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Risks
Volume 9
Issue 11
share announcement

Risks, Volume 9, Issue 11 (November 2021) – 24 articles

Cover Story (view full-size image): The risk premium of a European call option is defined as the relative difference in expected payoff under the P- and Q-probability measures. An option is regarded as (in)expensive when it bears a (positive) negative risk premium. In this work, we focus on options with a zero-risk premium, defined by the so-called zero-risk premium strike. This strike indicates the transition point from which call options are considered expensive. In order to calculate this zero-risk premium strike, pricing and physical distributional information on the return of the underlying asset is needed. We simultaneously extract this information from options data using a tilted bilateral gamma model. View this paper
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18 pages, 790 KiB  
Article
Digital Banking in Northern India: The Risks on Customer Satisfaction
by Baljinder Kaur, Sood Kiran, Simon Grima and Ramona Rupeika-Apoga
Risks 2021, 9(11), 209; https://doi.org/10.3390/risks9110209 - 17 Nov 2021
Cited by 59 | Viewed by 11096
Abstract
The widespread use of digital technologies and the current pandemic (COVID) have fueled the need and call for digital transformation in the banking sector. Although this has various benefits, it is a disruption to the norm to which a bank customer has to [...] Read more.
The widespread use of digital technologies and the current pandemic (COVID) have fueled the need and call for digital transformation in the banking sector. Although this has various benefits, it is a disruption to the norm to which a bank customer has to become accustomed. This variance means that customers would have to make some changes to their routine. This can constitute risks in terms of maintaining customer satisfaction at previous levels. These risks are associated with customer retention because a service or product needs to be aligned with customer expectations to avoid them switching to other service providers. Moreover, it can also have an effect on reputation. Offering digital account opening or remote deposits may not satisfy customers; competitive advantage depends on many aspects such as providing a hassle-free, personalized and cyber-secure experience, economic aspects and the needs of the society at large. Therefore, there is a need to understand the intensity of the risk factors that influence customer satisfaction for digitalized banking services and products. To do this, we carried out a structured survey, framed on the five dimensions of the SERVQUAL model, which was sent out to Northern Indian banking customers, to which we received 222 valid responses. We subjected the data received to Structural Equation Modelling using the SmartPLS version 3 application software. Results reveal that digital banking customers in Northern India are genuinely satisfied with the quality of services provided by digital banking. Moreover, ‘reliability’ has the strongest risk factor impact on customer satisfaction, followed by ‘tangibility’ and ‘responsiveness’. Full article
(This article belongs to the Special Issue The Risk Landscape within FinTech and InsurTech Business Models)
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22 pages, 5695 KiB  
Article
Development of an Impairment Point in Time Probability of Default Model for Revolving Retail Credit Products: South African Case Study
by Douw Gerbrand Breed, Niel van Jaarsveld, Carsten Gerken, Tanja Verster and Helgard Raubenheimer
Risks 2021, 9(11), 208; https://doi.org/10.3390/risks9110208 - 15 Nov 2021
Cited by 1 | Viewed by 3821
Abstract
A new methodology to derive IFRS 9 PiT PDs is proposed. The methodology first derives a PiT term structure with accompanying segmented term structures. Secondly, the calibration of credit scores using the Lorenz curve approach is used to create account-specific PD term structures. [...] Read more.
A new methodology to derive IFRS 9 PiT PDs is proposed. The methodology first derives a PiT term structure with accompanying segmented term structures. Secondly, the calibration of credit scores using the Lorenz curve approach is used to create account-specific PD term structures. The PiT term structures are derived by using empirical information based on the most recent default information and account risk characteristics prior to default. Different PiT PD term structures are developed to capture the structurally different default risk patterns for different pools of accounts using segmentation. To quantify what a materially different term structure constitutes, three tests are proposed. Account specific PiT PDs are derived through the Lorenz curve calibration using the latest default experience and credit scores. The proposed methodology is illustrated on an actual dataset, using a revolving retail credit portfolio from a South African bank. The main advantages of the proposed methodology include the use of well-understood methods (e.g., Lorenz curve calibration, scorecards, term structure modelling) in the banking industry. Further, the inclusion of re-default events in the proposed IFRS 9 PD methodology will simplify the development of the accompanying IFRS 9 LGD model due to the reduced complexity for the modelling of cure cases. Moreover, attrition effects are naturally included in the PD term structures and no longer require a separate model. Lastly, the PD term structure is based on months since observation, and therefore the arrears cycle could be investigated as a possible segmentation. Full article
(This article belongs to the Special Issue Quantitative Risk Modeling and Management—New Regulatory Challenges)
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15 pages, 1311 KiB  
Article
A Critical Analysis of Volatility Surprise in Bitcoin Cryptocurrency and Other Financial Assets
by Yianni Doumenis, Javad Izadi, Pradeep Dhamdhere, Epameinondas Katsikas and Dimitrios Koufopoulos
Risks 2021, 9(11), 207; https://doi.org/10.3390/risks9110207 - 12 Nov 2021
Cited by 11 | Viewed by 7100
Abstract
The purpose of this paper is to investigate the viability as compared with other financial assets of cryptocurrencies as a currency or as an asset investment. This paper also aims to see which macro variable relates more to the price of cryptocurrencies, especially [...] Read more.
