Risks

17 pages, 406 KiB

Open AccessEditor’s ChoiceArticle

Risk Approach—Risk Hierarchy or Construction Investment Risks in the Light of Interim Empiric Primary Research Conclusions

by Tibor Pál Szemere, Mónika Garai-Fodor and Ágnes Csiszárik-Kocsir

Risks 2021, 9(5), 84; https://doi.org/10.3390/risks9050084 - 1 May 2021

Cited by 13 | Viewed by 3250

Abstract

The focus of this study is to examine the investment project process. Since investment can also be considered as economic interactions, certain risks are associated with their implementation. Risk factors were given a particular priority during the secondary and primary research, while determining [...] Read more.

The focus of this study is to examine the investment project process. Since investment can also be considered as economic interactions, certain risks are associated with their implementation. Risk factors were given a particular priority during the secondary and primary research, while determining the most relevant risk factors of investment project processes in relation to the B2B market. The risk map for investment project processes was created in line with the relevant secondary sources, qualitative and quantitative primary results. This is topical because the importance of investments is unquestionable in a market economy. Therefore, a comprehensive risk assessment might provide results that are useful for both supply and demand side actors in B2B market relations. Based on the results of the primary study, the perceived risks of the project process were defined, and they were structured into a risk hierarchy system. Based on the qualitative results, we performed a quantitative study. Based on the responses of the sample subjects, we determined the perceived risk factors, and on the basis of them, we segmented the service provider (contractor) market. The main socio-demographic characteristics of each segment were also explored in the framework of the research. Full article

11 pages, 709 KiB

Open AccessEditor’s ChoiceArticle

The Use of Discriminant Analysis to Assess the Risk of Bankruptcy of Enterprises in Crisis Conditions Using the Example of the Tourism Sector in Poland

by Joanna Wieprow and Agnieszka Gawlik

Risks 2021, 9(4), 78; https://doi.org/10.3390/risks9040078 - 16 Apr 2021

Cited by 23 | Viewed by 4301

Abstract

The aim of this article is to use multiple discriminant analysis (MDA) and logit models to assess the risk of bankruptcy of companies in the Polish tourism sector in the crisis conditions caused by the COVID-19 pandemic. A review of the literature is [...] Read more.

The aim of this article is to use multiple discriminant analysis (MDA) and logit models to assess the risk of bankruptcy of companies in the Polish tourism sector in the crisis conditions caused by the COVID-19 pandemic. A review of the literature is used to select models appropriate to analyze the risk of bankruptcy of tourism enterprises listed on the Warsaw Stock Exchange (WSE). The data are from half-year financial statements (the first half of 2019 and 2020, respectively). The obtained results are compared with the current values of the Altman EM-score model and selected financial ratios. An analysis allowed the estimation of the risk of bankruptcy of enterprises from the tourism sector in Poland as well as the assessment of the prognostic value of these models in the tourism sector and the risk of a collapse of this market in Poland. The article fills the research gap created by the negligible use of solvency analysis of the tourism sector and constitutes the basis for estimating the risk of collapse of the tourism sector in a crisis situation. Full article

(This article belongs to the Special Issue Risk in Contemporary Management)

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20 pages, 642 KiB

Open AccessEditor’s ChoiceArticle

A Machine Learning Approach for Micro-Credit Scoring

by Apostolos Ampountolas, Titus Nyarko Nde, Paresh Date and Corina Constantinescu

Risks 2021, 9(3), 50; https://doi.org/10.3390/risks9030050 - 9 Mar 2021

Cited by 37 | Viewed by 19263

Abstract

In micro-lending markets, lack of recorded credit history is a significant impediment to assessing individual borrowers’ creditworthiness and therefore deciding fair interest rates. This research compares various machine learning algorithms on real micro-lending data to test their efficacy at classifying borrowers into various [...] Read more.

