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Volume 9, January

Risks, Volume 9, Issue 2 (February 2021) – 17 articles

Cover Story (view full-size image): This study found the most important variables that represent the future projections of the Bank of International Settlements’ capital adequacy ratio, which is the index of financial soundness in a bank as a comprehensive measure of capital adequacy. By using machine learning techniques, this study found 38 most important variables for representing the BIS capital adequacy ratio among 1929 variables. View this paper.
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Article
A Two-Population Mortality Model to Assess Longevity Basis Risk
Risks 2021, 9(2), 44; https://doi.org/10.3390/risks9020044 - 20 Feb 2021
Cited by 1 | Viewed by 1139
Abstract
Index-based hedging solutions are used to transfer the longevity risk to the capital markets. However, mismatches between the liability of the hedger and the hedging instrument cause longevity basis risk. Therefore, an appropriate two-population model to measure and assess longevity basis risk is [...] Read more.
Index-based hedging solutions are used to transfer the longevity risk to the capital markets. However, mismatches between the liability of the hedger and the hedging instrument cause longevity basis risk. Therefore, an appropriate two-population model to measure and assess longevity basis risk is required. In this paper, we aim to construct a two-population mortality model to provide an effective hedge against the basis risk. The reference population is modelled by using the Lee–Carter model with the renewal process and exponential jumps, and the dynamics of the book population are specified. The analysis based on the U.K. mortality data indicate that the proposed model for the reference population and the common age effect model for the book population provide a better fit compared to the other models considered in the paper. Different two-population models are used to investigate the impact of sampling risk on the index-based hedge, as well as to analyse the risk reduction regarding hedge effectiveness. The results show that the proposed model provides a significant risk reduction when mortality jumps and sampling risk are taken into account. Full article
(This article belongs to the Special Issue Interplay between Financial and Actuarial Mathematics)
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Article
Liquidity Synchronization, Its Determinants and Outcomes under Economic Growth Volatility: Evidence from Emerging Asian Economies
Risks 2021, 9(2), 43; https://doi.org/10.3390/risks9020043 - 20 Feb 2021
Cited by 5 | Viewed by 948
Abstract
This study investigates the country-level determinants of liquidity synchronization and degrees of liquidity synchronization during economic growth volatility. As a non-diversifiable risk factor, liquidity co-movement shock spreads market-wide and thus disrupts the overall functioning of the financial market. Firms in Asian markets operate [...] Read more.
This study investigates the country-level determinants of liquidity synchronization and degrees of liquidity synchronization during economic growth volatility. As a non-diversifiable risk factor, liquidity co-movement shock spreads market-wide and thus disrupts the overall functioning of the financial market. Firms in Asian markets operate in legal and regulatory environments distinct from those of firms analyzed in the previous literature. Comprehensive analyses of liquidity synchronicity in emerging markets are limited. A major knowledge gap pertaining to Asian emerging markets serves as the primary motivation for this study. Seven Asian emerging economies are selected from the MSCI emerging market index: Bangladesh, China, India, Indonesia, Malaysia, Pakistan and the Philippines for analysis from 2010 to 2019. The empirical findings show high levels of liquidity synchronicity in weaker economic and financial environments with low GDP growth, high inflation and interest rates and underdeveloped financial systems taking the form of low levels of private credit. Liquidity synchronicity is also affected by poor investor protection, political instability, weak rule of law and government ineffectiveness. Moreover, levels of liquidity synchronicity are higher in a period of economic growth volatility. Full article
Article
Machine Learning Approaches for Auto Insurance Big Data
Risks 2021, 9(2), 42; https://doi.org/10.3390/risks9020042 - 20 Feb 2021
Cited by 12 | Viewed by 3435
Abstract
The growing trend in the number and severity of auto insurance claims creates a need for new methods to efficiently handle these claims. Machine learning (ML) is one of the methods that solves this problem. As car insurers aim to improve their customer [...] Read more.
