Applying simulation optimization to the asset allocation of a property–casualty insurer

Yu, Tzu‐Yi; Tsai, Chenghsien; Huang, Hsiao-Tzu

doi:10.1016/j.ejor.2010.04.012

Cited by 11 publications

(20 citation statements)

References 14 publications

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“…The literature that follows this approach considers the asset allocation problem as stochastic, and the solutions are illustrated by Hamilton—Jacobi—Bellman partial differential equations (Yu et al . ()).…”

Section: Literature Reviewmentioning

confidence: 99%

“…We use the correlation matrix R proposed by Yu et al . () to estimate these Wiener processes (see ).…”

Section: Modelmentioning

confidence: 99%

“…Like Yu et al . (), we suppose that the stock index behaviour is a continuous time stochastic process. This process follows an interest rate adjusted geometric Brownian motion:

\frac{d S}{S} = (r - r_{s}) d t + σ_{S} d W_{t}

where dS is the change in the market index' prices, r s represents the risk premium and σ S denotes the index price volatility.…”

Section: Modelmentioning

confidence: 99%

“…The price process of the three proposed alternative investments supposed to be geometric Brownian motions like Yu et al . ():

\frac{d A I^{italicij}}{A I^{italicij}} = μ_{A I^{italicij}} d t + σ_{A I^{italicij}} d W_{t} i = \{l, h\} and j = \{l, h\}

where

μ_{A I^{ij}}

and

σ_{A I^{ij}}

denote, respectively, the expected return and standard deviation of the alternative investment prices characterized by a risk i and a return j .…”

Section: Modelmentioning

confidence: 99%

“…For shareholders' expected utility, we adopt the utility function used by Yu et al . (). For the technical efficiency, we use the directional output distance function proposed by Färe et al .…”

Section: Introductionmentioning

confidence: 99%

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Multiobjective Optimization for the Asset Allocation of European Nonlife Insurance Companies

Jarraya

Bouri

2013

Multi Criteria Decision Anal

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An optimal asset allocation is crucial for non-life insurance companies. The most previous studies focused on this topic use a mono-objective technique optimization. This technique usually allows the maximization of shareholders' expected utility. As non-life insurance company is a complex system, it has many stakeholders other than shareholders. So, the satisfaction of the shareholders' expected utility cannot lead usually to the satisfaction of other stakeholders' objectives. Therefore, the focus on utility maximization can be a destruction source of other objectives such as productivity, competitiveness and solvency. Our developed model integrates simulation approach with a Multi-Objective Particle Swarm Optimization algorithm. This model insures an optimal asset allocation that maximizes, simultaneously, shareholders expected utility and technical efficiency of European non-life insurance companies. The empirical application conducts a comparison between the attained results with multi-objective optimization technique and mono-objective technique to search the optimal asset allocation for non-life insurance companies. Our results show that the investment portfolio will be more diversified between most available investment assets. In addition, any decision maker should take account of different stakeholders' objectives. Accordingly multi-objective optimization allows us to find the best asset allocation that maximizes simultaneously expected utility and technical efficiency of non-life insurance companies.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…We use the correlation matrix R proposed by Yu et al . () to estimate these Wiener processes (see ).…”

Section: Modelmentioning

confidence: 99%

“…Like Yu et al . (), we suppose that the stock index behaviour is a continuous time stochastic process. This process follows an interest rate adjusted geometric Brownian motion:

\frac{d S}{S} = (r - r_{s}) d t + σ_{S} d W_{t}

where dS is the change in the market index' prices, r s represents the risk premium and σ S denotes the index price volatility.…”

Section: Modelmentioning

confidence: 99%

“…The price process of the three proposed alternative investments supposed to be geometric Brownian motions like Yu et al . ():

\frac{d A I^{italicij}}{A I^{italicij}} = μ_{A I^{italicij}} d t + σ_{A I^{italicij}} d W_{t} i = \{l, h\} and j = \{l, h\}

where

μ_{A I^{ij}}

and

σ_{A I^{ij}}

denote, respectively, the expected return and standard deviation of the alternative investment prices characterized by a risk i and a return j .…”

Section: Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multiobjective Optimization for the Asset Allocation of European Nonlife Insurance Companies

Jarraya

Bouri

2013

Multi Criteria Decision Anal

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show abstract

Coupling a memetic algorithm to simulation models for promising multi-period asset allocations

Huang

2013

Computers & Operations Research

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Genetic-algorithm-based simulation optimization considering a single stochastic constraint

Tsai

2014

European Journal of Operational Research

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Applying simulation optimization to the asset allocation of a property–casualty insurer

Cited by 11 publications

References 14 publications

Multiobjective Optimization for the Asset Allocation of European Nonlife Insurance Companies

Multiobjective Optimization for the Asset Allocation of European Nonlife Insurance Companies

Coupling a memetic algorithm to simulation models for promising multi-period asset allocations

Genetic-algorithm-based simulation optimization considering a single stochastic constraint

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