In micro-lending markets, lack of recorded credit history is a significant impediment to assessing individual borrowers’ creditworthiness and therefore deciding fair interest rates. This research compares various machine learning algorithms on real micro-lending data to test their efficacy at classifying borrowers into various credit categories. We demonstrate that off-the-shelf multi-class classifiers such as random forest algorithms can perform this task very well, using readily available data about customers (such as age, occupation, and location). This presents inexpensive and reliable means to micro-lending institutions around the developing world with which to assess creditworthiness in the absence of credit history or central credit databases. Full article

(This article belongs to the Special Issue Interplay between Financial and Actuarial Mathematics)

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19 pages, 530 KiB

Open AccessEditor’s ChoiceArticle

Mortality Forecasting with an Age-Coherent Sparse VAR Model

by Hong Li and Yanlin Shi

Risks 2021, 9(2), 35; https://doi.org/10.3390/risks9020035 - 5 Feb 2021

Cited by 13 | Viewed by 3393

Abstract

This paper proposes an age-coherent sparse Vector Autoregression mortality model, which combines the appealing features of existing VAR-based mortality models, to forecast future mortality rates. In particular, the proposed model utilizes a data-driven method to determine the autoregressive coefficient matrix, and then employs [...] Read more.

This paper proposes an age-coherent sparse Vector Autoregression mortality model, which combines the appealing features of existing VAR-based mortality models, to forecast future mortality rates. In particular, the proposed model utilizes a data-driven method to determine the autoregressive coefficient matrix, and then employs a rotation algorithm in the projection phase to generate age-coherent mortality forecasts. In the estimation phase, the age-specific mortality improvement rates are fitted to a VAR model with dimension reduction algorithms such as the elastic net. In the projection phase, the projected mortality improvement rates are assumed to follow a short-term fluctuation component and a long-term force of decay, and will eventually converge to an age-invariant mean in expectation. The age-invariance of the long-term mean guarantees age-coherent mortality projections. The proposed model is generalized to multi-population context in a computationally efficient manner. Using single-age, uni-sex mortality data of the UK and France, we show that the proposed model is able to generate more reasonable long-term projections, as well as more accurate short-term out-of-sample forecasts than popular existing mortality models under various settings. Therefore, the proposed model is expected to be an appealing alternative to existing mortality models in insurance and demographic analyses. Full article

(This article belongs to the Special Issue Mortality Forecasting and Applications)

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17 pages, 3439 KiB

Open AccessEditor’s ChoiceArticle

An Expectation-Maximization Algorithm for the Exponential-Generalized Inverse Gaussian Regression Model with Varying Dispersion and Shape for Modelling the Aggregate Claim Amount

by George Tzougas and Himchan Jeong

Risks 2021, 9(1), 19; https://doi.org/10.3390/risks9010019 - 8 Jan 2021

Cited by 8 | Viewed by 2729

Abstract

This article presents the Exponential–Generalized Inverse Gaussian regression model with varying dispersion and shape. The EGIG is a general distribution family which, under the adopted modelling framework, can provide the appropriate level of flexibility to fit moderate costs with high frequencies and heavy-tailed [...] Read more.

This article presents the Exponential–Generalized Inverse Gaussian regression model with varying dispersion and shape. The EGIG is a general distribution family which, under the adopted modelling framework, can provide the appropriate level of flexibility to fit moderate costs with high frequencies and heavy-tailed claim sizes, as they both represent significant proportions of the total loss in non-life insurance. The model’s implementation is illustrated by a real data application which involves fitting claim size data from a European motor insurer. The maximum likelihood estimation of the model parameters is achieved through a novel Expectation Maximization (EM)-type algorithm that is computationally tractable and is demonstrated to perform satisfactorily. Full article

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16 pages, 537 KiB

Open AccessEditor’s ChoiceReview

Supply Chain Risk Management: Literature Review

by Amulya Gurtu and Jestin Johny

Risks 2021, 9(1), 16; https://doi.org/10.3390/risks9010016 - 6 Jan 2021

Cited by 141 | Viewed by 87065

Abstract

The risks associated with global supply chain management has created a discourse among practitioners and academics. This is evident by the business uncertainties growing in supply chain management, which pose threats to the entire network flow and economy. This paper aims to review [...] Read more.