The growing trend in the number and severity of auto insurance claims creates a need for new methods to efficiently handle these claims. Machine learning (ML) is one of the methods that solves this problem. As car insurers aim to improve their customer service, these companies have started adopting and applying ML to enhance the interpretation and comprehension of their data for efficiency, thus improving their customer service through a better understanding of their needs. This study considers how automotive insurance providers incorporate machinery learning in their company, and explores how ML models can apply to insurance big data. We utilize various ML methods, such as logistic regression, XGBoost, random forest, decision trees, naïve Bayes, and K-NN, to predict claim occurrence. Furthermore, we evaluate and compare these models’ performances. The results showed that RF is better than other methods with the accuracy, kappa, and AUC values of 0.8677, 0.7117, and 0.840, respectively. Full article
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Article
Cardless Banking System in Malaysia: An Extended TAM
Risks 2021, 9(2), 41; https://doi.org/10.3390/risks9020041 - 15 Feb 2021
Cited by 1 | Viewed by 1492
Abstract
The main objective of this study is to analyse consumers’ behavioural intentions to use cardless banking technology in Malaysia. The intentions to use this technology are evaluated through an extended Technology Acceptance Model (TAM) framework. The data were collected from 447 Maybank and [...] Read more.
The main objective of this study is to analyse consumers’ behavioural intentions to use cardless banking technology in Malaysia. The intentions to use this technology are evaluated through an extended Technology Acceptance Model (TAM) framework. The data were collected from 447 Maybank and Hong Leong Bank customers in Selangor and Kuala Lumpur. The results show that self-efficacy (SE) had a positive impact on the perceived ease of use (PEOU), while perceived risk (PR) had a negative impact on perceived usefulness (PU) and intention to use (IU) cardless banking. Next, the perceived ease of use (PEOU) had a positive impact on perceived usefulness (PU). The results further support the idea that perceived usefulness (PU) and perceived ease of use (PEOU) had the strongest impacts on intention to use (IU). The practical implications of this study suggest that developers of cardless banking technology should introduce secure, less complicated, and easily accessible technology to improve consumers’ intentions to use. The perceived usefulness of this technology can be improved through promotional strategies and consumer training. Theoretically, this study has successfully extended TAM in the context of cardless banking technology in Malaysia. Moreover, this study will assist bankers in designing effective marketing strategies to attract more customers, which will add significant value to the overall business of the banking industry. Full article
(This article belongs to the Special Issue Economic and Financial Crimes)
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Article
Sensitivity of Performance Indexes to Disaster Risk
Risks 2021, 9(2), 40; https://doi.org/10.3390/risks9020040 - 13 Feb 2021
Viewed by 821
Abstract
We examine how sensitive the new performance indexes incorporating high moments and disaster risk are to disaster risk. The new performance indexes incorporating high moments and disaster risk are the Aumann-Serrano performance index and Foster-Hart performance index proposed by Kadan and Liu. These [...] Read more.
We examine how sensitive the new performance indexes incorporating high moments and disaster risk are to disaster risk. The new performance indexes incorporating high moments and disaster risk are the Aumann-Serrano performance index and Foster-Hart performance index proposed by Kadan and Liu. These performance indexes provide evaluations sensitive to the underlying risk. We show, by numerical examples and empirical examples, how sensitive these indexes are to disaster risk. Although these indexes are known to be either quite sensitive or excessively sensitive to disaster risk or maximum loss in the literature, we show by the regression analysis of the index and summary statistics these indexes are in fact not excessively sensitive to maximum loss in representative stock data, which contain disastrous observations. The numerical estimate of the Foster-Hart performance index is found to be effective in showing the performance index. Our analysis suggests these indexes can handle various empirical data containing quite disastrous observations. Full article
Article
Tail Risk and Extreme Events: Connections between Oil and Clean Energy
Risks 2021, 9(2), 39; https://doi.org/10.3390/risks9020039 - 11 Feb 2021
Cited by 3 | Viewed by 1038
Abstract
Do tail events in the oil market trigger extreme responses by the clean-energy financial market (and vice versa)? This paper investigates the relationship between oil price and clean-energy stock with a novel methodology, namely extreme events study. The aim is to investigate an [...] Read more.
Do tail events in the oil market trigger extreme responses by the clean-energy financial market (and vice versa)? This paper investigates the relationship between oil price and clean-energy stock with a novel methodology, namely extreme events study. The aim is to investigate an asymmetry effect between the response to good versus bad days. The results show how the two markets influence each other more negatively, i.e., extreme negative events significantly impact the other market. Furthermore, we document how the impact of the shock transmitted by oil prices to clean-energy stocks is less than the amount of shock transmitted oppositely. These findings have important implications for investor and renewable energy policies. Full article
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Article
Developing a Risk Model for Assessment and Control of the Spread of COVID-19
Risks 2021, 9(2), 38; https://doi.org/10.3390/risks9020038 - 09 Feb 2021
Cited by 3 | Viewed by 1084
Abstract
Coronavirus disease 2019 (COVID-19) continues to spread rapidly all over the world challenging nearly all governments. The exact nature of COVID-19’s spread and risk factors for such a rapid spread are still imprecise as available data depend on confirmed cases only. This may [...] Read more.
