Relevancy of the Cost-of-Capital Rate for the Insurance Companies

For many assets and liabilities there exist deep and liquid markets so that the market value are reasily observed. However, for non-hedgeable risks, the market value of liabilities must be estimated. The Draft Solvency II Directive suggests in article 75 that the valuation of technical provisions (for non hedgeable risks) shall be the sum of a best estimate and a market value margin measuring the cost of risk. The market value margin is calculated as the present value of the cost of holding the solvency capital requirement for non-hedgeable risks during the whole run-off period of the in-force portfolio. One of the majour input of the market value margin is the cost-of-capital rate which corresponds to the risk premium applied on each unit of risk. According to European Commission (2007), European insurance and Reinsurance Federation (2008), and Chief Risk Officer Forum (2008), a single cost-of-capital rate shall be used by all insurance undertakings and for all lines of business. This paper aims at analyzing the cost-of-capital rate given by European Insurance and Reinsurance Federation (2008), and Chief Risk Officer Forum (2008). In particular, we highlight that it is very difficult to assess a cost-of- capital rate by using either the frictional cost approach or the full industry information beta methodology. Nevertheless, we highlight also that it seems to be irrelevant to use only one risk premium or all the risks and all the companies. We show that risk is not characterized by a fixed prices. In fact, the price of risk depends on the basket of risks at which it belongs, the risk level considered and the time period.Market value margin, cost-of-capital rate, diversification effect, risk level.

Dynamic Analysis of the Insurance Linked Securities Index

This paper aims to provide a dynamic analysis of the insurance linked securities index. We are discussing the behaviour of the index for three years and pointing out the consequences of some major events like Katrina or the last and current financial crisis. Some stylized facts of the index, like the non-Gaussianity, the asymmetry or the clusters of volatility, are highlighted. We are using some GARCH-type models and the generalized hyperbolic distributions in order to capture these elements. The GARCH in Mean model with a Normal Inverse Gaussian distribution seems to be very efficient to fit the log-returns of the insurance linked securities index.Insurance Linked Securities, Garch-type models, Normal Inverse Gaussian Distribution

Relevancy of the Cost-of-Capital Rate for the Insurance Companies

URL des Documents de travail : http://ces.univ-paris1.fr/cesdp/CESFramDP2008.htmClassification JEL : G12, G20, G22, G32.Documents de travail du Centre d'Economie de la Sorbonne 2008.94 - ISSN : 1955-611XFor many assets and liabilities there exist deep and liquid markets so that the market value are reasily observed. However, for non-hedgeable risks, the market value of liabilities must be estimated. The Draft Solvency II Directive suggests in article 75 that the valuation of technical provisions (for non hedgeable risks) shall be the sum of a best estimate and a market value margin measuring the cost of risk. The market value margin is calculated as the present value of the cost of holding the solvency capital requirement for non-hedgeable risks during the whole run-off period of the in-force portfolio. One of the majour input of the market value margin is the cost-of-capital rate which corresponds to the risk premium applied on each unit of risk. According to European Commission (2007), European insurance and Reinsurance Federation (2008), and Chief Risk Officer Forum (2008), a single cost-of-capital rate shall be used by all insurance undertakings and for all lines of business. This paper aims at analyzing the cost-of-capital rate given by European Insurance and Reinsurance Federation (2008), and Chief Risk Officer Forum (2008). In particular, we highlight that it is very difficult to assess a cost-of- capital rate by using either the frictional cost approach or the full industry information beta methodology. Nevertheless, we highlight also that it seems to be irrelevant to use only one risk premium or all the risks and all the companies. We show that risk is not characterized by a fixed prices. In fact, the price of risk depends on the basket of risks at which it belongs, the risk level considered and the time period.Pour beaucoup d'actifs et de passifs, il existe des marchés profonds et liquides telle que la valeur de marché est directement observée. Toutefois, pour les risques non-hedgeables, la valeur de marché des passifs doit être estimée. La proposition de directive solvabilité II suggère dans l'article 75 que l'évaluation des provisions techniques doit être la somme d'un "best estimate" et d'une marge pour valeur de marché mesurant le coût du risque. Cette marge est calculée comme étant la valeur présente du coût lié à la détention du capital couvrant les risques non-hedgeables jusqu'à épuisement des provisions. Un des éléments essentiels de cette marge est le coût du capital unitaire qui correspond à la prime de risque appliqué à chaque unité de risque. Selon la Commission Européenne, l'European insurance and Reinsurance Federation (2008), et le Chief Risk Officer Forum (2008), un unique coût du capital unitaire doit être utilisé par tous les "preneurs" d'assurance et pour toutes les lignes d'affaires. Ce papier a pour objectif d'analyser un tel coût du capital unitaire. Nous montrons en particulier qu'il est extrêmement difficile à mesurer, tant par une approche par les coûts de friction que par un modèle à la Fama-French à deux facteurs. Toutefois, nous mettons en avant qu'il ne paraît pas pertinent de retenir une seule prime de risque pour l'ensemble des risques et l'ensemble des compagnies. Nous montrons que le risque n'est pas caractérisé par un prix unique. En fait, ce prix dépend du panier de risque dans lequel le risque étudié appartient, du niveau de risque considéré et de la période

Is the Insurance Cost-of-Capital Fair?