The purpose of this paper is to investigate the viability as compared with other financial assets of cryptocurrencies as a currency or as an asset investment. This paper also aims to see which macro variable relates more to the price of cryptocurrencies, especially Bitcoin. Since the whole concept of cryptocurrencies is quite novel, an attempt has been made to briefly explain the underlying blockchain technology that forms the bedrock of cryptocurrencies. In this study, we use secondary data, i.e., the price history of Bitcoin from September 2014 to September 2021 for the last seven years, captured from trading exchanges. We predicted monthly returns of Bitcoin with that of Standard & Poor’s 500 Index (S&P 500), gold, and Treasury Bonds. Our findings show that Bitcoin has very high volatility compared to S&P 500, Gold and Treasury Bonds. Also, our findings show that there is a positive correlation between Bitcoin’s price volatility and the other three financial assets before and during COVID-19. Hence, Bitcoin is acting more as a speculative asset rather than a steady store of value. This can be drawn from the comparison with the debt market i.e., a Treasury Bond that invests in long-dated (30 years) US treasuries with which Bitcoin shows no relationship. The findings of this study could help with understanding the future of Bitcoin. This has important implications for Bitcoin investors. The current study contributes to the extant literature by providing empirical evidence on long-term social sustainability vis-à-vis supply chain traceability. Full article
(This article belongs to the Special Issue Cryptocurrencies and Risk Management)
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12 pages, 486 KiB  
Article
Designing a Model for Testing the Effectiveness of a Regulation: The Case of DORA for Insurance Undertakings
by Simon Grima and Pierpaolo Marano
Risks 2021, 9(11), 206; https://doi.org/10.3390/risks9110206 - 12 Nov 2021
Cited by 18 | Viewed by 2451
Abstract
Technology is sometimes seen as a disruption that although provides opportunities for growth and development, also provides opportunities for deception, theft, and fraud. On the other hand, automation can make it easier to identify and protect from threats. Hence, a proposal was made [...] Read more.
Technology is sometimes seen as a disruption that although provides opportunities for growth and development, also provides opportunities for deception, theft, and fraud. On the other hand, automation can make it easier to identify and protect from threats. Hence, a proposal was made by the European Commission to enact a digital operations resilience act. Therefore, our objective in this paper is to lay out the perceived characteristics of effective regulation by using DORA as our case study. We do this by carrying out a literature review and extracting using the thematic analysis approach propositions for these characteristics. Then, we test these using exploratory factor analysis and design a model for perceived effective regulation (PERM). We test the reliability and validity of this model by using the Cronbach alpha. Results show that according to our model, an effective regulation should have four characteristics, specifically “Flexibility and Integration”, “Proportionality and Cost”, “Reliability and Transparency”, and “Relevance and Timeliness”. Findings laid out in this paper and PERM can be used to test other proposed regulations to ensure that they are effective before being enacted and also to determine when there is a need for a revamp in specified areas of current regulations and requirements. Full article
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24 pages, 1310 KiB  
Article
Value-Based Financial Risk Prediction Model
by Jiří Pospíšil, Nataša Matulayová, Pavla Macháčková, Pavlína Jurníčková, Ivana Olecká and Helena Pospíšilová
Risks 2021, 9(11), 205; https://doi.org/10.3390/risks9110205 - 11 Nov 2021
Cited by 4 | Viewed by 3155
Abstract
The model of financial risk prediction we developed and present in our paper is based on the theoretical assumption that there exists a significant relationship between actual economic situation and values. This assumption confirmed by the research influences the potential risk in financial [...] Read more.