Coronavirus disease 2019 (COVID-19) continues to spread rapidly all over the world challenging nearly all governments. The exact nature of COVID-19’s spread and risk factors for such a rapid spread are still imprecise as available data depend on confirmed cases only. This may result in an asymmetrically distributed burden among countries. There is an urgent need for developing a new technique or model to identify and analyze risk factors affecting such a spread. Fuzzy logic appears to be suitable for dealing with multi-risk groups with undefined data. The main purpose of this research was to develop a risk analysis model for COVID-19’s spread evaluation. Other objectives included identifying such risk factors aiming to find out reasons for such a fast spread. Nine risk groups were identified and 46 risk factors were categorized under these groups. The methodology in this study depended on identifying each risk factor by its probability of occurrence and its impact on viruses spreading. Many logical rules were used to support the proposed risk analysis model and represented the relation between probabilities and impacts as well as to connect other risk factors. The model was verified and applied in Saudi Arabia with further probable use in similar conditions. Based on the model results, it was found that (daily activities) and (home isolation) are considered groups with highest risk. On the other hand, many risk factors were categorized with high severity such as (poor social distance), (crowdedness) and (poor personal hygiene practices). It was demonstrated that the impact of COVID-19’s spread was found with a positive correlation with the risk factors’ impact, while there was no association between probability of occurrence and impact of the risk factors on COVID-19’s spread. Saudi Arabia’s quick actions have greatly reduced the impact of the risks affecting COVID-19’s spread. Finally, the new model can be applied easily in most countries to help decision makers in evaluating and controlling COVID-19’s spread. Full article
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Article
Calibration of Transition Intensities for a Multistate Model: Application to Long-Term Care
Risks 2021, 9(2), 37; https://doi.org/10.3390/risks9020037 - 08 Feb 2021
Cited by 2 | Viewed by 1131
Abstract
We consider a non-homogeneous continuous time Markov chain model for Long-Term Care with five states: the autonomous state, three dependent states of light, moderate and severe dependence levels and the death state. For a general approach, we allow for non null intensities [...] Read more.
We consider a non-homogeneous continuous time Markov chain model for Long-Term Care with five states: the autonomous state, three dependent states of light, moderate and severe dependence levels and the death state. For a general approach, we allow for non null intensities for all the returns from higher dependence levels to all lesser dependencies in the multi-state model. Using data from the 2015 Portuguese National Network of Continuous Care database, as the main research contribution of this paper, we propose a method to calibrate transition intensities with the one step transition probabilities estimated from data. This allows us to use non-homogeneous continuous time Markov chains for modeling Long-Term Care. We solve numerically the Kolmogorov forward differential equations in order to obtain continuous time transition probabilities. We assess the quality of the calibration using the Portuguese life expectancies. Based on reasonable monthly costs for each dependence state we compute, by Monte Carlo simulation, trajectories of the Markov chain process and derive relevant information for model validation and premium calculation. Full article
(This article belongs to the Special Issue Pension Design, Modelling and Risk Management)
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Article
Myopic Savings Behaviour of Future Polish Pensioners
Risks 2021, 9(2), 36; https://doi.org/10.3390/risks9020036 - 06 Feb 2021
Cited by 2 | Viewed by 1296
Abstract
Low saving rates combined with low effective retirement age herald old-age poverty. This paper examines the preferred strategies of future Polish pensioners in order to sustain the standard of living in the future. A two-step approach is used: as a first-best strategy, we [...] Read more.
Low saving rates combined with low effective retirement age herald old-age poverty. This paper examines the preferred strategies of future Polish pensioners in order to sustain the standard of living in the future. A two-step approach is used: as a first-best strategy, we explore determinants of supplementary saving with binary logistic models; as a second-best strategy, we examine alternative options with principal component analysis. Future retirees rarely accumulate long-term savings, do not use dedicated instruments, and they start to save additionally far too late. Savings are concentrated in wealthier and better educated groups. Such myopia is governed by their political stance and not by awareness of dire prospects. Second-best strategies are based on optimistic assumptions about future health (seeking for additional jobs), on the assumed generosity of acquaintances or social institutions (relying on external assistance), or on rebelling. Given the increasing political power of elder generations, balancing the interests of workers and retirees will be an increasingly difficult task for policy makers. Full article
(This article belongs to the Special Issue Pension Design, Modelling and Risk Management)
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Article
Mortality Forecasting with an Age-Coherent Sparse VAR Model
Risks 2021, 9(2), 35; https://doi.org/10.3390/risks9020035 - 05 Feb 2021
Cited by 3 | Viewed by 1073
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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Article
Smart Beta Allocation and Macroeconomic Variables: The Impact of COVID-19
Risks 2021, 9(2), 34; https://doi.org/10.3390/risks9020034 - 04 Feb 2021
Cited by 1 | Viewed by 1202
Abstract
Smart beta strategies across economic regimes seek to address inefficiencies created by market-based indices, thereby enhancing portfolio returns above traditional benchmarks. Our goal is to develop a strategy for re-hedging smart beta portfolios that shows the connection between multi-factor strategies and macroeconomic variables. [...] Read more.
