Using Neural Networks to Price and Hedge Variable Annuity Guarantees

Doyle, Daniel; Groendyke, Chris

doi:10.3390/risks7010001

Cited by 16 publications

(4 citation statements)

References 44 publications

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“…Content may change prior to final publication. [110]. Datasets available for metamodelling research: Interested researchers can access synthetic datasets related to Metamodelling for sensitivity analysis in [111], [112].…”

Section: Component Techniquesmentioning

confidence: 99%

Machine Learning for Financial Risk Management: A Survey

et al. 2020

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Financial risk management avoids losses and maximizes profits, and hence is vital to most businesses. As the task relies heavily on information-driven decision making, machine learning is a promising source for new methods and technologies. In recent years, we have seen increasing adoption of machine learning methods for various risk management tasks. Machine-learning researchers, however, often struggle to navigate the vast and complex domain knowledge and the fast-evolving literature. This paper fills this gap, by providing a systematic survey of the rapidly growing literature of machine learning research for financial risk management. The contributions of the paper are four-folds: First, we present a taxonomy of financial-risk-management tasks and connect them with relevant machine learning methods. Secondly, we highlight significant publications in the past decade. Thirdly, we identify major challenges being faced by researchers in this area. And finally, we point out emerging trends and promising research directions.

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Section: Component Techniquesmentioning

confidence: 99%

Machine Learning for Financial Risk Management: A Survey

et al. 2020

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“…MLP able to solve non-linear problems while maintaining the original structure of perceptron of feedforward layered [25]. Neural network has been demonstrated to successfully employed in various field of studies such as medical [26], [27], agriculture [28], [29], industrial [30], [31] and finance [32], [33].…”

Section: A Neural Networkmentioning

confidence: 99%

Data Pre-Processing Algorithm for Neural Network Binary Classification Model in Bank Tele-Marketing

Halim*¹,

Jaya²,

Fadzil³

2020

IJITEE

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Tele-marketing presents a huge challenge in identifying potential customers with lack of effective marketing strategy may led a company to succumbs to problems such as prolonged marketing campaign. Various attempts to improve the performance of binary classification model for bank tele-marketing data. Previous researches indicate that the neural network is the most common algorithms being employed and able to produce commendable results with higher accuracy percentages compared to other algorithms. Despite several attempts to improve the model through treatment of imbalance dataset and features selection, this research argues that they are incomplete. Therefore, this research proposes a data pre-processing algorithm for bank tele-marketing binary classification neural network. Three datasets have been employed (19, 16, and 20 features) to evaluate the performance of the algorithm towards the classification model. The data pre-processing algorithm is divided into three phases; data cleaning, data imbalance treatment and finally data normalization. In this paper, the result indicated that binary classification model complemented with data cleaning techniques such as Missing common (MC) and Tomek Links (TL) shows a better result compared to Ignore Missing (IM). In terms of data normalization, techniques such as MaxAbsScaler (MAS) and MinMaxScaler (MMS) consistently indicated better performance from other normalization techniques. The classification model employed in this paper utilize data pre-processing algorithm combination of MC-TL-MMS. The algorithm using this approach able to record an area of the receiver operating characteristic curve (AUC) of 0.9129 and 0.9464 by using 16 features and 20 features respectively. This result presents the highest figure in terms of performance accuracy compared to other previous researches

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“…In recent years, some papers have considered the pricing of equity-linked contracts by neural networks. Doyle & Groendyke (2019) explored the use of neural networks to price and hedge equity-linked contracts. They showed that neural networks offer an important computational gain compared to crude Monte-Carle methods, however they did not consider stochastic mortality neither stochastic volatility in their framework.…”

Section: Introductionmentioning

confidence: 99%

Pricing equity-linked life insurance contracts with multiple risk factors by neural networks

Barigou¹,

Delong²

2020

Preprint

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This paper considers the pricing of equity-linked life insurance contracts with death and survival benefits in a general model with multiple stochastic risk factors: interest rate, equity, volatility, unsystematic and systematic mortality. We price the equitylinked contracts by assuming that the insurer hedges the risks to reduce the local variance of the net asset value process and requires a compensation for the non-hedgeable part of the liability in the form of an instantaneous standard deviation risk margin. The price can then be expressed as the solution of a system of non-linear partial differential equations. We reformulate the problem as a backward stochastic differential equation with jumps and solve it numerically by the use of efficient neural networks. Sensitivity analysis is performed with respect to initial parameters and an analysis of the accuracy of the approximation of the true price with our neural networks is provided.

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Using Neural Networks to Price and Hedge Variable Annuity Guarantees

Cited by 16 publications

References 44 publications

Machine Learning for Financial Risk Management: A Survey

Machine Learning for Financial Risk Management: A Survey

Data Pre-Processing Algorithm for Neural Network Binary Classification Model in Bank Tele-Marketing

Pricing equity-linked life insurance contracts with multiple risk factors by neural networks

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