Nonparametric conditional autoregressive expectile model via neural network with applications to estimating financial risk

Xu, Qifa; Liu, Xi; Jiang, Cuixia; Yu, Keming

doi:10.1002/asmb.2212

Cited by 12 publications

(3 citation statements)

References 37 publications

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“…In many practical industry fields, the demand for accurate information prediction has been improved increasingly. Such as the financial field, it is the most significant that accurately predict the future trend of the financial data based on available data [1,2]. Meanwhile, the prediction of vehicle flowrate and visitors' flowrate in the transportation industry is important for allocation of transportation resources [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Small Sample Data-Driven Method for Interval Prediction on Structural Responses

Wang

2023

J. Mod. Mech. Eng. Technol.

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This paper proposes an approach of intervals prediction on the structural responses under the limited samples by using Grey-BP neural networks (GBP) and genetic algorithm (GA). For the response prediction of complex structures, the presented method can ensure the necessary accuracy and can greatly reduce the cost of computation. Meanwhile, the method is different from the other traditional neural networks, which represents the superiority of dealing with the interval prediction under limited samples with help of the grey theory. In this paper, the Grey-BP neural networks are established by introducing the grey theory, which is used to achieve the mapping relationship between input and output of the system and accomplish the approximation of real mapping. The accuracy of built the mapping model can be tested by error analysis. Subsequently, the issue of interval prediction can be translated to optimal problem for extremum value. Although the explicit expression of the established mapping relationship is unknown, the fitness of every value includes the information of extremum. So, the best fitness and worst fitness can be searched in the given bound of uncertain variables based on genetic algorithm, and then achieve the upper bound and lower bound of the system response depending on the capability of global search. After proposed technologies are given in detail, one numerical example and one engineering example are presented and the results are discussed with common methods based on traditional BP neural network and Monte Carlo, which demonstrates the validity and reasonability of the developed methodology.

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Section: Introductionmentioning

confidence: 99%

Small Sample Data-Driven Method for Interval Prediction on Structural Responses

Wang

2023

J. Mod. Mech. Eng. Technol.

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“…Very recently, following applications in other disciplines, machine learning techniques have been advocated to solve insurance problems. Despite their recent application in the finance and insurance industry, 6‐8 they are quickly becoming popular 9‐11 in addressing the new paradigm of the “high‐tech business world” 12 . Concerning the problem addressed in this article, the available literature is relatively poor.…”

Section: Introductionmentioning

confidence: 99%

Machine learning techniques in nested stochastic simulations for life insurance

Castellani

Fiore

Marino

et al. 2021

Appl Stoch Models Bus & Ind

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The insurance regulatory regime introduced in the European Union by the “Solvency II” Directive 2009/138, that has become applicable on 1 January 2016, is aimed to safeguard policyholders and beneficiaries by requiring insurance undertakings to hold own funds able to cover losses, in excess to the expected ones, at the 99.5% confidence level, over a 1‐year period. In order to assess risks and evaluate the regulatory Solvency Capital Requirement, undertakings should compute the probability distribution of the Net Asset Value over a 1‐year period, with a financially inspired market consistent approach. In life insurance, given the peculiarities of the contracts, the valuation of the Net Asset Value distribution requires a nested Monte Carlo simulation, which is extremely time‐consuming. Machine learning techniques are considered a promising candidate to reduce the computational burden of nested simulations. This work investigates the potential of well‐established methods, such as deep learning networks and support vector regressors, when applied to the valuation of the Solvency Capital Requirement of participating life insurance policies, by empirically assessing their effectiveness and by comparing their efficiency and accuracy, also w.r.t. the “traditional” least squares Monte Carlo technique. The work aims also to contribute to the global process of renewal of the European insurance industry, where Solvency II has made the board of directors fully responsible of the choice of evaluation techniques and algorithmic processes, under the periodic monitoring of national supervisory authorities.

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“…for all t ∈ R and a fixed τ ∈ (0, 1), see primarily [29] and also [19,1] for further references. These expectiles have attracted considerable attention in recent years and have been applied successfully in many areas, for instance, in demography [31], in education [33] and extensively in finance [48,23,50,25]. In fact, it has recently been shown (see, e.g.…”

Section: Introductionmentioning

confidence: 99%

Learning rates for kernel-based expectile regression

Farooq

Steinwart

2018

Mach Learn

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Conditional expectiles are becoming an increasingly important tool in finance as well as in other areas of applications. We analyse a support vector machine type approach for estimating conditional expectiles and establish learning rates that are minimax optimal modulo a logarithmic factor if Gaussian RBF kernels are used and the desired expectile is smooth in a Besov sense. As a special case, our learning rates improve the best known rates for kernel-based least squares regression in this scenario. Key ingredients of our statistical analysis are a general calibration inequality for the asymmetric least squares loss, a corresponding variance bound as well as an improved entropy number bound for Gaussian RBF kernels.

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Nonparametric conditional autoregressive expectile model via neural network with applications to estimating financial risk

Cited by 12 publications

References 37 publications

Small Sample Data-Driven Method for Interval Prediction on Structural Responses

Small Sample Data-Driven Method for Interval Prediction on Structural Responses

Machine learning techniques in nested stochastic simulations for life insurance

Learning rates for kernel-based expectile regression

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