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Open AccessEditorial

Special Issue “Risks: Feature Papers 2021”

by Mogens Steffensen

Risks 2022, 10(3), 64; https://doi.org/10.3390/risks10030064 - 11 Mar 2022

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Abstract

The 2021 Feature Papers Special Issue is a list of high-quality research output that shows the width and the breadth of the journal Risks [...] Full article

(This article belongs to the Special Issue Risks: Feature Papers 2021)

Research

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13 pages, 10994 KiB

Open AccessArticle

Interpolation of Quantile Regression to Estimate Driver’s Risk of Traffic Accident Based on Excess Speed

by Albert Pitarque and Montserrat Guillen

Risks 2022, 10(1), 19; https://doi.org/10.3390/risks10010019 - 12 Jan 2022

Cited by 3 | Viewed by 2476

Abstract

Quantile regression provides a way to estimate a driver’s risk of a traffic accident by means of predicting the percentile of observed distance driven above the legal speed limits over a one year time interval, conditional on some given characteristics such as total [...] Read more.

Quantile regression provides a way to estimate a driver’s risk of a traffic accident by means of predicting the percentile of observed distance driven above the legal speed limits over a one year time interval, conditional on some given characteristics such as total distance driven, age, gender, percent of urban zone driving and night time driving. This study proposes an approximation of quantile regression coefficients by interpolating only a few quantile levels, which can be chosen carefully from the unconditional empirical distribution function of the response. Choosing the levels before interpolation improves accuracy. This approximation method is convenient for real-time implementation of risky driving identification and provides a fast approximate calculation of a risk score. We illustrate our results with data on 9614 drivers observed over one year. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2021)

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23 pages, 692 KiB

Open AccessEditor’s ChoiceArticle

ESG-Washing in the Mutual Funds Industry? From Information Asymmetry to Regulation

by Bertrand Candelon, Jean-Baptiste Hasse and Quentin Lajaunie

Risks 2021, 9(11), 199; https://doi.org/10.3390/risks9110199 - 5 Nov 2021

Cited by 11 | Viewed by 9589

Abstract

In this paper, we study the asymmetric information between asset managers and investors in the socially responsible investment (SRI) market. Specifically, we investigate the lack of transparency of the extra-financial information communicated by asset managers. Using a unique international panel dataset of approximately [...] Read more.

In this paper, we study the asymmetric information between asset managers and investors in the socially responsible investment (SRI) market. Specifically, we investigate the lack of transparency of the extra-financial information communicated by asset managers. Using a unique international panel dataset of approximately 1500 equity mutual funds, we provide empirical evidence that some asset managers portray themselves as socially responsible yet do not make tangible investment decisions. Furthermore, our results indicate that the financial performance of mutual funds is not related to asset managers’ signals but should be evaluated relatively using extra-financial ratings. In summary, our findings advocate for a unified regulation framework that constrains asset managers’ communication. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2021)

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23 pages, 1078 KiB

Open AccessArticle

Mean-Reverting 4/2 Principal Components Model. Financial Applications

by Marcos Escobar-Anel and Zhenxian Gong

Risks 2021, 9(8), 141; https://doi.org/10.3390/risks9080141 - 27 Jul 2021

Cited by 1 | Viewed by 2521

Abstract

In this paper, we propose a new multivariate mean-reverting model incorporating state-of-the art 4/2 stochastic volatility and a convenient principal component stochastic volatility (PCSV) decomposition for the stochastic covariance. We find a quasi closed-form characteristic function and propose analytic approximations, which aid in [...] Read more.

In this paper, we propose a new multivariate mean-reverting model incorporating state-of-the art 4/2 stochastic volatility and a convenient principal component stochastic volatility (PCSV) decomposition for the stochastic covariance. We find a quasi closed-form characteristic function and propose analytic approximations, which aid in the pricing of derivatives and calculation of risk measures. Parameters are estimated on three bivariate series, using a two-stage methodology involving method of moments and least squares. Moreover, a scaling factor is added for extra degrees of freedom to match data features. As an application, we consider investment strategies for a portfolio with two risky assets and a risk-free cash account. We calculate value-at-risk (VaR) values at a 95% risk level using both simulation-based and distribution-based methods. A comparison of these VaR values supports the effectiveness of our approximations and the potential for higher dimensions. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2021)

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21 pages, 825 KiB

Open AccessArticle

Improving Explainability of Major Risk Factors in Artificial Neural Networks for Auto Insurance Rate Regulation

by Shengkun Xie

Risks 2021, 9(7), 126; https://doi.org/10.3390/risks9070126 - 2 Jul 2021

Cited by 5 | Viewed by 3164

Abstract

In insurance rate-making, the use of statistical machine learning techniques such as artificial neural networks (ANN) is an emerging approach, and many insurance companies have been using them for pricing. However, due to the complexity of model specification and its implementation, model explainability [...] Read more.