Smart beta strategies across economic regimes seek to address inefficiencies created by market-based indices, thereby enhancing portfolio returns above traditional benchmarks. Our goal is to develop a strategy for re-hedging smart beta portfolios that shows the connection between multi-factor strategies and macroeconomic variables. This is done, first, by analyzing finite correlations between the portfolio weights and macroeconomic variables and, more remarkably, by defining an investment tilting variable. The latter is analyzed with a discriminant analysis approach with a twofold application. The first is the selection of the crucial re-hedging thresholds which generate a strong connection between factors and macroeconomic variables. The second is forecasting portfolio dynamics (gain and loss). The capability of forecasting is even more evident in the COVID-19 period. Analysis is carried out on the iShares US exchange traded fund (ETF) market using monthly data in the period December 2013–May 2020, thereby highlighting the impact of COVID-19. Full article
(This article belongs to the Special Issue Financial Networks in Fintech Risk Management II)
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Article
Forward-Looking Volatility Estimation for Risk-Managed Investment Strategies during the COVID-19 Crisis
Risks 2021, 9(2), 33; https://doi.org/10.3390/risks9020033 - 01 Feb 2021
Cited by 3 | Viewed by 2073
Abstract
Under the impact of both increasing credit pressure and low economic returns characterizing developed countries, investment levels have decreased over recent years. Moreover, the recent turbulence caused by the COVID-19 crisis has accelerated the latter process. Within this scenario, we consider the so-called [...] Read more.
Under the impact of both increasing credit pressure and low economic returns characterizing developed countries, investment levels have decreased over recent years. Moreover, the recent turbulence caused by the COVID-19 crisis has accelerated the latter process. Within this scenario, we consider the so-called Volatility Target (VolTarget) strategy. In particular, we focus our attention on estimating volatility levels of a risky asset to perform a VolTarget simulation over two different time horizons. We first consider a 20 year period, from January 2000 to January 2020, then we analyse the last 12 months to emphasize the effects related to the COVID-19 virus’s diffusion. We propose a hybrid algorithm based on the composition of a GARCH model with a Neural Network (NN) approach. Let us underline that, as an alternative to standard allocation methods based on realized and backward oriented volatilities, we exploited an innovative forward-looking estimation process exploiting a Machine Learning (ML) solution. Our solution provides a more accurate volatility estimation, allowing us to derive an effective investor risk-return profile during market crisis periods. Moreover, we show that, via a forward-looking VolTarget strategy while using an ML-based prediction as the input, the average outcome for an investment in a drawdown plan is more sustainable while representing an efficient risk-control solution for long time period investments. Full article
(This article belongs to the Special Issue Computational Methods in Quantitative Risk Management)
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Article
Estimating the BIS Capital Adequacy Ratio for Korean Banks Using Machine Learning: Predicting by Variable Selection Using Random Forest Algorithms
Risks 2021, 9(2), 32; https://doi.org/10.3390/risks9020032 - 01 Feb 2021
Viewed by 1169
Abstract
The purpose of this study is to find the most important variables that represent the future projections of the Bank of International Settlements’ (BIS) capital adequacy ratio, which is the index of financial soundness in a bank as a comprehensive and important measure [...] Read more.