The model of financial risk prediction we developed and present in our paper is based on the theoretical assumption that there exists a significant relationship between actual economic situation and values. This assumption confirmed by the research influences the potential risk in financial behaviour and it becomes actual especially in the case of changing life conditions. The concept of the model is based on data received from 3768 respondents questioned across the Czech Republic. Measured variables were indexed, and the cluster and factor analyses were used for multivariate analysis. The model is unique in the combination of personal values projected into six generalized value types and developed economic indexes clustered in four types of economic situations. The primary purpose of the model is to identify the anticipated personal financial risk of clients. The model has fundamental applications as a diagnostic or auto-diagnostic tool in social work, counselling, psychotherapy, and other helping professions, or as a research instrument leading to various hypotheses and to the enhancement of theories concerning economic behaviour. Full article
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26 pages, 1912 KiB  
Article
Using Model Performance to Assess the Representativeness of Data for Model Development and Calibration in Financial Institutions
by Chamay Kruger, Willem Daniel Schutte and Tanja Verster
Risks 2021, 9(11), 204; https://doi.org/10.3390/risks9110204 - 10 Nov 2021
Cited by 3 | Viewed by 2926
Abstract
This paper proposes a methodology that utilises model performance as a metric to assess the representativeness of external or pooled data when it is used by banks in regulatory model development and calibration. There is currently no formal methodology to assess representativeness. The [...] Read more.
This paper proposes a methodology that utilises model performance as a metric to assess the representativeness of external or pooled data when it is used by banks in regulatory model development and calibration. There is currently no formal methodology to assess representativeness. The paper provides a review of existing regulatory literature on the requirements of assessing representativeness and emphasises that both qualitative and quantitative aspects need to be considered. We present a novel methodology and apply it to two case studies. We compared our methodology with the Multivariate Prediction Accuracy Index. The first case study investigates whether a pooled data source from Global Credit Data (GCD) is representative when considering the enrichment of internal data with pooled data in the development of a regulatory loss given default (LGD) model. The second case study differs from the first by illustrating which other countries in the pooled data set could be representative when enriching internal data during the development of a LGD model. Using these case studies as examples, our proposed methodology provides users with a generalised framework to identify subsets of the external data that are representative of their Country’s or bank’s data, making the results general and universally applicable. Full article
(This article belongs to the Special Issue Quantitative Risk Modeling and Management—New Regulatory Challenges)
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21 pages, 2337 KiB  
Article
Subnational Mortality Modelling: A Bayesian Hierarchical Model with Common Factors
by Qian Lu, Katja Hanewald and Xiaojun Wang
Risks 2021, 9(11), 203; https://doi.org/10.3390/risks9110203 - 10 Nov 2021
Cited by 1 | Viewed by 2112
Abstract
We propose a new model in a Bayesian hierarchical framework to project mortality at both national and subnational levels based on sparse or missing data. The new model, which has a country–region–province structure, uses common factors to pool information at the national level [...] Read more.
We propose a new model in a Bayesian hierarchical framework to project mortality at both national and subnational levels based on sparse or missing data. The new model, which has a country–region–province structure, uses common factors to pool information at the national level and within regions consisting of several provinces or states. We illustrate the model’s use by drawing on a new database containing provincial-level mortality data for China from four censuses conducted during the period 1982–2010. The new model provides good estimates and reasonable forecasts at both the country and provincial levels. The model’s forecast intervals reflect provincial- and regional-level uncertainty. Using subnational data for the period 1999–2018 from the Centers for Disease Control and Prevention (CDC), we also apply the model to the United States. We use mortality forecasts to compute and compare national and subnational life expectancies for China and the United States. The model predicts that, in 2030, China will have a similar national life expectancy at age 60 and a similar heterogeneity in subnational life expectancy as the United States. Full article
(This article belongs to the Special Issue Longevity Risk Modelling and Management)
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12 pages, 1724 KiB  
Article
Establishing a Credit Risk Evaluation System for SMEs Using the Soft Voting Fusion Model
by Ge Gao, Hongxin Wang and Pengbin Gao
Risks 2021, 9(11), 202; https://doi.org/10.3390/risks9110202 - 09 Nov 2021
Cited by 10 | Viewed by 2300
Abstract
In China, SMEs are facing financing difficulties, and commercial banks and financial institutions are the main financing channels for SMEs. Thus, a reasonable and efficient credit risk assessment system is important for credit markets. Based on traditional statistical methods and AI technology, a [...] Read more.
In China, SMEs are facing financing difficulties, and commercial banks and financial institutions are the main financing channels for SMEs. Thus, a reasonable and efficient credit risk assessment system is important for credit markets. Based on traditional statistical methods and AI technology, a soft voting fusion model, which incorporates logistic regression, support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), is constructed to improve the predictive accuracy of SMEs’ credit risk. To verify the feasibility and effectiveness of the proposed model, we use data from 123 SMEs nationwide that worked with a Chinese bank from 2016 to 2020, including financial information and default records. The results show that the accuracy of the soft voting fusion model is higher than that of a single machine learning (ML) algorithm, which provides a theoretical basis for the government to control credit risk in the future and offers important references for banks to make credit decisions. Full article
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20 pages, 568 KiB  
Article
Does Working Capital Management Influence Operating and Market Risk of Firms?
by Ahsan Akbar, Minhas Akbar, Marina Nazir, Petra Poulova and Samrat Ray
Risks 2021, 9(11), 201; https://doi.org/10.3390/risks9110201 - 08 Nov 2021
Cited by 9 | Viewed by 7245
Abstract
Extant empirical studies have predominantly focused on the nexus between working capital management (WCM) and corporate profitability. While there is a dearth of literature on the nexus between WCM and a firm’s risk, the present study examines Pakistani-listed firms coming from 12 diverse [...] Read more.