In insurance rate-making, the use of statistical machine learning techniques such as artificial neural networks (ANN) is an emerging approach, and many insurance companies have been using them for pricing. However, due to the complexity of model specification and its implementation, model explainability may be essential to meet insurance pricing transparency for rate regulation purposes. This requirement may imply the need for estimating or evaluating the variable importance when complicated models are used. Furthermore, from both rate-making and rate-regulation perspectives, it is critical to investigate the impact of major risk factors on the response variables, such as claim frequency or claim severity. In this work, we consider the modelling problems of how claim counts, claim amounts and average loss per claim are related to major risk factors. ANN models are applied to meet this goal, and variable importance is measured to improve the model’s explainability due to the models’ complex nature. The results obtained from different variable importance measurements are compared, and dominant risk factors are identified. The contribution of this work is in making advanced mathematical models possible for applications in auto insurance rate regulation. This study focuses on analyzing major risks only, but the proposed method can be applied to more general insurance pricing problems when additional risk factors are being considered. In addition, the proposed methodology is useful for other business applications where statistical machine learning techniques are used. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2021)

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25 pages, 634 KiB

Open AccessArticle

A Finite Mixture Modelling Perspective for Combining Experts’ Opinions with an Application to Quantile-Based Risk Measures

by Despoina Makariou, Pauline Barrieu and George Tzougas

Risks 2021, 9(6), 115; https://doi.org/10.3390/risks9060115 - 9 Jun 2021

Cited by 1 | Viewed by 3388

Abstract

The key purpose of this paper is to present an alternative viewpoint for combining expert opinions based on finite mixture models. Moreover, we consider that the components of the mixture are not necessarily assumed to be from the same parametric family. This approach [...] Read more.

The key purpose of this paper is to present an alternative viewpoint for combining expert opinions based on finite mixture models. Moreover, we consider that the components of the mixture are not necessarily assumed to be from the same parametric family. This approach can enable the agent to make informed decisions about the uncertain quantity of interest in a flexible manner that accounts for multiple sources of heterogeneity involved in the opinions expressed by the experts in terms of the parametric family, the parameters of each component density, and also the mixing weights. Finally, the proposed models are employed for numerically computing quantile-based risk measures in a collective decision-making context. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2021)

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12 pages, 671 KiB

Open AccessArticle

Financial Distress and Information Sharing: Evidences from the Italian Credit Register

by Lucia Gibilaro and Gianluca Mattarocci

Risks 2021, 9(5), 94; https://doi.org/10.3390/risks9050094 - 12 May 2021

Cited by 2 | Viewed by 2328

Abstract

Credit risk exposure evaluation is driven by the quality of the information available on the debtors and customers with multiple lending exposures, which could be evaluated differently by different lenders. The existence of an information asymmetry among lenders can be mitigated using private [...] Read more.

Credit risk exposure evaluation is driven by the quality of the information available on the debtors and customers with multiple lending exposures, which could be evaluated differently by different lenders. The existence of an information asymmetry among lenders can be mitigated using private information sharing instruments, such as the credit registers. The paper analyses the effect of information disclosure through credit registers and evaluates the impact of revising the amount of credit offered to customers served also by other lenders. The results show that the information available for each lender is different and after the disclosure of past due or a default status declared by a financial intermediary, all the other lenders react to the new information available. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2021)

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23 pages, 648 KiB

Open AccessArticle

Nonparametric Estimation of Extreme Quantiles with an Application to Longevity Risk

by Catalina Bolancé and Montserrat Guillen

Risks 2021, 9(4), 77; https://doi.org/10.3390/risks9040077 - 15 Apr 2021

Cited by 7 | Viewed by 2607

Abstract

A new method to estimate longevity risk based on the kernel estimation of the extreme quantiles of truncated age-at-death distributions is proposed. Its theoretical properties are presented and a simulation study is reported. The flexible yet accurate estimation of extreme quantiles of age-at-death [...] Read more.