The risks associated with global supply chain management has created a discourse among practitioners and academics. This is evident by the business uncertainties growing in supply chain management, which pose threats to the entire network flow and economy. This paper aims to review the existing literature on risk factors in supply chain management in an uncertain and competitive business environment. Papers that contained the word “risk” in their titles, keywords, or abstracts were selected for conducting the theoretical analyses. Supply chain risk management is an integral function of the supply network. It faces unpredictable challenges due to nations’ economic policies and globalization, which have raised uncertainty and challenges for supply chain organizations. These significantly affect the financial performance of the organizations and the economy of a nation. Debate on supply chain risk management may promote competitiveness in business. Risk mitigation strategies will reduce the impact caused due to natural and human-made disasters. Full article

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26 pages, 657 KiB

Open AccessEditor’s ChoiceReview

Machine Learning in P&C Insurance: A Review for Pricing and Reserving

by Christopher Blier-Wong, Hélène Cossette, Luc Lamontagne and Etienne Marceau

Risks 2021, 9(1), 4; https://doi.org/10.3390/risks9010004 - 23 Dec 2020

Cited by 42 | Viewed by 18653

Abstract

In the past 25 years, computer scientists and statisticians developed machine learning algorithms capable of modeling highly nonlinear transformations and interactions of input features. While actuaries use GLMs frequently in practice, only in the past few years have they begun studying these newer [...] Read more.

In the past 25 years, computer scientists and statisticians developed machine learning algorithms capable of modeling highly nonlinear transformations and interactions of input features. While actuaries use GLMs frequently in practice, only in the past few years have they begun studying these newer algorithms to tackle insurance-related tasks. In this work, we aim to review the applications of machine learning to the actuarial science field and present the current state of the art in ratemaking and reserving. We first give an overview of neural networks, then briefly outline applications of machine learning algorithms in actuarial science tasks. Finally, we summarize the future trends of machine learning for the insurance industry. Full article

(This article belongs to the Special Issue Data Mining in Actuarial Science: Theory and Applications)

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9 pages, 479 KiB

Open AccessEditor’s ChoiceArticle

Why to Buy Insurance? An Explainable Artificial Intelligence Approach

by Alex Gramegna and Paolo Giudici

Risks 2020, 8(4), 137; https://doi.org/10.3390/risks8040137 - 14 Dec 2020

Cited by 41 | Viewed by 6246

Abstract

We propose an Explainable AI model that can be employed in order to explain why a customer buys or abandons a non-life insurance coverage. The method consists in applying similarity clustering to the Shapley values that were obtained from a highly accurate XGBoost [...] Read more.

We propose an Explainable AI model that can be employed in order to explain why a customer buys or abandons a non-life insurance coverage. The method consists in applying similarity clustering to the Shapley values that were obtained from a highly accurate XGBoost predictive classification algorithm. Our proposed method can be embedded into a technologically-based insurance service (Insurtech), allowing to understand, in real time, the factors that most contribute to customers’ decisions, thereby gaining proactive insights on their needs. We prove the validity of our model with an empirical analysis that was conducted on data regarding purchases of insurance micro-policies. Two aspects are investigated: the propensity to buy an insurance policy and the risk of churn of an existing customer. The results from the analysis reveal that customers can be effectively and quickly grouped according to a similar set of characteristics, which can predict their buying or churn behaviour well. Full article

(This article belongs to the Special Issue Financial Networks in Fintech Risk Management II)

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18 pages, 585 KiB

Open AccessEditor’s ChoiceArticle

A Deep Neural Network Algorithm for Semilinear Elliptic PDEs with Applications in Insurance Mathematics

by Stefan Kremsner, Alexander Steinicke and Michaela Szölgyenyi

Risks 2020, 8(4), 136; https://doi.org/10.3390/risks8040136 - 9 Dec 2020

Cited by 16 | Viewed by 4423

Abstract

In insurance mathematics, optimal control problems over an infinite time horizon arise when computing risk measures. An example of such a risk measure is the expected discounted future dividend payments. In models which take multiple economic factors into account, this problem is high-dimensional. [...] Read more.

