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Volume 14
Issue 5
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J. Risk Financial Manag., Volume 14, Issue 5 (May 2021) – 43 articles

Cover Story (view full-size image): This paper defines a daily index representing the risk–return on investments in the American film industry. The index predicts the riskiness and the expected return of movie projects at the level of the overall industry and could be used to determine a premium for insurance for such an investment. Though not currently legal in the United States, such an index could be employed in other countries by film production companies as well as venture capitalists interested in investing in motion picture productions, or, more broadly, in the holdings of a media conglomerate, an exhibition chain, or some other aspect of the media landscape. View this paper.
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18 pages, 721 KiB  
Article
Do Financial Professionals Process Information Better as a Group Than Non-Professionals?
by Orie E. Barron, Charles R. Enis and Hong Qu
J. Risk Financial Manag. 2021, 14(5), 230; https://doi.org/10.3390/jrfm14050230 - 20 May 2021
Viewed by 2039
Abstract
In this study, we study information processing by financial professionals benchmarked with non-professionals and how correlation among individual forecasts explains the group level forecast performance. In an experiment in which participants make price forecasts based on common financial information, we find that individual [...] Read more.
In this study, we study information processing by financial professionals benchmarked with non-professionals and how correlation among individual forecasts explains the group level forecast performance. In an experiment in which participants make price forecasts based on common financial information, we find that individual professionals are no better than individual non-professionals in forecasting, but professionals’ mean forecasts are superior. Our analysis suggests that financial professionals’ individual errors are less correlated as they process information from more diverse perspectives. This leads to superior mean forecasts because the uncorrelated individual errors cancel each other out in the aggregate. In contrast, non-professionals are similar in using salient information such as earnings or cash flow. As a result, their individual errors are highly correlated. Instead of cancelling each other out, the individual errors are enlarged in the aggregated mean forecasts. We are the first to show the difference in the comparisons of professionals and non-professionals at the group level versus at the individual level. Our paper contributes to the literature by documenting the evidence of diversity in information processing by financial professionals. Full article
(This article belongs to the Section Applied Economics and Finance)
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14 pages, 611 KiB  
Article
Financial Contagion: A Tale of Three Bubbles
by Nathan Burks, Adetokunbo Fadahunsi and Ann Marie Hibbert
J. Risk Financial Manag. 2021, 14(5), 229; https://doi.org/10.3390/jrfm14050229 - 20 May 2021
Cited by 2 | Viewed by 3930
Abstract
The primary purpose of the study is to identify and measure the properties of asset bubbles, volatility clustering, and financial contagion during three recent financial market anomalies that originated in the U.S. and Chinese markets. In particular, we focus on the 2000 DotCom [...] Read more.
The primary purpose of the study is to identify and measure the properties of asset bubbles, volatility clustering, and financial contagion during three recent financial market anomalies that originated in the U.S. and Chinese markets. In particular, we focus on the 2000 DotCom Bubble, the 2008 Housing Crisis, and the 2015 Chinese Bubble. We employ three main empirical methods; the LPPL model to identify asset bubbles, the DCC-GARCH model to measure volatility clustering, and the Diebold-Yilmaz volatility spillover index to measure the level of financial contagion. We provide robust evidence that during the DotCom bubble there was very limited spillover between the S&P 500, the Shanghai, and the Shenzhen Composite Indexes. However, there was significantly more spillover effects in the two more recent crises, i.e., the Housing crisis and the 2015 Chinese Bubble. Together, these results highlight the fact that as financial markets have become more globalized, there are greater levels of volatility transmission and correspondingly fewer potential benefits from international diversification. Full article
(This article belongs to the Special Issue Economic Forecasting)
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23 pages, 411 KiB  
Article
Risk Management: Exploring Emerging Human Resource Issues during the COVID-19 Pandemic
by Yifan Zhong, Yameng Li, Jian Ding and Yiyi Liao
J. Risk Financial Manag. 2021, 14(5), 228; https://doi.org/10.3390/jrfm14050228 - 19 May 2021
Cited by 29 | Viewed by 30032
Abstract
The unanticipated coronavirus disease 2019 (COVID-19) pandemic has hit global business heavily, disrupting the management of human resources across numerous industries. More than 500 articles (indexed in Scopus and the Web of Science) on the impact of the COVID-19 outbreak on emerging human [...] Read more.