A new method to estimate longevity risk based on the kernel estimation of the extreme quantiles of truncated age-at-death distributions is proposed. Its theoretical properties are presented and a simulation study is reported. The flexible yet accurate estimation of extreme quantiles of age-at-death conditional on having survived a certain age is fundamental for evaluating the risk of lifetime insurance. Our proposal combines a parametric distributions with nonparametric sample information, leading to obtain an asymptotic unbiased estimator of extreme quantiles for alternative distributions with different right tail shape, i.e., heavy tail or exponential tail. A method for estimating the longevity risk of a continuous temporary annuity is also shown. We illustrate our proposal with an application to the official age-at-death statistics of the population in Spain. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2021)

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24 pages, 788 KiB

Open AccessArticle

Matrix-Tilted Archimedean Copulas

by Marius Hofert and Johanna F. Ziegel

Risks 2021, 9(4), 68; https://doi.org/10.3390/risks9040068 - 6 Apr 2021

Cited by 1 | Viewed by 2745

Abstract

The new class of matrix-tilted Archimedean copulas is introduced. It combines properties of Archimedean and elliptical copulas by introducing a tilting matrix in the stochastic representation of Archimedean copulas, similar to the Cholesky factor for elliptical copulas. Basic properties of this copula construction [...] Read more.

The new class of matrix-tilted Archimedean copulas is introduced. It combines properties of Archimedean and elliptical copulas by introducing a tilting matrix in the stochastic representation of Archimedean copulas, similar to the Cholesky factor for elliptical copulas. Basic properties of this copula construction are discussed and a further extension outlined. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2021)

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14 pages, 899 KiB

Open AccessArticle

Short-Term Price Reaction to Filing for Bankruptcy and Restructuring Proceedings—The Case of Poland

by Błażej Prusak and Marcin Potrykus

Risks 2021, 9(3), 56; https://doi.org/10.3390/risks9030056 - 18 Mar 2021

Cited by 5 | Viewed by 2884

Abstract

This study aims to check market reaction to filing for bankruptcy and restructuring proceedings and to verify the short-term effect of a price reversal in the Polish market in the years 2004–2019. The research was conducted by dividing the analysed companies according to [...] Read more.

This study aims to check market reaction to filing for bankruptcy and restructuring proceedings and to verify the short-term effect of a price reversal in the Polish market in the years 2004–2019. The research was conducted by dividing the analysed companies according to the procedure (bankruptcy and restructuring) and market (the main market and the NewConnect market). The research methodology used in the study is the event analysis method (AR, CAR, AAR and CAAR rates were used in the research), with a few statistical tests (T-test, Generalized rank Z Test, Generalized rank T-Test, Patell or Standardized Residual Test, Kolari and Pynnönen adjusted Patell or Standardized Residual Test). It was found that share prices in the Polish share market react quickly to public information about filing an application for bankruptcy or restructuring. For all analysed companies, the mean rate of return on the event day was equal to −14%, and on the next day, it was −3%. Regardless of the type of share market and the form of proceedings, the reversal effect was not confirmed in the short term. It was found that cumulative above-average rates of return fall more strongly for companies listed on the less liquid Newconnect market (−23.6%), and when information on the filing for bankruptcy proceedings is provided (−28.5%), as opposed to the main market (−19.1%) and restructuring proceedings (−17%). The cumulative average rate of return for all analysed companies in the research period (−2, +10 days) was equal to −20.6%. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2021)

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28 pages, 15391 KiB

Open AccessArticle

Assessing the Performance of Random Forests for Modeling Claim Severity in Collision Car Insurance

by Yves Staudt and Joël Wagner

Risks 2021, 9(3), 53; https://doi.org/10.3390/risks9030053 - 16 Mar 2021

Cited by 11 | Viewed by 6065

Abstract

For calculating non-life insurance premiums, actuaries traditionally rely on separate severity and frequency models using covariates to explain the claims loss exposure. In this paper, we focus on the claim severity. First, we build two reference models, a generalized linear model and a [...] Read more.

For calculating non-life insurance premiums, actuaries traditionally rely on separate severity and frequency models using covariates to explain the claims loss exposure. In this paper, we focus on the claim severity. First, we build two reference models, a generalized linear model and a generalized additive model, relying on a log-normal distribution of the severity and including the most significant factors. Thereby, we relate the continuous variables to the response in a nonlinear way. In the second step, we tune two random forest models, one for the claim severity and one for the log-transformed claim severity, where the latter requires a transformation of the predicted results. We compare the prediction performance of the different models using the relative error, the root mean squared error and the goodness-of-lift statistics in combination with goodness-of-fit statistics. In our application, we rely on a dataset of a Swiss collision insurance portfolio covering the loss exposure of the period from 2011 to 2015, and including observations from 81 309 settled claims with a total amount of CHF 184 mio. In the analysis, we use the data from 2011 to 2014 for training and from 2015 for testing. Our results indicate that the use of a log-normal transformation of the severity is not leading to performance gains with random forests. However, random forests with a log-normal transformation are the favorite choice for explaining right-skewed claims. Finally, when considering all indicators, we conclude that the generalized additive model has the best overall performance. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2021)

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