scholarly journals Metodología financiera de gestión y cuantificación de riesgos de las entidades aseguradoras

2013 ◽  
pp. 81
Author(s):  
Rafael Hernández Barros
Keyword(s):  
Value At Risk ◽  
Financial Risk ◽  
Stress Testing ◽  
Financial Models ◽  
Insurance Industry ◽  
Solvency Ii ◽  
Capital Requirements ◽  
Value Theory ◽  
Different Types ◽  
As Stress

El artículo describe las diferentes metodologías financieras de gestión integral de riesgos, detallando tanto aquellas utilizadas tradicionalmente en el sector asegurador para tarificar ycalcular las provisiones, y que ahora están siendo utilizadas para calcular la solvencia y los requerimientos de capital, como los modelos financieros más avanzados, tales como los modelosde “stress testing”, utilizados para analizar lo que podría ocurrir en determinados escenarios; la técnica de modelización del valor en riesgo (VaR), para calcular la pérdida máxima posible dentrode un periodo de tiempo y para un determinado nivel de probabilidad; la teoría del valor extremo, que se centra en el estudio de los extremos de la distribución de pérdidas y ganancias esperadas,tratando de estimar las pérdidas máximas que pueden producirse; y la aplicación de cópulas, para incorporar la dependencia entre diferentes tipos de riesgo. Supone también una aproximación aSolvencia II y a las nuevas exigencias de cuantificación del capital que trae consigo esta nueva legislación europea del sector asegurador.<br /><br />The article describes the different methodologies of financial risk management, featuring both those traditionally used in the insurance industry to estimate insurance, that they are now being used to calculate the solvency and capital requirements, as the more advances financial models as "stress testing", used to analyze what might happen in certain scenarios; the modeling technique of value at risk (VaR), to estimate the maximum possible loss within a period of time and for a certain level of probability; the extreme value theory, which focuses on the study of the ends of the expected losses and income distribution, trying to estimate the maximum losses that may occur; and the application of copulas to incorporate the dependence between different types of risk. It also implies an approach to Solvency II and to the new capital requirements for quantifying capital that brings this new insurance European legislation.

scholarly journals Metodología financiera de gestión y cuantificación de riesgos de las entidades aseguradoras

2013 ◽  
pp. 81
Author(s):  
Rafael Hernández Barros
Keyword(s):  
Value At Risk ◽  
Financial Risk ◽  
Stress Testing ◽  
Financial Models ◽  
Insurance Industry ◽  
Solvency Ii ◽  
Capital Requirements ◽  
Value Theory ◽  
Different Types ◽  
As Stress

El artículo describe las diferentes metodologías financieras de gestión integral de riesgos, detallando tanto aquellas utilizadas tradicionalmente en el sector asegurador para tarificar ycalcular las provisiones, y que ahora están siendo utilizadas para calcular la solvencia y los requerimientos de capital, como los modelos financieros más avanzados, tales como los modelosde “stress testing”, utilizados para analizar lo que podría ocurrir en determinados escenarios; la técnica de modelización del valor en riesgo (VaR), para calcular la pérdida máxima posible dentrode un periodo de tiempo y para un determinado nivel de probabilidad; la teoría del valor extremo, que se centra en el estudio de los extremos de la distribución de pérdidas y ganancias esperadas,tratando de estimar las pérdidas máximas que pueden producirse; y la aplicación de cópulas, para incorporar la dependencia entre diferentes tipos de riesgo. Supone también una aproximación aSolvencia II y a las nuevas exigencias de cuantificación del capital que trae consigo esta nueva legislación europea del sector asegurador.<br /><br />The article describes the different methodologies of financial risk management, featuring both those traditionally used in the insurance industry to estimate insurance, that they are now being used to calculate the solvency and capital requirements, as the more advances financial models as "stress testing", used to analyze what might happen in certain scenarios; the modeling technique of value at risk (VaR), to estimate the maximum possible loss within a period of time and for a certain level of probability; the extreme value theory, which focuses on the study of the ends of the expected losses and income distribution, trying to estimate the maximum losses that may occur; and the application of copulas to incorporate the dependence between different types of risk. It also implies an approach to Solvency II and to the new capital requirements for quantifying capital that brings this new insurance European legislation.

