Credit Benchmarking, Risk Premium Adjustment Factors, and Credit Solvency Capital Requirements. A Recovery-Based Approach

AbstractWe define and study in this work a simple model designed for managing long-term market risk of financial institutions with long-term commitments. It allows the assessment of solvency capital requirements and the allocation of risk budgets. This model allows one to avoid over-assessment of solvency capital requirements specifically after market disruptions. It relies on a dampener component in charge of refining risk assessment after market failures. Rather than aiming at a realistic and thus complex description of equity prices movements, this model concentrates on minimal features enabling accurate computation of capital requirements. It is defined both in a discrete and continuous fashion. In the latter case, we prove the existence, uniqueness and stability of the solution of the stochastic functional differential equation that specifies the model. One difficulty is that the proposed underlying stochastic process has neither stationary nor independent increments. We are however able to perform statistical analyses in view of its validation. Numerical experiments show that our model outperforms more elaborate ones of common use as far as medium-term (between 6 months and 5 years) risk assessment is concerned.

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On longevity risk securitization and solvency capital requirements in life annuities

Mathematical and Statistical Methods for Actuarial Sciences and Finance ◽

10.1007/978-88-470-2342-0_30 ◽

2012 ◽

pp. 255-262

Author(s):

Susanna Levantesi ◽

Massimiliano Menzietti ◽

Tiziana Torri

Keyword(s):

Capital Requirements ◽

Longevity Risk ◽

Solvency Capital ◽

Life Annuities

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MODELING THE MORTALITY TREND UNDER MODERN SOLVENCY REGIMES

Astin Bulletin ◽

10.1017/asb.2013.24 ◽

2013 ◽

Vol 44 (1) ◽

pp. 1-38 ◽

Cited By ~ 26

Author(s):

Matthias Börger ◽

Daniel Fleischer ◽

Nikita Kuksin

Keyword(s):

Solvency Ii ◽

Capital Requirements ◽

Longevity Risk ◽

Mortality Trend ◽

Swiss Solvency Test ◽

Solvency Capital ◽

Simultaneous Modeling ◽

One Year ◽

The One ◽

Solvency Regimes

AbstractStochastic modeling of mortality/longevity risks is necessary for internal models of (re)insurers under the new solvency regimes, such as Solvency II and the Swiss Solvency Test. In this paper, we propose a mortality model which fulfills all requirements imposed by these regimes. We show how the model can be calibrated and applied to the simultaneous modeling of both mortality and longevity risk for several populations. The main contribution of this paper is a stochastic trend component which explicitly models changes in the long-term mortality trend assumption over time. This allows to quantify mortality and longevity risk over the one-year time horizon prescribed by the solvency regimes without relying on nested simulations. We illustrate the practical ability of our model by calculating solvency capital requirements for some example portfolios, and we compare these capital requirements with those from the Solvency II standard formula.

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OPTIMAL ASSET ALLOCATION IN LIFE INSURANCE: THE IMPACT OF REGULATION

Astin Bulletin ◽

10.1017/asb.2016.12 ◽

2016 ◽

Vol 46 (3) ◽

pp. 605-626 ◽

Cited By ~ 10

Author(s):

An Chen ◽

Peter Hieber

Keyword(s):

Life Insurance ◽

Asset Allocation ◽

Regulatory Policy ◽

Capital Requirements ◽

Insurance Company ◽

Traffic Light ◽

Optimal Insurance ◽

Insurance Contracts ◽

Solvency Capital ◽

The Impact

AbstractIn a typical equity-linked life insurance contract, the insurance company is entitled to a share of return surpluses as compensation for the return guarantee granted to the policyholders. The set of possible contract terms might, however, be restricted by a regulatory default constraint — a fact that can force the two parties to initiate sub-optimal insurance contracts. We show that this effect can be mitigated if regulatory policy is more flexible. We suggest that the regulator implement a traffic light system where companies are forced to reduce the riskiness of their asset allocation in distress. In a utility-based framework, we show that the introduction of such a system can increase the benefits of the policyholder without deteriorating the benefits of the insurance company. At the same time, default probabilities (and thus solvency capital requirements) can be reduced.