In insurance mathematics, optimal control problems over an infinite time horizon arise when computing risk measures. An example of such a risk measure is the expected discounted future dividend payments. In models which take multiple economic factors into account, this problem is high-dimensional. The solutions to such control problems correspond to solutions of deterministic semilinear (degenerate) elliptic partial differential equations. In the present paper we propose a novel deep neural network algorithm for solving such partial differential equations in high dimensions in order to be able to compute the proposed risk measure in a complex high-dimensional economic environment. The method is based on the correspondence of elliptic partial differential equations to backward stochastic differential equations with unbounded random terminal time. In particular, backward stochastic differential equations—which can be identified with solutions of elliptic partial differential equations—are approximated by means of deep neural networks. Full article

(This article belongs to the Special Issue Computational Finance and Risk Analysis in Insurance)

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21 pages, 552 KiB

Open AccessEditor’s ChoiceArticle

Price Formation and Optimal Trading in Intraday Electricity Markets with a Major Player

by Olivier Féron, Peter Tankov and Laura Tinsi

Risks 2020, 8(4), 133; https://doi.org/10.3390/risks8040133 - 7 Dec 2020

Cited by 15 | Viewed by 3885

Abstract

We study price formation in intraday electricity markets in the presence of intermittent renewable generation. We consider the setting where a major producer may interact strategically with a large number of small producers. Using stochastic control theory, we identify the optimal strategies of [...] Read more.

We study price formation in intraday electricity markets in the presence of intermittent renewable generation. We consider the setting where a major producer may interact strategically with a large number of small producers. Using stochastic control theory, we identify the optimal strategies of agents with market impact and exhibit the Nash equilibrium in a closed form in the asymptotic framework of mean field games with a major player. Full article

(This article belongs to the Special Issue Stochastic Modeling and Pricing in Energy Markets)

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31 pages, 2911 KiB

Open AccessEditor’s ChoiceArticle

A Generative Adversarial Network Approach to Calibration of Local Stochastic Volatility Models

by Christa Cuchiero, Wahid Khosrawi and Josef Teichmann

Risks 2020, 8(4), 101; https://doi.org/10.3390/risks8040101 - 27 Sep 2020

Cited by 38 | Viewed by 6840

Abstract

We propose a fully data-driven approach to calibrate local stochastic volatility (LSV) models, circumventing in particular the ad hoc interpolation of the volatility surface. To achieve this, we parametrize the leverage function by a family of feed-forward neural networks and learn their parameters [...] Read more.

We propose a fully data-driven approach to calibrate local stochastic volatility (LSV) models, circumventing in particular the ad hoc interpolation of the volatility surface. To achieve this, we parametrize the leverage function by a family of feed-forward neural networks and learn their parameters directly from the available market option prices. This should be seen in the context of neural SDEs and (causal) generative adversarial networks: we generate volatility surfaces by specific neural SDEs, whose quality is assessed by quantifying, possibly in an adversarial manner, distances to market prices. The minimization of the calibration functional relies strongly on a variance reduction technique based on hedging and deep hedging, which is interesting in its own right: it allows the calculation of model prices and model implied volatilities in an accurate way using only small sets of sample paths. For numerical illustration we implement a SABR-type LSV model and conduct a thorough statistical performance analysis on many samples of implied volatility smiles, showing the accuracy and stability of the method. Full article

(This article belongs to the Special Issue Machine Learning in Finance, Insurance and Risk Management)