The unanticipated coronavirus disease 2019 (COVID-19) pandemic has hit global business heavily, disrupting the management of human resources across numerous industries. More than 500 articles (indexed in Scopus and the Web of Science) on the impact of the COVID-19 outbreak on emerging human resources issues and related practices were published from 1 January 2020 to 31 January 2021. In this study, we conduct a systematic literature review on emerging studies in the business and management field to explore what the emerging human resource issues are during the COVID-19 pandemic and propose related practices to solve these issues. The analysis of the published literature identifies nine main human resource issues across 13 industries. The findings of this study suggest that COVID-19 has enormous impact on conventional human resource management and requires the theoretical and empirical attention of researchers. The propositions nominate related human resource practices to deal with emerging human resources issues and identify several research venues for future studies in this field. Full article
(This article belongs to the Special Issue COVID-19’s Risk Management and Its Impact on the Economy)
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16 pages, 1512 KiB  
Article
Data-Driven Services in Insurance: Potential Evolution and Impact in the Swiss Market
by Carlo Pugnetti and Mischa Seitz
J. Risk Financial Manag. 2021, 14(5), 227; https://doi.org/10.3390/jrfm14050227 - 19 May 2021
Cited by 11 | Viewed by 5006
Abstract
Using real-time customer data holds great potential for the insurance industry. The frequency and relevance of interactions can be improved to provide assistance in real time. Better prevention and risk management can significantly improve pricing and reduce losses. These changes, however, hold the [...] Read more.
Using real-time customer data holds great potential for the insurance industry. The frequency and relevance of interactions can be improved to provide assistance in real time. Better prevention and risk management can significantly improve pricing and reduce losses. These changes, however, hold the potential for structural changes in the industry. This research aims at understanding the potential path of the development of services in insurance and the challenges faced by insurers. A panel of industry experts provided the industry’s view, which was then compared with the responses of 1542 Swiss retail customers. We find that customers have high trust in insurance companies and are open to purchasing additional services, particularly for prevention and assistance. Insurance companies, however, are currently focusing on cost improvement measures. Customers are open to sourcing services from other providers, suggesting that insurance companies need to evolve their approach to take advantage of the current market window. Full article
(This article belongs to the Special Issue Sustainability in the Service Industries)
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16 pages, 744 KiB  
Article
The Influence of Oil Prices on Equity Returns of Canadian Energy Firms
by Sourav Batabyal and Robert Killins
J. Risk Financial Manag. 2021, 14(5), 226; https://doi.org/10.3390/jrfm14050226 - 18 May 2021
Cited by 4 | Viewed by 2501
Abstract
Using monthly data from January 2000 to August 2018, this paper examines how the Canadian oil and gas industry and individual firms’ equity prices react to oil price fluctuations, which are measured by the traditional West Texas Intermediate (WTI) benchmark and the Canada-specific [...] Read more.
Using monthly data from January 2000 to August 2018, this paper examines how the Canadian oil and gas industry and individual firms’ equity prices react to oil price fluctuations, which are measured by the traditional West Texas Intermediate (WTI) benchmark and the Canada-specific Western Canadian Select (WCS) benchmark. The findings provide support for the view that oil price movements are an important factor in explaining the equity returns of the overall industry and for many individual oil and gas firms in Canada. Both WTI and WCS measures provide statistically significant evidence, but the results support that WTI may still be the more relevant measure for Canadian-based firms. We also find that the spread between WTI and WCS has a minimal impact on the firms’ equity returns. Additional tests for asymmetric impacts of oil price movements on Canadian oil and gas equity returns have provided little evidence, whereas time-varying impacts are found for a handful of firms. The empirical findings predicated on the holistic view of the impacts of oil price fluctuations on equity market returns will enhance investor confidence and strengthen the Canadian economy. Full article
(This article belongs to the Special Issue Energy Economics and Finance)
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28 pages, 1293 KiB  
Article
Multiscale Stochastic Volatility Model with Heavy Tails and Leverage Effects
by Zhongxian Men, Tony S. Wirjanto and Adam W. Kolkiewicz
J. Risk Financial Manag. 2021, 14(5), 225; https://doi.org/10.3390/jrfm14050225 - 18 May 2021
Cited by 1 | Viewed by 2487
Abstract
This paper studies multiscale stochastic volatility models of financial asset returns. It specifies two components in the log-volatility process and allows for leverage/asymmetric effects from both components while return innovation terms follow a heavy/fat tailed Student t distribution. The two components are shown [...] Read more.
