Global Optimization for Automatic Model Points Selection in Life Insurance Portfolios

Starting from an original portfolio of life insurance policies, in this article we propose a methodology to select model points portfolios that reproduce the original one, preserving its market risk under a certain measure. In order to achieve this goal, we first define an appropriate risk functional that measures the market risk associated to the interest rates evolution. Although other alternative interest rate models could be considered, we have chosen the LIBOR (London Interbank Offered Rate) market model. Once we have selected the proper risk functional, the problem of finding the model points of the replicating portfolio is formulated as a problem of minimizing the distance between the original and the target model points portfolios, under the measure given by the proposed risk functional. In this way, a high-dimensional global optimization problem arises and a suitable hybrid global optimization algorithm is proposed for the efficient solution of this problem. Some examples illustrate the performance of a parallel multi-CPU implementation for the evaluation of the risk functional, as well as the efficiency of the hybrid Basin Hopping optimization algorithm to obtain the model points portfolio.

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An improved teaching–learning-based optimization algorithm for solving global optimization problem

Information Sciences ◽

10.1016/j.ins.2014.11.001 ◽

2015 ◽

Vol 297 ◽

pp. 171-190 ◽

Cited By ~ 57

Author(s):

Debao Chen ◽

Feng Zou ◽

Zheng Li ◽

Jiangtao Wang ◽

Suwen Li

Keyword(s):

Global Optimization ◽

Optimization Algorithm ◽

Optimization Problem ◽

Global Optimization Problem ◽

Teaching Learning Based Optimization ◽

Teaching Learning

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A New Global Optimization Algorithm and its Application

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.33-37.1407 ◽

2008 ◽

Vol 33-37 ◽

pp. 1407-1412

Author(s):

Ying Hui Lu ◽

Shui Lin Wang ◽

Hao Jiang ◽

Xiu Run Ge

Keyword(s):

Global Optimization ◽

Objective Function ◽

Optimization Algorithm ◽

Inverse Analysis ◽

Optimization Problem ◽

Practical Application ◽

Global Optimization Algorithm ◽

Material Parameters ◽

Non Linear ◽

Variable Space

In geotechnical engineering, based on the theory of inverse analysis of displacement, the problem for identification of material parameters can be transformed into an optimization problem. Commonly, because of the non-linear relationship between the identified parameters and the displacement, the objective function bears the multimodal characteristic in the variable space. So to solve better the multimodal characteristic in the non-linear inverse analysis, a new global optimization algorithm, which integrates the dynamic descent algorithm and the modified BFGS (Brogden-Fletcher-Goldfrab-Shanno) algorithm, is proposed. Five typical multimodal functions in the variable space are tested to prove that the new proposed algorithm can quickly converge to the best point with few function evaluations. In the practical application, the new algorithm is employed to identify the Young’s modulus of four different materials. The results of the identification further show that the new proposed algorithm is a very highly efficient and robust one.

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A Mixed Integer Efficient Global Optimization Algorithm with Multiple Infill Strategy - Applied to a Wing Topology Optimization Problem

AIAA Scitech 2019 Forum ◽

10.2514/6.2019-2356 ◽

2019 ◽

Cited By ~ 3

Author(s):

Satadru Roy ◽

William A. Crossley ◽

Bret Stanford ◽

Kenneth T. Moore ◽

Justin S. Gray

Keyword(s):

Global Optimization ◽

Topology Optimization ◽

Optimization Algorithm ◽

Optimization Problem ◽

Mixed Integer ◽

Efficient Global Optimization ◽

Global Optimization Algorithm ◽

Topology Optimization Problem

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LIBOR market model with multiplicative basis

International Journal of Financial Engineering ◽

10.1142/s2424786318500147 ◽

2018 ◽

Vol 05 (02) ◽

pp. 1850014 ◽

Cited By ~ 1

Author(s):

Yangfan Zhong

Keyword(s):

Interest Rate ◽

Interest Rates ◽

Slight Modification ◽

Cash Flows ◽

Market Model ◽

Interest Rate Models ◽

Forward Rates ◽

Libor Market Model ◽

Multiplicative Basis ◽

Negative Interest Rates

The study on the multiple-curve interest rate models becomes increasingly active since the 2007 credit crunch, for which one curve, typically the OIS curve, is used for discounting purpose, while the LIBOR curves (associated with various market tenors) are used for projecting the future cash flows. In this work, we extend the standard LIBOR market model to accommodate such multiple-curve setting by means of a multiplicative basis. The multiplicative basis is modeled as an exponential function of multi-factor square-root processes. Under the multiplicative basis setup, the OIS forward rates are correlated with the implied (additive) LIBOR-OIS spreads. We then derive closed-form pricing formulas for caplet, swaption, and interest rate futures in the multiplicative basis framework. In particular, we show that the valuation of caplet and swaption can be easily computed by a proper integral of real-valued functions, which facilitates the calibration of our model. Finally, we discuss a slight modification of our model to allow for negative interest rates.