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Graphical abstract

23 pages, 673 KiB

Open AccessEditor’s ChoiceArticle

EM Estimation for the Poisson-Inverse Gamma Regression Model with Varying Dispersion: An Application to Insurance Ratemaking

by George Tzougas

Risks 2020, 8(3), 97; https://doi.org/10.3390/risks8030097 - 11 Sep 2020

Cited by 16 | Viewed by 4772

Abstract

This article presents the Poisson-Inverse Gamma regression model with varying dispersion for approximating heavy-tailed and overdispersed claim counts. Our main contribution is that we develop an Expectation-Maximization (EM) type algorithm for maximum likelihood (ML) estimation of the Poisson-Inverse Gamma regression model with varying [...] Read more.

This article presents the Poisson-Inverse Gamma regression model with varying dispersion for approximating heavy-tailed and overdispersed claim counts. Our main contribution is that we develop an Expectation-Maximization (EM) type algorithm for maximum likelihood (ML) estimation of the Poisson-Inverse Gamma regression model with varying dispersion. The empirical analysis examines a portfolio of motor insurance data in order to investigate the efficiency of the proposed algorithm. Finally, both the a priori and a posteriori, or Bonus-Malus, premium rates that are determined by the Poisson-Inverse Gamma model are compared to those that result from the classic Negative Binomial Type I and the Poisson-Inverse Gaussian distributions with regression structures for their mean and dispersion parameters. Full article

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26 pages, 1640 KiB

Open AccessEditor’s ChoiceArticle

Nagging Predictors

by Ronald Richman and Mario V. Wüthrich

Risks 2020, 8(3), 83; https://doi.org/10.3390/risks8030083 - 4 Aug 2020

Cited by 44 | Viewed by 5657

Abstract

We define the nagging predictor, which, instead of using bootstrapping to produce a series of i.i.d. predictors, exploits the randomness of neural network calibrations to provide a more stable and accurate predictor than is available from a single neural network run. Convergence results [...] Read more.

We define the nagging predictor, which, instead of using bootstrapping to produce a series of i.i.d. predictors, exploits the randomness of neural network calibrations to provide a more stable and accurate predictor than is available from a single neural network run. Convergence results for the family of Tweedie’s compound Poisson models, which are usually used for general insurance pricing, are provided. In the context of a French motor third-party liability insurance example, the nagging predictor achieves stability at portfolio level after about 20 runs. At an insurance policy level, we show that for some policies up to 400 neural network runs are required to achieve stability. Since working with 400 neural networks is impractical, we calibrate two meta models to the nagging predictor, one unweighted, and one using the coefficient of variation of the nagging predictor as a weight, finding that these latter meta networks can approximate the nagging predictor well, only with a small loss of accuracy. Full article

(This article belongs to the Special Issue Computational Finance and Risk Analysis in Insurance)

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19 pages, 1053 KiB

Open AccessEditor’s ChoiceArticle

Neural Networks and Betting Strategies for Tennis

by Vincenzo Candila and Lucio Palazzo

Risks 2020, 8(3), 68; https://doi.org/10.3390/risks8030068 - 29 Jun 2020

Cited by 18 | Viewed by 7593

Abstract

Recently, the interest of the academic literature on sports statistics has increased enormously. In such a framework, two of the most significant challenges are developing a model able to beat the existing approaches and, within a betting market framework, guarantee superior returns than [...] Read more.

Recently, the interest of the academic literature on sports statistics has increased enormously. In such a framework, two of the most significant challenges are developing a model able to beat the existing approaches and, within a betting market framework, guarantee superior returns than the set of competing specifications considered. This contribution attempts to achieve both these results, in the context of male tennis. In tennis, several approaches to predict the winner are available, among which the regression-based, point-based and paired comparison of the competitors’ abilities play a significant role. Contrary to the existing approaches, this contribution employs artificial neural networks (ANNs) to forecast the probability of winning in tennis matches, starting from all the variables used in a large selection of the previous methods. From an out-of-sample perspective, the implemented ANN model outperforms four out of five competing models, independently of the considered period. For what concerns the betting perspective, we propose four different strategies. The resulting returns on investment obtained from the ANN appear to be more broad and robust than those obtained from the best competing model, irrespective of the betting strategy adopted. Full article