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Longevity trend risk over limited time horizons

Annals of Actuarial Science ◽

10.1017/s174849952000007x ◽

2020 ◽

Vol 14 (2) ◽

pp. 262-277

Author(s):

Stephen J. Richards ◽

Iain D. Currie ◽

Torsten Kleinow ◽

Gavin P. Ritchie

Keyword(s):

Finite Time ◽

Value At Risk ◽

Time Window ◽

Capital Requirements ◽

Multiple Models ◽

Limited Time ◽

Risk Capital ◽

Model Risk ◽

Time Horizons ◽

Solvency Capital

AbstractWe consider various aspects of longevity trend risk viewed through the prism of a finite time window. We show the broad equivalence of value-at-risk (VaR) capital requirements at a p-value of 99.5% to conditional tail expectations (CTEs) at 99%. We also show how deferred annuities have higher risk, which can require double the solvency capital of equivalently aged immediate anuities. However, results vary considerably with the choice of model and so longevity trend-risk capital can only be determined through consideration of multiple models to inform actuarial judgement. This model risk is even starker when trying to value longevity derivatives. We briefly discuss the importance of using smoothed models and describe two methods to considerably shorten VaR and CTE run times.

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Risk Measures and Multivariate Extensions of Breiman's Theorem

Journal of Applied Probability ◽

10.1239/jap/1339878792 ◽

2012 ◽

Vol 49 (2) ◽

pp. 364-384 ◽

Cited By ~ 13

Author(s):

Anne-Laure Fougeres ◽

Cecile Mercadier

Keyword(s):

Ruin Probability ◽

Value At Risk ◽

Risk Measures ◽

Risk Measure ◽

Capital Requirements ◽

Regulatory Capital ◽

Tail Probabilities ◽

Solvency Capital ◽

Finite Time Horizon ◽

Standard Risk

The modeling of insurance risks has received an increasing amount of attention because of solvency capital requirements. The ruin probability has become a standard risk measure to assess regulatory capital. In this paper we focus on discrete-time models for the finite time horizon. Several results are available in the literature to calibrate the ruin probability by means of the sum of the tail probabilities of individual claim amounts. The aim of this work is to obtain asymptotics for such probabilities under multivariate regular variation and, more precisely, to derive them from extensions of Breiman's theorem. We thus present new situations where the ruin probability admits computable equivalents. We also derive asymptotics for the value at risk.

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Operational risk quantification and modelling within Romanian insurance industry

Proceedings of the International Conference on Business Excellence ◽

10.1515/picbe-2017-0068 ◽

2017 ◽

Vol 11 (1) ◽

pp. 637-648

Author(s):

Răzvan Tudor ◽

Dumitru Badea

Keyword(s):

Standard Model ◽

Insurance Industry ◽

Operational Risk ◽

Solvency Ii ◽

Capital Requirements ◽

Capital Requirement ◽

Risk Capital ◽

Operational Risks ◽

The Standard Model ◽

Solvency Capital

Abstract This paper aims at covering and describing the shortcomings of various models used to quantify and model the operational risk within insurance industry with a particular focus on Romanian specific regulation: Norm 6/2015 concerning the operational risk issued by IT systems. While most of the local insurers are focusing on implementing the standard model to compute the Operational Risk solvency capital required, the local regulator has issued a local norm that requires to identify and assess the IT based operational risks from an ISO 27001 perspective. The challenges raised by the correlations assumed in the Standard model are substantially increased by this new regulation that requires only the identification and quantification of the IT operational risks. The solvency capital requirement stipulated by the implementation of Solvency II doesn’t recommend a model or formula on how to integrate the newly identified risks in the Operational Risk capital requirements. In this context we are going to assess the academic and practitioner’s understanding in what concerns: The Frequency-Severity approach, Bayesian estimation techniques, Scenario Analysis and Risk Accounting based on risk units, and how they could support the modelling of operational risk that are IT based. Developing an internal model only for the operational risk capital requirement proved to be, so far, costly and not necessarily beneficial for the local insurers. As the IT component will play a key role in the future of the insurance industry, the result of this analysis will provide a specific approach in operational risk modelling that can be implemented in the context of Solvency II, in a particular situation when (internal or external) operational risk databases are scarce or not available.

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