Extant empirical studies have predominantly focused on the nexus between working capital management (WCM) and corporate profitability. While there is a dearth of literature on the nexus between WCM and a firm’s risk, the present study examines Pakistani-listed firms coming from 12 diverse industrial segments to observe this association for a time span of ten years (2005–2014). To ensure robustness, we employed a System Generalized Method of Moments (SGMM) regression estimation to investigate the influence of WCM on the operational and market risk for firms. Empirical testing revealed that higher working capital levels were associated with lower volatility in firms’ stock price, which shows that shareholders prefer a conservative working capital policy. Moreover, firms with better cash positions were subject to lesser stock market volatility. In contrast, excess working capital and a larger net trade cycle were associated with increased volatility in the operating income. Besides, firms with lower working capital levels relative to their respective industry experienced fewer fluctuations in their operating profits. Our findings assert that short-term financial management has important ramifications for firms’ operating and market fundamentals. Practical implications are discussed for corporate managers and relevant stakeholders. Full article
(This article belongs to the Special Issue Financial Risk Management in SMEs)
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24 pages, 751 KiB  
Article
An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression
by Youssef Zizi, Amine Jamali-Alaoui, Badreddine El Goumi, Mohamed Oudgou and Abdeslam El Moudden
Risks 2021, 9(11), 200; https://doi.org/10.3390/risks9110200 - 08 Nov 2021
Cited by 17 | Viewed by 5410
Abstract
In the face of rising defaults and limited studies on the prediction of financial distress in Morocco, this article aims to determine the most relevant predictors of financial distress and identify its optimal prediction models in a normal Moroccan economic context over two [...] Read more.
In the face of rising defaults and limited studies on the prediction of financial distress in Morocco, this article aims to determine the most relevant predictors of financial distress and identify its optimal prediction models in a normal Moroccan economic context over two years. To achieve these objectives, logistic regression and neural networks are used based on financial ratios selected by lasso and stepwise techniques. Our empirical results highlight the significant role of predictors, namely interest to sales and return on assets in predicting financial distress. The results show that logistic regression models obtained by stepwise selection outperform the other models with an overall accuracy of 93.33% two years before financial distress and 95.00% one year prior to financial distress. Results also show that our models classify distressed SMEs better than healthy SMEs with type I errors lower than type II errors. Full article
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23 pages, 692 KiB  
Article
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 - 05 Nov 2021
Cited by 10 | Viewed by 7367
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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55 pages, 1013 KiB  
Review
Stochastic Claims Reserving Methods with State Space Representations: A Review
by Nataliya Chukhrova and Arne Johannssen
Risks 2021, 9(11), 198; https://doi.org/10.3390/risks9110198 - 04 Nov 2021
Cited by 4 | Viewed by 1917
Abstract
Often, the claims reserves exceed the available equity of non-life insurance companies and a change in the claims reserves by a small percentage has a large impact on the annual accounts. Therefore, it is of vital importance for any non-life insurer to handle [...] Read more.
Often, the claims reserves exceed the available equity of non-life insurance companies and a change in the claims reserves by a small percentage has a large impact on the annual accounts. Therefore, it is of vital importance for any non-life insurer to handle claims reserving appropriately. Although claims data are time series data, the majority of the proposed (stochastic) claims reserving methods is not based on time series models. Among the time series models, state space models combined with Kalman filter learning algorithms have proven to be very advantageous as they provide high flexibility in modeling and an accurate detection of the temporal dynamics of a system. Against this backdrop, this paper aims to provide a comprehensive review of stochastic claims reserving methods that have been developed and analyzed in the context of state space representations. For this purpose, relevant articles are collected and categorized, and the contents are explained in detail and subjected to a conceptual comparison. Full article
(This article belongs to the Special Issue Statistical Methods for Quantitative Risk Management)
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14 pages, 1091 KiB  
Article
Supply Chain Management and Risk Management in an Environment of Stochastic Uncertainty (Retail)
by Sergey A. Lochan, Tatiana P. Rozanova, Valery V. Bezpalov and Dmitry V. Fedyunin
Risks 2021, 9(11), 197; https://doi.org/10.3390/risks9110197 - 04 Nov 2021
Cited by 13 | Viewed by 3503
Abstract
In the context of stochastic uncertainty and the increasing complexity of logistics processes in the retail sector, managers face a problem in obtaining accurate forecasts for the dynamics of changes in key business performance indicators. The purpose of the present work is to [...] Read more.