This paper studies multiscale stochastic volatility models of financial asset returns. It specifies two components in the log-volatility process and allows for leverage/asymmetric effects from both components while return innovation terms follow a heavy/fat tailed Student t distribution. The two components are shown to be important in capturing persistent dependence in return volatility, which is often absent in applications of stochastic volatility models which incorporate leverage/asymmetric effects. The models are applied to asset returns from a foreign currency market and an equity market. The model fits are assessed, and the proposed models are shown to compare favorably to the one-component asymmetric stochastic volatility models with Gaussian and Student t distributed innovation terms. Full article
(This article belongs to the Special Issue Volatility Modelling and Forecasting)
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16 pages, 464 KiB  
Article
Debt Market Trends and Predictors of Specialization: An Analysis of Pakistani Corporate Sector
by Kanwal Iqbal Khan, Faisal Qadeer, Mário Nuno Mata, Rui Miguel Dantas, João Xavier Rita and Jéssica Nunes Martins
J. Risk Financial Manag. 2021, 14(5), 224; https://doi.org/10.3390/jrfm14050224 - 17 May 2021
Cited by 6 | Viewed by 3518
Abstract
Recently, debt structure research has started focusing on the strategic perspective of financing choices, particularly to understand the reasons for debt specialization (DS). This paper examines trends of specialization over time and industry by using a comprehensive dataset on types of debt employed [...] Read more.
Recently, debt structure research has started focusing on the strategic perspective of financing choices, particularly to understand the reasons for debt specialization (DS). This paper examines trends of specialization over time and industry by using a comprehensive dataset on types of debt employed by the public limited companies during 2009–2018. The objective of the current study is to analyze the effect of debt market conditions by identifying significant predictors of DS. Time-series and cross-sectional results confirm the existence of DS, which is further validated by the findings of the cluster analysis. The empirical results indicate that overall, 61% of the companies solely rely on a single type of debt, mostly on short-term obligations accompanied by long-term secured and other debts. Moreover, small, mature, rated, group-affiliated, and low-leverage companies incline more towards this strategy. Credit rating, debt maturity, financial and interest coverage ratios serve as the primary determinants of the debt market that are significantly associated with the measures of DS. The results contribute to the capital structure literature by specifying that financing choice has an important implication in deciding the debt structure composition of the organizations. Full article
(This article belongs to the Special Issue Frontiers in Quantitative Finance)
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15 pages, 1225 KiB  
Article
Short-Term Capital Flows, Exchange Rate Expectation and Currency Internationalization: Evidence from China
by Mingming Li, Fengming Qin and Zhaoyong Zhang
J. Risk Financial Manag. 2021, 14(5), 223; https://doi.org/10.3390/jrfm14050223 - 17 May 2021
Cited by 8 | Viewed by 4866
Abstract
This paper intended to employ a portfolio approach to assess the effect of exchange rate expectation on Chinese RMB internationalization and empirically test the interactive effects among short-term capital flows, RMB appreciation expectation and the internationalization process using a VAR model with monthly [...] Read more.
This paper intended to employ a portfolio approach to assess the effect of exchange rate expectation on Chinese RMB internationalization and empirically test the interactive effects among short-term capital flows, RMB appreciation expectation and the internationalization process using a VAR model with monthly data ranging from February 2004 to December 2020. The results suggest that RMB exchange rate appreciation could lead to an increase in the foreign demand for RMB and RMB denominated assets, while RMB internationalization would attract more short-term capital inflow due to the reduced transaction costs. The empirical evidence from the VAR model estimation confirms the finding that expected RMB appreciation induces short-term capital inflow and promotes RMB internationalization. The robustness checks confirm the evidence. The results have important policy implication for RMB internationalization and for maintaining a sound and stable financial system. Full article
(This article belongs to the Special Issue Monetary and Financial Market Integration in East Asia)
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29 pages, 2277 KiB  
Article
Risk Spillover during the COVID-19 Global Pandemic and Portfolio Management
by Mohamed Yousfi, Abderrazak Dhaoui and Houssam Bouzgarrou
J. Risk Financial Manag. 2021, 14(5), 222; https://doi.org/10.3390/jrfm14050222 - 14 May 2021
Cited by 22 | Viewed by 5243
Abstract
This paper aims to examine the volatility spillover, diversification benefits, and hedge ratios between U.S. stock markets and different financial variables and commodities during the pre-COVID-19 and COVID-19 crisis, using daily data and multivariate GARCH models. Our results indicate that the risk spillover [...] Read more.
