%

“…In the case of internal models, a good model would be a model that can accurately predict the future risk of the company (for an interesting discussion on this, see Embrechts (2017)). Since the internal models are designed to evaluate the risk over one year (see Dacorogna et al (2018a)), the prediction horizon for the risk is thus also one year. The threshold chosen for the risk measures 99%, and 99.5% makes it impossible to directly test the predicted risk statistically, since there will never be enough relevant data at those thresholds.…”

Section: Introductionmentioning

confidence: 99%

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How to Gain Confidence in the Results of Internal Risk Models? Approaches and Techniques for Validation

Dacorogna¹

2023

Risks

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The development of risk models for managing portfolios of financial institutions and insurance companies requires, both from the regulatory and management points of view, a strong validation of the quality of the results provided by internal risk models. In Solvency II, for instance, regulators ask for independent validation reports from companies who apply for the approval of their internal models. We analyze here various ways to enable management and regulators to gain confidence in the quality of models. It all starts by ensuring a good calibration of the risk models and the dependencies between the various risk drivers. Then, by applying stress tests to the model and various empirical analyses, in particular the probability integral transform, we can build a full and credible framework to validate risk models.

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Section: Testing the One-year Change Of Pandc Reservesmentioning

confidence: 99%

“…Here we present results, obtained in Dacorogna et al (2018a), for two simple stochastic processes to reach the ultimate risk and for which we have derived explicit formulae:…”

Section: Testing the One-year Change Of Pandc Reservesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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How to Gain Confidence in the Results of Internal Risk Models? Approaches and Techniques for Validation

Dacorogna¹

2023

Risks

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“…In order to resolve the issue of a short time period, ruin theory and geometric Brownian motion are proposed. In order to resolve deterministic and time issues, the following methods are proposed: bootstrapping (Ohlsson and Lauzeningks, 2009) and stochastic reserving methods, including a Robust Chain Ladder (Peremans et al, 2017), the Fröhlich model (Fröhlich and Weng, 2018), a neural network approach (Hejazi and Jackson, 2017), and a COT method (Dacorogna et al, 2018) developed by the SCOR insurance group.…”

Section: Literature Reviewmentioning

confidence: 99%

Alternative Capital Requirement for Insurers: Possibilities and Issues

Zariņa

Voronova

Pettere

2021

IJEBR

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Solvency II framework regulates how much capital the insurance companies of the European Union must hold. Although the framework lasts four years, there is still place for improvements considering experience. The goals of the research are to propose an alternative capital model methodology using copulas for reserve risk and to show the case study of potential capital shift impact. To conduct the research, the authors have used the extensive literature review, analytical methods and modelling. Research scope is non-life insurance companies under the Solvency II framework with a focus on reserve risk. The research will help avoid that alternative models are only a modern risk management tool and add risk management reality. Higher capital surplus can be achieved if a copula approach is used for risk aggregation in the Baltic non-life market. The Baltic market does not use alternative capital requirement and internal models. Using an alternative model is the right insurer's approach in modern risk management.

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“…This distribution is derived via Bayes theorem by combining a prior, representative of external knowledge, and observations. To this extent, we need a data structure that eases the procedure 1 In this framework, the dynamics of the one-year change in P&C insurance reserves estimation has been also studied in Dacorogna et al (2018) by analyzing the process that leads to the ultimate risk in the case of "fixed-sum" insurance contracts.…”

Section: The Correlated Chain Laddermentioning

confidence: 99%

A Bayesian Internal Model for Reserve Risk: An Extension of the Correlated Chain Ladder

Ercole

Clemente

2020

Risks

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The goal of this paper was to exploit the Bayesian approach and MCMC procedures to structure an internal model to quantify the reserve risk of a non-life insurer under Solvency II regulation. To this aim, we provide an extension of the Correlated Chain Ladder (CCL) model to the one-year time horizon. In this way, we obtain the predictive distribution of the next year obligations and we are able to assess a capital requirement compliant with Solvency II framework. Numerical results compare the one-year CCL with other traditional approaches, such as Re-Reserving and the Merz and Wüthrich formula. One-year CCL proves to be a legitimate alternative, providing values comparable with the more traditional approaches and more robust and accurate risk estimations, that embed external knowledge not present in the data and allow for a more precise and tailored representation of the risk profile of the insurer.

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One-Year Change Methodologies for Fixed-Sum Insurance Contracts

Cited by 3 publications

References 8 publications

How to Gain Confidence in the Results of Internal Risk Models? Approaches and Techniques for Validation

How to Gain Confidence in the Results of Internal Risk Models? Approaches and Techniques for Validation

Alternative Capital Requirement for Insurers: Possibilities and Issues

A Bayesian Internal Model for Reserve Risk: An Extension of the Correlated Chain Ladder

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