In the context of stochastic uncertainty and the increasing complexity of logistics processes in the retail sector, managers face a problem in obtaining accurate forecasts for the dynamics of changes in key business performance indicators. The purpose of the present work is to assess the impact of risk events and unstable conditions on the level of quality of supply chain services and economic indicators of the retail trade network. Using the anyLogistix software tool, a simulation model was constructed that allows assessing operational risks and their impact on key indicators of the supply chain using the bullwhip effect. Besides, a statistical model of the impact of the ripple effect in the event of failures caused by the occurrence of a man-made risk event and the shutdown of production of one of the suppliers on the financial, customer, and operational performance indicators of the supply chain of grocery retail. The results obtained show that the main factors of changes in the supply chain are operational risks associated with fluctuations in demand and order execution time by the distribution center. With a sufficiently high level of occurrence, their impact on productivity and quality of service is low because they can be eliminated in a short time. The simulation results show that the most tangible risks for the food retail supply chain are supply chain failures, whose consequences require significant coordinating efforts and longer recovery times, as well as additional investments. For example, events, such as a fire in one distribution center and the shutdown of production for 1 week of one of the suppliers of dairy products will lead to the loss of USD 181.75 million by the grocery retailer, which is 3% of the expected revenue. We believe that risk management in supply chains is becoming increasingly complex, and to make effective managerial decisions, it is necessary to constantly improve the tools that combine analytical and optimization methods, as well as simulation modeling. Full article
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19 pages, 937 KiB  
Article
It Takes Two to Tango: Estimation of the Zero-Risk Premium Strike of a Call Option via Joint Physical and Pricing Density Modeling
by Stephan Höcht, Dilip B. Madan, Wim Schoutens and Eva Verschueren
Risks 2021, 9(11), 196; https://doi.org/10.3390/risks9110196 - 04 Nov 2021
Viewed by 1545
Abstract
It is generally said that out-of-the-money call options are expensive and one can ask the question from which moneyness level this is the case. Expensive actually means that the price one pays for the option is more than the discounted average payoff one [...] Read more.
It is generally said that out-of-the-money call options are expensive and one can ask the question from which moneyness level this is the case. Expensive actually means that the price one pays for the option is more than the discounted average payoff one receives. If so, the option bears a negative risk premium. The objective of this paper is to investigate the zero-risk premium moneyness level of a European call option, i.e., the strike where expectations on the option’s payoff in both the P- and Q-world are equal. To fully exploit the insights of the option market we deploy the Tilted Bilateral Gamma pricing model to jointly estimate the physical and pricing measure from option prices. We illustrate the proposed pricing strategy on the option surface of stock indices, assessing the stability and position of the zero-risk premium strike of a European call option. With small fluctuations around a slightly in-the-money level, on average, the zero-risk premium strike appears to follow a rather stable pattern over time. Full article
(This article belongs to the Special Issue Quantitative Risk Measurement and Management)
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20 pages, 495 KiB  
Article
A Nonlinear Autoregressive Distributed Lag (NARDL) Analysis of the FTSE and S&P500 Indexes
by David E. Allen and Michael McAleer
Risks 2021, 9(11), 195; https://doi.org/10.3390/risks9110195 - 03 Nov 2021
Cited by 11 | Viewed by 12076
Abstract
The paper features an examination of the link between the behaviour of the FTSE 100 and S&P500 Indexes in both an autoregressive distributed lag ARDL, plus a nonlinear autoregressive distributed lag NARDL framework. The attraction of NARDL is that it represents the simplest [...] Read more.
The paper features an examination of the link between the behaviour of the FTSE 100 and S&P500 Indexes in both an autoregressive distributed lag ARDL, plus a nonlinear autoregressive distributed lag NARDL framework. The attraction of NARDL is that it represents the simplest method available of modelling combined short- and long-run asymmetries. The bounds testing framework adopted means that it can be applied to stationary and non-stationary time series vectors, or combinations of both. The data comprise a daily FTSE adjusted price series, commencing in April 2009 and terminating in March 2021, and a corresponding daily S&P500 Index adjusted-price series obtained from Yahoo Finance. The data period includes all the gyrations caused by the Brexit vote in the UK, beginning with the vote to leave in 2016 and culminating in the actual agreement to withdraw in January 2020. It was then followed by the impact of the global spread of COVID-19 from the beginning of 2020. The results of the analysis suggest that movements in the contemporaneous levels of daily S&P500 Index levels have very significant effects on the behaviour of the levels of the daily FTSE 100 Index. They also suggest that negative movements have larger impacts than do positive movements in S&P500 levels, and that long-term multiplier impacts take about 10 days to take effect. These effects are supported by the results of quantile regression analysis. A key result is that weak form market efficiency does not apply in the second period. Full article
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18 pages, 821 KiB  
Article
Indonesian Hotels’ Dynamic Capability under the Risks of COVID-19
by Muhammad Yunus Amar, Alim Syariati, Ridwan Ridwan and Rika Dwi Ayu Parmitasari
Risks 2021, 9(11), 194; https://doi.org/10.3390/risks9110194 - 03 Nov 2021
Cited by 9 | Viewed by 2808
Abstract
The effects of COVID-19 on tourism are irreversible, with potential reductions in income, job losses, shifting working landscapes, and visible health-related fears. These adversities are reinforced in the hospitality business, particularly for hotels, the income streams of which rely on individual movements. This [...] Read more.