scholarly journals Operational risk quantification and modelling within Romanian insurance industry

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.

scholarly journals Generating unfavourable VaR scenarios under Solvency II with patchwork copulas

2021 ◽  
Vol 9 (1) ◽  
pp. 327-346
Author(s):  
Dietmar Pfeifer ◽  
Olena Ragulina
Keyword(s):  
Value At Risk ◽  
Insurance Industry ◽  
Solvency Ii ◽  
Two Dimensions ◽  
Insurance Companies ◽  
The European Union ◽  
Case Scenario ◽  
Worst Case ◽  
Risk Scenarios ◽  
Copula Approach

Abstract The central idea of the paper is to present a general simple patchwork construction principle for multivariate copulas that create unfavourable VaR (i.e. Value at Risk) scenarios while maintaining given marginal distributions. This is of particular interest for the construction of Internal Models in the insurance industry under Solvency II in the European Union. Besides this, the Delegated Regulation by the European Commission requires all insurance companies under supervision to consider different risk scenarios in their risk management system for the company’s own risk assessment. Since it is unreasonable to assume that the potential worst case scenario will materialize in the company, we think that a modelling of various unfavourable scenarios as described in this paper is likewise appropriate. Our explicit copula approach can be considered as a special case of ordinal sums, which in two dimensions even leads to the technically worst VaR scenario.

scholarly journals PRUDENT DECISIONS TO ESTIMATE THE RISK OF LOSS IN INSURANCE

2017 ◽  
Vol 23 (2) ◽  
pp. 428-440 ◽  
Author(s):  
Casian BUTACI ◽  
Simona DZITAC ◽  
Ioan DZITAC ◽  
Gabriela BOLOGA
Keyword(s):  
Internal Model ◽  
Empirical Distribution ◽  
Solvency Ii ◽  
Capital Requirements ◽  
Value Theory ◽  
Theory Approach ◽  
Financial Assets ◽  
European Directive ◽  
Insurance Issue

The directive 2009/138/EC „Solvency II”, provides the determination of insurance capital requirements based either on a standard formula or an internal model built by the company and approved by the regulatory authority. The build of an internal model involves the determination of an extreme quantile from the empirical distribution of portfolio. An estimate of this quantile, with a 99.5% confidence level, requires a large number of simulations, each taking into account different scenarios as: insufficient reserves, unfavourable developments of financial assets, etc. The present paper proposes to argue the necessity of the extreme value theory approach in order to estimate the risk of loss for the insurance issue, in accordance with European Directive „Solvency II”, from the perspective of making prudent decisions for the assessment of insurance capital requirements.

Measuring market risk for an agricultural exporter firm: a Copula approach

2017 ◽  
Vol 30 (1) ◽  
pp. 72-86 ◽  
Author(s):  
Julián Fernández
Keyword(s):  
Value At Risk ◽  
Financial Risk ◽  
Real Option ◽  
Market Risk ◽  
Value Theory ◽  
Optimal Decision ◽  
Agricultural Commodity ◽  
Content Type ◽  
The Future ◽  
Agricultural Exporter

Purpose The purpose of this paper is to analyse the effect of market risk on the revenues perceived by an agricultural producer, namely, a coffee exporter firm. Design/methodology/approach To model this risk, copula models and extreme value theory are used to perform more robust estimations, which take into account the multivariate dependence between the risk factors. As a final point, different quantitative measures of risk, such as the value at risk and the expected shortfall, are estimated as an indicator of the maximum expected loss. Findings One of the principal findings is that for an agricultural exporter firm, there is an optimal decision between exporting to another country and selling the commodity in the national market. The choice regarding the levels exported will determine the firm’s amount of risk and expected return. Research limitations/implications One of the limitations found in modelling the risk/return of the firm is the data. Not much data on the structure of the firm can be found, and many of the firms are averse to providing such information. Practical implications The purpose of the paper is to create a measure of risk to analyse the future of the firm, generating a measure of expected risk and return that takes into account the uncertainty of the future. The applications can be applied to measure the risk of a potential investment and real option valuation. Originality/value This paper applied multiple coherent measures of financial risk to an agricultural commodity exporter firm. This can be novel, especially in the context of a non-financial firm.