This paper aims at presenting the insurance cost-of-capital com- putation issue. It highlights two methodologies introduced by Chief Risk Of- ficer Forum (2008) to perform the cost-of-capital rate and which more or less justify the risk premium adopted by supervisory authorities. These strategies are based either on market return of insurance companies or on the modelling of insurance business profit and loss. We estimate a cost-of-capital rate corre- sponding to these basic methodologies and point out benefits and drawbacks of each method. We show that the risk premium adopted by the supervisory authorities is inside the interval confidence given either by market data or by the modelling : thus it would correspond to a fair cost-of-capital rate. In addi- tion to that we discuss the fact that this rate is quite low and allow to adopt a relative conservative strategy.insurance; cost-of-capital; computation issue;

The Number of Regimes Across Asset Returns: Identification and Economic Value

Cahier de recherche du CERAG 2011-05 E2A shared belief in the financial industry is that markets are driven by two types of regimes. Bull markets would be characterized by high returns and low volatility whereas bear markets would display low returns coupled with high volatility. Modeling the dynam- ics of different asset classes (stocks, bonds, commodities and currencies) with a Markov- Switching model and using a density-based test, we reject the hypothesis that two regimes are enough to capture asset returns' evolutions for many of the investigated assets. Once the accuracy of our test methodology has been assessed through Monte Carlo experi- ments, our empirical results point out that between two and five regimes are required to capture the features of each asset's distribution. Moreover, we show that only a part of the underlying number of regimes is explained by the distributional characteristics of the returns such as kurtosis. A thorough out-of-sample analysis provides additional evidence that there are more than just bulls and bears in financial markets. Finally, we high- light that taking into account the real number of regimes allows both improved portfolio returns and density forecasts

Identifying and Explaining the Number of Regimes Driving Asset Returns

Cahier de recherche du CERAG 2011-05 E2A shared belief in the financial industry is that markets are driven by two types of regimes. Bull markets would be characterized by high returns and low volatil- ity whereas bear markets would display low returns coupled with high volatility. Modelling the dynamics of different asset classes (stocks, bonds, commodities and currencies) with a Markov-Switching model and using a density-based test, we re- ject the hypothesis that two regimes are enough to capture asset returns' evolutions. Once the accuracy of our test methodology has been assessed through Monte Carlo experiments, our empirical results point out that between three and five regimes are required to capture the features of each asset's distribution. A probit multinomial regression highlights that only a part of the underlying number of regimes is par- tially explained by the absolute average yearly risk premium and by distributional charateristics of the returns such as the kurtosis

The Number of Regimes Across Asset Returns: Identification and Economic Value

A shared belief in the financial industry is that markets are driven by two types of regimes. Bull markets would be characterized by high returns and low volatility whereas bear markets would display low returns coupled with high volatility. Modeling the dynam- ics of different asset classes (stocks, bonds, commodities and currencies) with a Markov- Switching model and using a density-based test, we reject the hypothesis that two regimes are enough to capture asset returns' evolutions for many of the investigated assets. Once the accuracy of our test methodology has been assessed through Monte Carlo experi- ments, our empirical results point out that between two and five regimes are required to capture the features of each asset's distribution. Moreover, we show that only a part of the underlying number of regimes is explained by the distributional characteristics of the returns such as kurtosis. A thorough out-of-sample analysis provides additional evidence that there are more than just bulls and bears in financial markets. Finally, we high- light that taking into account the real number of regimes allows both improved portfolio returns and density forecasts.Bull and bear markets; Markov switching models; Number of regimes; Density based tests

Identifying and Explaining the Number of Regimes Driving Asset Returns

A shared belief in the financial industry is that markets are driven by two types of regimes. Bull markets would be characterized by high returns and low volatil- ity whereas bear markets would display low returns coupled with high volatility. Modelling the dynamics of different asset classes (stocks, bonds, commodities and currencies) with a Markov-Switching model and using a density-based test, we re- ject the hypothesis that two regimes are enough to capture asset returns' evolutions. Once the accuracy of our test methodology has been assessed through Monte Carlo experiments, our empirical results point out that between three and five regimes are required to capture the features of each asset's distribution. A probit multinomial regression highlights that only a part of the underlying number of regimes is par- tially explained by the absolute average yearly risk premium and by distributional charateristics of the returns such as the kurtosis.financial industry; markets; asset classes