(This article belongs to the Special Issue Risks in Gambling)

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21 pages, 2027 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Heads and Tails of Earnings Management: Quantitative Analysis in Emerging Countries

by Pavol Durana, Katarina Valaskova, Darina Chlebikova, Vladislav Krastev and Irina Atanasova

Risks 2020, 8(2), 57; https://doi.org/10.3390/risks8020057 - 1 Jun 2020

Cited by 16 | Viewed by 5225

Abstract

Earnings management is a globally used tool for long-term profitable enterprises and for the apparatus of reduction of bankruptcy risk in developed countries. This phenomenon belongs to the integral and fundamental part of their business finance. However, this has still been lax in [...] Read more.

Earnings management is a globally used tool for long-term profitable enterprises and for the apparatus of reduction of bankruptcy risk in developed countries. This phenomenon belongs to the integral and fundamental part of their business finance. However, this has still been lax in emerging countries. The models of detections of the existence of earnings management are based on discretionary accrual. The goal of this article is to detect the existence of earnings management in emerging countries by times series analysis. This econometric investigation uses the observations of earnings before interest and taxes of 1089 Slovak enterprises and 1421 Bulgarian enterprises in financial modelling. Our findings confirm the significant existence of earnings management in both analyzed countries, based on a quantitative analysis of unit root and stationarity. The managerial activities are purposeful, which is proven by the existence of no stationarity in the time series and a clear occurrence of the unit root. In addition, the results highlight the year 2014 as a significant milestone of change in the development of earnings management in both countries, based on homogeneity analyses. These facts identify significant parallels between Slovak and Bulgarian economics and business finance. Full article

(This article belongs to the Special Issue Quantitative Methods in Economics and Finance)

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16 pages, 499 KiB

Open AccessEditor’s ChoiceArticle

Mean-Variance Optimization Is a Good Choice, But for Other Reasons than You Might Think

by Andrea Rigamonti

Risks 2020, 8(1), 29; https://doi.org/10.3390/risks8010029 - 14 Mar 2020

Cited by 13 | Viewed by 9051

Abstract

Mean-variance portfolio optimization is more popular than optimization procedures that employ downside risk measures such as the semivariance, despite the latter being more in line with the preferences of a rational investor. We describe strengths and weaknesses of semivariance and how to minimize [...] Read more.

Mean-variance portfolio optimization is more popular than optimization procedures that employ downside risk measures such as the semivariance, despite the latter being more in line with the preferences of a rational investor. We describe strengths and weaknesses of semivariance and how to minimize it for asset allocation decisions. We then apply this approach to a variety of simulated and real data and show that the traditional approach based on the variance generally outperforms it. The results hold even if the CVaR is used, because all downside risk measures are difficult to estimate. The popularity of variance as a measure of risk appears therefore to be rationally justified. Full article

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18 pages, 452 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Rational Savings Account Models for Backward-Looking Interest Rate Benchmarks

by Andrea Macrina and David Skovmand

Risks 2020, 8(1), 23; https://doi.org/10.3390/risks8010023 - 3 Mar 2020

Cited by 10 | Viewed by 4184

Abstract

Interest rate benchmarks are currently undergoing a major transition. The LIBOR benchmark is planned to be discontinued by the end of 2021 and superseded by what ISDA calls an adjusted risk-free rate (RFR). ISDA has recently announced that the LIBOR replacement will most [...] Read more.