scholarly journals France

2019 ◽  
Vol 19 (323) ◽  
Author(s):  
Keyword(s):  
Interest Rates ◽  
Insurance Industry ◽  
Solvency Ii ◽  
Capital Requirements ◽  
The European Union ◽  
French Economy ◽  
Low Interest Rates ◽  
Challenging Environment ◽  
High Level

The French insurance industry is the largest in the EU27 and therefore the largest in the European Union after Brexit. The French insurance market is large both because the French economy is the second largest in the EU27 and because insurance is a significant part of the French economy. France has a very high level of insurance penetration, particularly for life insurance. There are 742 insurers in the insurance industry. This large number of insurers creates a diverse and competitive market. There are 339 insurers subject to Solvency II with less than EUR 1 billion in assets. It appears these small insurers are well capitalized with all exceeding a 100 percent SCR even if the transitional measures in Solvency II and the long-term guarantee package are not taken into account. Given the diversification benefits embedded in Solvency II capital requirements and the challenging environment of prolonged low interest rates, the presence of many independent small entities will have an impact on the overall efficiency and cost of delivering products to policyholders in the market.

scholarly journals PROPERTY RISK UNDER SOLVENCY II: EFFECTS OF DIFFERENT UNSMOOTHING TECHNIQUES

2019 ◽  
Vol 25 (1) ◽  
pp. 1-19
Author(s):  
Pablo Durán Santomil ◽  
Luís Otero González ◽  
Onofre Martorell Cunill ◽  
Anna M. Gil-Lafuente
Keyword(s):  
At Risk ◽  
Standard Model ◽  
Confidence Level ◽  
Calculation Method ◽  
Value At Risk ◽  
Solvency Ii ◽  
Capital Requirements ◽  
Insurance Companies ◽  
The Standard Model ◽  
Monthly Index

Solvency II imposes risk-based capital requirements on EU insurance companies. This paper evaluates the property risk standard model proposed. The calibration was performed from the IPD UK monthly index total returns for the period between December 1986 and December 2009. In general, it is considered that returns derived from valuation-based indices are smoother than those derived from transaction-based indices. This paper contributes to the existing literature by applying various unsmoothing techniques to this index. The results show that the capital requirements, applying the same calculation method (historical value at risk at the 99.5% confidence level) as in the calibration of the standard model, are generally bigger than those proposed in the standard model of Solvency II.

scholarly journals Revisiting Calibration of the Solvency II Standard Formula for Mortality Risk: Does the Standard Stress Scenario Provide an Adequate Approximation of Value-at-Risk?

Risks ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 58 ◽  
Author(s):  
Rokas Gylys ◽  
Jonas Šiaulys
Keyword(s):  
At Risk ◽  
Mortality Risk ◽  
Value At Risk ◽  
Relative Size ◽  
Mortality Rates ◽  
Solvency Ii ◽  
Capital Requirements ◽  
Mortality Data ◽  
Model Based ◽  
Standard Formula

The primary objective of this work is to analyze model based Value-at-Risk associated with mortality risk arising from issued term life assurance contracts and to compare the results with the capital requirements for mortality risk as determined using Solvency II Standard Formula. In particular, two approaches to calculate Value-at-Risk are analyzed: one-year VaR and run-off VaR. The calculations of Value-at-Risk are performed using stochastic mortality rates which are calibrated using the Lee-Carter model fitted using mortality data of selected European countries. Results indicate that, depending on the approach taken to calculate Value-at-Risk, the key factors driving its relative size are: sensitivity of technical provisions to the latest mortality experience, volatility of mortality rates in a country, policy term and benefit formula. Overall, we found that Solvency II Standard Formula on average delivers an adequate capital requirement, however, we also highlight particular situations where it could understate or overstate portfolio specific model based Value-at-Risk for mortality risk.

Stress Testing in a Value at Risk Framework

CFA Digest ◽  
1999 ◽  
Vol 29 (2) ◽  
pp. 76-78
Author(s):  
Thomas J. Latta
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Stress Testing ◽  
A Value ◽  
Risk Framework
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