The purpose of this study is to find the most important variables that represent the future projections of the Bank of International Settlements’ (BIS) capital adequacy ratio, which is the index of financial soundness in a bank as a comprehensive and important measure of capital adequacy. This study analyzed the past 12 years of data from all domestic banks in South Korea. The research data include all financial information, such as key operating indicators, major business activities, and general information of the financial supervisory service of South Korea from 2008 to 2019. In this study, machine learning techniques, Random Forest Boruta algorithms, Random Forest Recursive Feature Elimination, and Bayesian Regularization Neural Networks (BRNN) were utilized. Among 1929 variables, this study found 38 most important variables for representing the BIS capital adequacy ratio. An additional comparison was executed to confirm the statistical validity of future prediction performance between BRNN and ordinary least squares (OLS) models. BRNN predicted the BIS capital adequacy ratio more robustly and accurately than the OLS models. We believe our findings would appeal to the readership of your journal such as the policymakers, managers and practitioners in the bank-related fields because this study highlights the key findings from the data-driven approaches using machine learning techniques. Full article
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Article
Efficiency Testing of Prediction Markets: Martingale Approach, Likelihood Ratio and Bayes Factor Analysis
Risks 2021, 9(2), 31; https://doi.org/10.3390/risks9020031 - 01 Feb 2021
Cited by 3 | Viewed by 996
Abstract
This paper studies efficient market hypothesis in prediction markets and the results are illustrated for the in-play football betting market using the quoted odds for the English Premier League. Our analysis is based on the martingale property, where the last quoted probability should [...] Read more.
This paper studies efficient market hypothesis in prediction markets and the results are illustrated for the in-play football betting market using the quoted odds for the English Premier League. Our analysis is based on the martingale property, where the last quoted probability should be the best predictor of the outcome and all previous quotes should be statistically insignificant. We use regression analysis to test for the significance of the previous quotes in both the time setup and the spatial setup based on stopping times, when the quoted probabilities reach certain bounds. The main contribution of this paper is to show how a potentially different distributional opinion based on the violation of the market efficiency can be monetized by optimal trading, where the agent maximizes logarithmic utility function. In particular, the trader can realize a trading profit that corresponds to the likelihood ratio in the situation of one market maker and one market taker, or the Bayes factor in the situation of two or more market takers. Full article
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Article
Triggers and Obstacles to the Development of the FinTech Sector in Poland
Risks 2021, 9(2), 30; https://doi.org/10.3390/risks9020030 - 01 Feb 2021
Cited by 4 | Viewed by 1630
Abstract
The article aims to show the opportunities for the formation of new FinTech startups in Poland and further development of the sector, as well as to identify the most critical threats. The study offers the descriptive and deductive analysis based on the literature [...] Read more.
The article aims to show the opportunities for the formation of new FinTech startups in Poland and further development of the sector, as well as to identify the most critical threats. The study offers the descriptive and deductive analysis based on the literature review. The empirical part relies on the data from external databases as well as the dataset collected in a survey run among the FinTechs in Poland in January 2020. The paper reveals that Poland is a fast-growing FinTech market which satisfies various requirements such as the number of secure Internet servers, mobile telephone subscriptions, the available labor force, as well as growing tertiary education enrolment. The crucial obstacles to the development of the sector is the uncertainty about the availability of skilled workers in the future and the lack of proper legal regulations. Full article
(This article belongs to the Special Issue The Risk Landscape within FinTech and InsurTech Business Models)
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Article
What Best Predicts Corporate Bank Loan Defaults? An Analysis of Three Different Variable Domains
Risks 2021, 9(2), 29; https://doi.org/10.3390/risks9020029 - 25 Jan 2021
Cited by 4 | Viewed by 1352
Abstract
This paper aims to compare the accuracy of financial ratios, tax arrears and annual report submission delays for the prediction of bank loan defaults. To achieve this, 12 variables from these three domains are used, while the study applies a longitudinal whole-population dataset [...] Read more.
This paper aims to compare the accuracy of financial ratios, tax arrears and annual report submission delays for the prediction of bank loan defaults. To achieve this, 12 variables from these three domains are used, while the study applies a longitudinal whole-population dataset from an Estonian commercial bank with 12,901 observations of defaulted and non-defaulted firms. The analysis is performed using statistical (logistic regression) and machine learning (neural networks) methods. Out of the three domains used, tax arrears show high prediction capabilities for bank loan defaults, while financial ratios and reporting delays are individually not useful for that purpose. The best default prediction accuracies were 83.5% with tax arrears only and 89.1% with all variables combined. The study contributes to the extant literature by enhancing the bank loan default prediction accuracy with the introduction of novel variables based on tax arrears, and also by indicating the pecking order of satisfying creditors’ claims in the firm failure process. Full article
(This article belongs to the Special Issue Credit Risk Modeling and Management in Banking Business)
Editorial
Acknowledgment to Reviewers of Risks in 2020
Risks 2021, 9(2), 28; https://doi.org/10.3390/risks9020028 - 24 Jan 2021
Viewed by 1039
Abstract
Peer review is the driving force of journal development, and reviewers are gatekeepers who ensure that Risks maintains its standards for the high quality of its published papers [...] Full article
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