Interest rate benchmarks are currently undergoing a major transition. The LIBOR benchmark is planned to be discontinued by the end of 2021 and superseded by what ISDA calls an adjusted risk-free rate (RFR). ISDA has recently announced that the LIBOR replacement will most likely be constructed from a compounded running average of RFR overnight rates over a period matching the LIBOR tenor. This new backward-looking benchmark is markedly different when compared with LIBOR. It is measurable only at the end of the term in contrast to the forward-looking LIBOR, which is measurable at the start of the term. The RFR provides a simplification because the cash flows and the discount factors may be derived from the same discounting curve, thus avoiding—on a superficial level—any multi-curve complications. We develop a new class of savings account models and derive a novel interest rate system specifically designed to facilitate a high degree of tractability for the pricing of RFR-based fixed-income instruments. The rational form of the savings account models under the risk-neutral measure enables the pricing in closed form of caplets, swaptions and futures written on the backward-looking interest rate benchmark. Full article

(This article belongs to the Special Issue Interest Rate Risk Modelling in Transformation)

27 pages, 2013 KiB

Open AccessEditor’s ChoiceArticle

Prediction of Claims in Export Credit Finance: A Comparison of Four Machine Learning Techniques

by Mathias Bärtl and Simone Krummaker

Risks 2020, 8(1), 22; https://doi.org/10.3390/risks8010022 - 1 Mar 2020

Cited by 20 | Viewed by 10978

Abstract

This study evaluates four machine learning (ML) techniques (Decision Trees (DT), Random Forests (RF), Neural Networks (NN) and Probabilistic Neural Networks (PNN)) on their ability to accurately predict export credit insurance claims. Additionally, we compare the performance of the ML techniques against a [...] Read more.

This study evaluates four machine learning (ML) techniques (Decision Trees (DT), Random Forests (RF), Neural Networks (NN) and Probabilistic Neural Networks (PNN)) on their ability to accurately predict export credit insurance claims. Additionally, we compare the performance of the ML techniques against a simple benchmark (BM) heuristic. The analysis is based on the utilisation of a dataset provided by the Berne Union, which is the most comprehensive collection of export credit insurance data and has been used in only two scientific studies so far. All ML techniques performed relatively well in predicting whether or not claims would be incurred, and, with limitations, in predicting the order of magnitude of the claims. No satisfactory results were achieved predicting actual claim ratios. RF performed significantly better than DT, NN and PNN against all prediction tasks, and most reliably carried their validation performance forward to test performance. Full article

(This article belongs to the Special Issue Machine Learning in Insurance)

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79 pages, 1797 KiB

Open AccessEditor’s ChoiceArticle

Machine Learning in Least-Squares Monte Carlo Proxy Modeling of Life Insurance Companies

by Anne-Sophie Krah, Zoran Nikolić and Ralf Korn

Risks 2020, 8(1), 21; https://doi.org/10.3390/risks8010021 - 21 Feb 2020

Cited by 12 | Viewed by 7091

Abstract

Under the Solvency II regime, life insurance companies are asked to derive their solvency capital requirements from the full loss distributions over the coming year. Since the industry is currently far from being endowed with sufficient computational capacities to fully simulate these distributions, [...] Read more.

Under the Solvency II regime, life insurance companies are asked to derive their solvency capital requirements from the full loss distributions over the coming year. Since the industry is currently far from being endowed with sufficient computational capacities to fully simulate these distributions, the insurers have to rely on suitable approximation techniques such as the least-squares Monte Carlo (LSMC) method. The key idea of LSMC is to run only a few wisely selected simulations and to process their output further to obtain a risk-dependent proxy function of the loss. In this paper, we present and analyze various adaptive machine learning approaches that can take over the proxy modeling task. The studied approaches range from ordinary and generalized least-squares regression variants over generalized linear model (GLM) and generalized additive model (GAM) methods to multivariate adaptive regression splines (MARS) and kernel regression routines. We justify the combinability of their regression ingredients in a theoretical discourse. Further, we illustrate the approaches in slightly disguised real-world experiments and perform comprehensive out-of-sample tests. Full article

(This article belongs to the Special Issue Machine Learning in Insurance)

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