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A simple approximation for the no-arbitrage drifts in Libor market model–SABR-family interest-rate models

The Journal of Computational Finance ◽

10.21314/jcf.2015.305 ◽

2015 ◽

Vol 19 (1) ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Riccardo Rebonato

Keyword(s):

Interest Rate ◽

Market Model ◽

Simple Approximation ◽

Interest Rate Models ◽

No Arbitrage ◽

Libor Market Model

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Risk Management for Bonds with Embedded Options

Mathematics ◽

10.3390/math8050790 ◽

2020 ◽

Vol 8 (5) ◽

pp. 790

Author(s):

Antonio Díaz ◽

Marta Tolentino

Keyword(s):

Risk Management ◽

Interest Rate ◽

Interest Rates ◽

Interest Rate Risk ◽

Interest Rate Models ◽

Management Measures ◽

Interest Rate Risk Management ◽

Embedded Options ◽

The Interest Rate ◽

Interest Rate Change

This paper examines the behavior of the interest rate risk management measures for bonds with embedded options and studies factors it depends on. The contingent option exercise implies that both the pricing and the risk management of bonds requires modelling future interest rates. We use the Ho and Lee (HL) and Black, Derman, and Toy (BDT) consistent interest rate models. In addition, specific interest rate measures that consider the contingent cash-flow structure of these coupon-bearing bonds must be computed. In our empirical analysis, we obtained evidence that effective duration and effective convexity depend primarily on the level of the forward interest rate and volatility. In addition, the higher the interest rate change and the lower the volatility, the greater the differences in pricing of these bonds when using the HL or BDT models.

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A Hybrid Whale Optimization Algorithm for Global Optimization

Mathematics ◽

10.3390/math9131477 ◽

2021 ◽

Vol 9 (13) ◽

pp. 1477

Author(s):

Chun-Yao Lee ◽

Guang-Lin Zhuo

Keyword(s):

Global Optimization ◽

Optimization Algorithm ◽

Optimization Problems ◽

Whale Optimization Algorithm ◽

Initial Population ◽

Test Functions ◽

Benchmark Test ◽

Thermal Exchange ◽

Whale Optimization ◽

Benchmark Test Functions

This paper proposes a hybrid whale optimization algorithm (WOA) that is derived from the genetic and thermal exchange optimization-based whale optimization algorithm (GWOA-TEO) to enhance global optimization capability. First, the high-quality initial population is generated to improve the performance of GWOA-TEO. Then, thermal exchange optimization (TEO) is applied to improve exploitation performance. Next, a memory is considered that can store historical best-so-far solutions, achieving higher performance without adding additional computational costs. Finally, a crossover operator based on the memory and a position update mechanism of the leading solution based on the memory are proposed to improve the exploration performance. The GWOA-TEO algorithm is then compared with five state-of-the-art optimization algorithms on CEC 2017 benchmark test functions and 8 UCI repository datasets. The statistical results of the CEC 2017 benchmark test functions show that the GWOA-TEO algorithm has good accuracy for global optimization. The classification results of 8 UCI repository datasets also show that the GWOA-TEO algorithm has competitive results with regard to comparison algorithms in recognition rate. Thus, the proposed algorithm is proven to execute excellent performance in solving optimization problems.

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Finding Global Minima with a Filled Function Approach for Non-Smooth Global Optimization

Discrete Dynamics in Nature and Society ◽

10.1155/2010/843609 ◽

2010 ◽

Vol 2010 ◽

pp. 1-10 ◽

Cited By ~ 1

Author(s):

Weixiang Wang ◽

Youlin Shang ◽

Ying Zhang

Keyword(s):

Global Optimization ◽

Optimization Problem ◽

Numerical Test ◽

Function Approach ◽

Global Optimization Problem ◽

Computational Results ◽

Filled Function ◽

Global Minima ◽

Smooth Case ◽

Definition Of

A filled function approach is proposed for solving a non-smooth unconstrained global optimization problem. First, the definition of filled function in Zhang (2009) for smooth global optimization is extended to non-smooth case and a new one is put forwarded. Then, a novel filled function is proposed for non-smooth the global optimization and a corresponding non-smooth algorithm based on the filled function is designed. At last, a numerical test is made. The computational results demonstrate that the proposed approach is effcient and reliable.

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