The effects of COVID-19 on tourism are irreversible, with potential reductions in income, job losses, shifting working landscapes, and visible health-related fears. These adversities are reinforced in the hospitality business, particularly for hotels, the income streams of which rely on individual movements. This study investigates the process undertaken by the hotel industry in Indonesia to face the current challenges, particularly in terms of the dynamic capabilities possessed by hotel businesses. This construct discusses the potentiality of maximizing existing resources and its impact on innovation norms to leverage hotel dynamics. A total of 329 hotel managers responded to the survey, and the data were finalized by employing PLS-SEM. The findings primarily support the hypothesized direct relationships, but refute the presence of indirect relationships. The results amplify how past investments in sustainable resources are easily deployed assets during COVID-19 and create a welcoming environment for dynamic innovation among hotels during periods of change. Full article
(This article belongs to the Special Issue Risk and Multifaceted Failures in Business Operations)
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17 pages, 2697 KiB  
Article
Improving Disaster Risk Management According to Development Projects
by Chang-Jae Kwak and Jung-Soo Kim
Risks 2021, 9(11), 193; https://doi.org/10.3390/risks9110193 - 02 Nov 2021
Cited by 1 | Viewed by 1702
Abstract
Since the 1990s, efforts have been made to reduce the damage caused by natural disasters, among which the Disaster Impact Assessment (DIA) System implemented in 1995 is noteworthy for its proactive response. The DIA System has undergone various institutional and technological changes to [...] Read more.
Since the 1990s, efforts have been made to reduce the damage caused by natural disasters, among which the Disaster Impact Assessment (DIA) System implemented in 1995 is noteworthy for its proactive response. The DIA System has undergone various institutional and technological changes to retain its original purpose. However, its operation has become inadequate because of the diversification of business types. This paper presents the improvements required in the DIA System based on an analysis of the problems that have emerged during its institutional development and over 9000 pieces of data collected from 2015 to 2017. The results show that, first, the DIA’s Practical Guidelines should be subdivided, considering the diversity of projects. Second, the system should be strengthened to ensure it is not mistaken for a mere bureaucratic box-ticking exercise. Third, non-structural measures should be expanded to reduce the number of casualties after development. Incorporating the improvements proposed in this study will improve the effectiveness of the DIA. Additionally, the DIA System could be established as an important model for Korea’s disaster risk reduction activities. Full article
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15 pages, 371 KiB  
Review
Machine Learning (ML) Technologies for Digital Credit Scoring in Rural Finance: A Literature Review
by Anil Kumar, Suneel Sharma and Mehregan Mahdavi
Risks 2021, 9(11), 192; https://doi.org/10.3390/risks9110192 - 31 Oct 2021
Cited by 14 | Viewed by 8062
Abstract
Rural credit is one of the most critical inputs for farm production across the globe. Despite so many advances in digitalization in emerging and developing economies, still a large part of society like small farm holders, rural youth, and women farmers are untouched [...] Read more.
Rural credit is one of the most critical inputs for farm production across the globe. Despite so many advances in digitalization in emerging and developing economies, still a large part of society like small farm holders, rural youth, and women farmers are untouched by the mainstream of banking transactions. Machine learning-based technology is giving a new hope to these individuals. However, it is the banking or non-banking institutions that decide how they will adopt this advanced technology, to have reduced human biases in loan decision making. Therefore, the scope of this study is to highlight the various AI-ML- based methods for credit scoring and their gaps currently in practice by banking or non-banking institutions. For this study, systematic literature review methods have been applied; existing research articles have been empirically reviewed with an attempt to identify and compare the best fit AI-ML-based model adopted by various financial institutions worldwide. The main purpose of this study is to present the various ML algorithms highlighted by earlier researchers that could be fit for a credit assessment of rural borrowers, particularly those who have no or inadequate loan history. However, it would be interesting to recognize further how the financial institutions could be able to blend the traditional and digital methods successfully without any ethical challenges. Full article
26 pages, 737 KiB  
Article
Crop Insurance Policies in India: An Empirical Analysis of Pradhan Mantri Fasal Bima Yojana
by Sandeep Kaur, Hem Raj, Harpreet Singh and Vijay Kumar Chattu
Risks 2021, 9(11), 191; https://doi.org/10.3390/risks9110191 - 26 Oct 2021
Cited by 2 | Viewed by 7458
Abstract
India is home to over one-third of all undernourished children worldwide, and it ranks 94th out of 107 nations in the Global Hunger Index 2020. Instability in production and market risks make agriculture a risky business and directly affect farmers’ income levels, thereby [...] Read more.
