Loss Models

Klugman, Stuart A.; Panjer, Harry H.; Willmot, Gordon E.

doi:10.1002/9780470391341

Cited by 224 publications

(30 citation statements)

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“…Let S(t) = 0 if N(t) = 0. When the insurance portfolio is large and an individual claim is relatively small compared to the size of the total reserve, a diffusion which approximates to the CramerLundberg model can be obtained using the following transformation (see [6,Chapter 11]). Assume that N(t) has intensity m, and let…”

Section: N(t)mentioning

confidence: 99%

A stochastic differential reinsurance game

Zeng

2010

J. Appl. Probab.

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We study a stochastic differential game between two insurance companies who employ reinsurance to reduce the risk of exposure. Under the assumption that the companies have large insurance portfolios compared to any individual claim size, their surplus processes can be approximated by stochastic differential equations. We formulate competition between the two companies as a game with a single payoff function which depends on the surplus processes. One company chooses a dynamic reinsurance strategy in order to maximize this expected payoff, while the other company simultaneously chooses a dynamic reinsurance strategy so as to minimize the same quantity. We describe the Nash equilibrium of this stochastic differential game and solve it explicitly for the case of maximizing/minimizing the exit probability.

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Section: N(t)mentioning

confidence: 99%

A stochastic differential reinsurance game

Zeng

2010

J. Appl. Probab.

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“…For more discussion of these estimators, see for example, Hyndman and Fan (1996) and Klugman et al (1998).…”

Section: Estimating Var and Cte Using Monte Carlomentioning

confidence: 99%

“…A slightly modified version of this estimate is also found in Klugman et al (1998), termed the smoothed quantile estimate.…”

Section: Bias Of Sample Estimates Of Var and Ctementioning

confidence: 99%

Quantifying and Correcting the Bias in Estimated Risk Measures

Kim

Hardy²

2007

ASTIN Bull.

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In this paper we explore the bias in the estimation of the Value at Risk and Conditional Tail Expectation risk measures using Monte Carlo simulation. We assess the use of bootstrap techniques to correct the bias for a number of different examples. In the case of the Conditional Tail Expectation, we show that application of the exact bootstrap can improve estimates, and we develop a practical guideline for assessing when to use the exact bootstrap.

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“…The natural disasters cause various negative consequences of the monitored objects which can be described from a mathematical viewpoint as various risk situations [4], [6].…”

Section: Various Risk Situationsmentioning

confidence: 99%

Insurance Models for Assessment of the Municipal LossesDue to Natural Disasters

Златева¹,

Velev²

2012

IJTEF

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Abstract-Various insurance models for assessment of the possible possible financial losses for a municipality due to natural disasters. Risk situations which describe the possible financial losses of the monitored objects, are considered. The main components of the insurance models are discussed. The loss assessment results can support the municipal government to take more informed decisions for effective use of limited financial resources to activities in emergency situations. A concept for implementing the insurance models as a part of a Web integrated information system for risk management of natural disasters are outlined.

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Loss Models

Cited by 224 publications

References 0 publications

A stochastic differential reinsurance game

A stochastic differential reinsurance game

Quantifying and Correcting the Bias in Estimated Risk Measures

Insurance Models for Assessment of the Municipal LossesDue to Natural Disasters

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