India is home to over one-third of all undernourished children worldwide, and it ranks 94th out of 107 nations in the Global Hunger Index 2020. Instability in production and market risks make agriculture a risky business and directly affect farmers’ income levels, thereby impacting food security. This review aimed to understand various features of different crop insurance policies in India and to analyze the Pradhan Mantri Fasal Bima Yojana’s (PMFBY) impacts on Indian farmers. A literature search was performed in all popular databases, including Scopus, Web of Science, ProQuest, AGRICOLA, AGRIS, and Google search engines, as well as annual Indian government reports. The keywords “Crop Insurance” OR “Pradhan Mantri Fasal Bima Yojana” OR “National Agriculture Schemes” AND “India” were searched to obtain relevant articles. By using cumulative data, we conducted a multiple regression analysis and a model was developed to estimate the effects of insurance characteristics on farmer coverage for the years 2017–2018 and 2018–2019. Agricultural insurance coverage under PMFBY remained low in terms of the number of farmers insured, the area insured, claims paid, and total farmers benefited. Compared to other schemes, the beneficiary and claim premium ratios were substantially lower under the PMFBY. The multiple regression analysis showed that farmers’ premiums have a significant effect on the number of farmers insured over time, although the subsidies do not have a significant influence in farmers’ insurance participation. Delays in claim settlement, the complexity of the system, and a lack of awareness among farmers are the major weaknesses of the PMFBY. Greater use of digital media could help spread awareness of these schemes among farmers. Full article
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18 pages, 2256 KiB  
Article
Public Pensions and Implicit Debt: An Investigation for EU Member States Using Ageing Working Group 2021 Projections
by Georgios Symeonidis, Platon Tinios and Michail Chouzouris
Risks 2021, 9(11), 190; https://doi.org/10.3390/risks9110190 - 26 Oct 2021
Cited by 2 | Viewed by 2641
Abstract
Ιmplicit pension debt is attracting increasing attention worldwide as a driver of fiscal dynamics, operating in parallel to the (explicit) National Debt. A prudent examination of a state’s fiscal prospects should ideally encompass both, with due attention paid to the special features of [...] Read more.
Ιmplicit pension debt is attracting increasing attention worldwide as a driver of fiscal dynamics, operating in parallel to the (explicit) National Debt. A prudent examination of a state’s fiscal prospects should ideally encompass both, with due attention paid to the special features of each kind of debt. The explosion of government deficits as a result of the COVID-19 pandemic only adds to the urgency of understanding the scale and nature of issues around accounting for contingent liabilities. The reports of the EU Ageing working group, produced and published every three years are used to derive estimates of the stock of outstanding implicit pension debt from flows of projected deficits. This can be performed for all European member states. This paper uses the last two rounds of the Ageing Report (2021, 2018) and derives conclusions on the evolution of pension debt and its correlation to the external debt. The paper concludes that producing comparable estimates of IPD should become an important input in EU policy discussion. Full article
(This article belongs to the Special Issue An Ageing Population, Retirement Planning, and Financial Insecurity)
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18 pages, 436 KiB  
Article
Designing Annuities with Flexibility Opportunities in an Uncertain Mortality Scenario
by Annamaria Olivieri
Risks 2021, 9(11), 189; https://doi.org/10.3390/risks9110189 - 22 Oct 2021
Cited by 2 | Viewed by 1538
Abstract
We consider annuity designs in which the benefit amount is allowed to fluctuate (up or down), based on a given mortality/longevity experience. This way, guarantees are relaxed in respect of traditional annuity arrangements. On the other hand, while the annuitant is exposed to [...] Read more.
We consider annuity designs in which the benefit amount is allowed to fluctuate (up or down), based on a given mortality/longevity experience. This way, guarantees are relaxed in respect of traditional annuity arrangements. On the other hand, while the annuitant is exposed to the risk of a future reduction of the benefit amount because of higher longevity, he/she can immediately take advantage of a lower premium loading, as well as of a future increase of the benefit amount in the case of higher mortality. Flexibility in the annuity design could be welcomed by individuals, as the conservative features of traditional products partly explain their lack of attractiveness in most markets. To further contribute to the flexibility of the product, we suggest a pricing structure based on periodic fees applied to the policy fund, instead of the usual upfront loading at issue. Periodic fees are more suitable to support a revision of the arrangement after issue, which is currently not allowed in traditional annuity products. We show that periodic fees can be introduced by identifying a discount factor to be used for pricing and reserving. We assume stochastic mortality, and we compare alternative mortality/longevity linking solutions, by assessing the periodic fees and other quantities. Full article
(This article belongs to the Special Issue Quantitative Risk Assessment in Life, Health and Pension Insurance)
21 pages, 2497 KiB  
Article
Equity Risk and Return across Hidden Market Regimes
by Dmitry A. Endovitsky, Viacheslav V. Korotkikh and Denis A. Khripushin
Risks 2021, 9(11), 188; https://doi.org/10.3390/risks9110188 - 22 Oct 2021
Cited by 2 | Viewed by 2758
Abstract
The key to understanding the dynamics of stock markets, particularly the mechanisms of their changes, is in the concept of the market regime. It is regarded as a regular transition from one state to another. Although the market agenda is never the same, [...] Read more.
The key to understanding the dynamics of stock markets, particularly the mechanisms of their changes, is in the concept of the market regime. It is regarded as a regular transition from one state to another. Although the market agenda is never the same, its functioning regime allows us to reveal the logic of its development. The article employs the concept of financial turbulence to identify hidden market regimes. These are revealed through the ratio of the components, which describe single changes of correlated risks and volatility. The combinations of typical and atypical variates of correlational and magnitude components of financial turbulence allowed four hidden regimes to be revealed. These were arranged by the degree of financial turbulence, conceptually analyzed and assessed from the perspective of their duration. The empirical data demonstrated ETF day trading profits for S&P 500 sectors, covering the period of January 1998–August 2020, as well as day trade profits of the Russian blue chips within the period of October 2006–February 2021. The results show a significant difference in regard to the market performance and volatility, which depend on hidden regimes. Both sample data groups demonstrated similar contemporaneous and lagged effects, which allows the prediction of volatility jumps in the periods following atypical correlations. Full article
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11 pages, 413 KiB  
Article
The Competency Niche: An Exploratory Study
by Zbysław Dobrowolski, Grzegorz Drozdowski and Józef Ledzianowski
Risks 2021, 9(11), 187; https://doi.org/10.3390/risks9110187 - 21 Oct 2021
Cited by 6 | Viewed by 2687
Abstract
In the era of a turbulent and less-predictable business environment, as confirmed by the COVID-19 pandemic, the ability to efficiently use human resources has become particularly important. There is a need to reduce employees' competency niche, and competency mismatches have become noticeable in [...] Read more.
In the era of a turbulent and less-predictable business environment, as confirmed by the COVID-19 pandemic, the ability to efficiently use human resources has become particularly important. There is a need to reduce employees' competency niche, and competency mismatches have become noticeable in the European Union. We performed qualitative interviews (n = 282) to determine the competency niche of employees from private firms in Poland. Results show that employees were passive in identifying their competence needs. Moreover, firms did not use the weak signals methodology to eliminate the competency niche. This novel study found that firms should be more active in identifying employee competency niches by analyzing early signs to be ready for any changes without delays. The findings create a basis for proposing preventive measures, and we point out avenues for future research. Full article
(This article belongs to the Special Issue Risk and Multifaceted Failures in Business Operations)
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15 pages, 961 KiB  
Article
Risking Sustainability: Political Risk Culture as Inhibiting Ecology-Centered Sustainability
by Susan T. Jackson
Risks 2021, 9(11), 186; https://doi.org/10.3390/risks9110186 - 20 Oct 2021
Cited by 5 | Viewed by 2028
Abstract
This exploratory study aims to understand why, and propose remedies for, the treatment of political risk and sustainability as siloed risk areas in risk analyses. I employ an interdisciplinary theoretical approach that focuses on the roles of values and worldviews, stages of sustainability [...] Read more.
This exploratory study aims to understand why, and propose remedies for, the treatment of political risk and sustainability as siloed risk areas in risk analyses. I employ an interdisciplinary theoretical approach that focuses on the roles of values and worldviews, stages of sustainability and hybrid knowledge to understand this siloing. The large-N interpretive method used here combines content frequency counts with discourse analysis to examine over 400 corporate communication documents from 37 companies. The study also explores how, through corporate communication, companies that provide political risk analysis convey what is at risk and what counts as sustainability. I argue that the broad shared ‘cultural’ tones of what it means to be in the political risk field pose challenges for integrating political risk and sustainability. The study concludes with several recommendations on how to overcome the current barriers in order to integrate political risk and sustainability in risk analyses. Full article
(This article belongs to the Special Issue Advances in Sustainable Risk Management)
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