This paper aims to examine the volatility spillover, diversification benefits, and hedge ratios between U.S. stock markets and different financial variables and commodities during the pre-COVID-19 and COVID-19 crisis, using daily data and multivariate GARCH models. Our results indicate that the risk spillover has reached the highest level during the COVID-19 period, compared to the pre-COVID period, which means that the COVID-19 pandemic enforced the risk spillover between U.S. stock markets and the remains assets. We confirm the economic benefit of diversification in both tranquil and crisis periods (e.g., a negative dynamic conditional correlation between the VIX and SP500). Moreover, the hedging analysis exhibits that the Dow Jones Islamic has the highest hedging effectiveness either before or during the recent COVID19 crisis, offering better resistance to uncertainty caused by unpredictable turmoil such as the COVID19 outbreak. Our finding may have some implications for portfolio managers and investors to reduce their exposure to the risk in their portfolio construction. Full article
(This article belongs to the Special Issue Financial Markets in Times of Crisis)
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17 pages, 1242 KiB  
Article
Benchmarking—A Way of Finding Risk Factors in Business Performance
by Jarmila Horváthová, Martina Mokrišová and Mária Vrábliková
J. Risk Financial Manag. 2021, 14(5), 221; https://doi.org/10.3390/jrfm14050221 - 14 May 2021
Cited by 7 | Viewed by 9245
Abstract
The purpose of this study was to emphasize that the Data Envelopment Analysis (DEA) method is an important benchmarking tool which provides necessary information for improving business performance. To fulfil the abovementioned goal, we used a sample of 48 Slovak companies involved in [...] Read more.
The purpose of this study was to emphasize that the Data Envelopment Analysis (DEA) method is an important benchmarking tool which provides necessary information for improving business performance. To fulfil the abovementioned goal, we used a sample of 48 Slovak companies involved in the field of heat supply. As their position in the economic and social environment of the country is essential, considerable attention should be paid to improving their performance. In addition to the DEA method, we applied the Best Value Method (BVM). We found that DEA is a highly important benchmarking tool, as it provides benchmarks for units that have problems with performance and helps us to reveal risk performance factors. The DEA method also allows us to determine target values of indicators. The originality of this paper is in its comparison of the results of the BVM and the DEA methods. Full article
(This article belongs to the Special Issue Risk and Financial Consequences)
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19 pages, 2091 KiB  
Article
The Application of Graphic Methods and the DEA in Predicting the Risk of Bankruptcy
by Róbert Štefko, Jarmila Horváthová and Martina Mokrišová
J. Risk Financial Manag. 2021, 14(5), 220; https://doi.org/10.3390/jrfm14050220 - 13 May 2021
Cited by 13 | Viewed by 3134
Abstract
The paper deals with the issue of analyzing the financial failure of businesses. The aim was to select key performance indicators entering the DEA model. The research was carried out on a sample of 343 Slovak heat management companies. When addressing the research [...] Read more.
The paper deals with the issue of analyzing the financial failure of businesses. The aim was to select key performance indicators entering the DEA model. The research was carried out on a sample of 343 Slovak heat management companies. When addressing the research problem, we made use of multidimensional scaling (MDS) and principal component analysis (PCA), which pointed out the areas of financial health of companies that may predict their financial failure. The core of our interest and research was the data envelopment analysis (DEA) method, which represents a more exact approach to the assessment of financial health. The important finding is that the statistical graphical methods—PCA and MDS—are very helpful in identifying outliers and selecting key performance indicators entering the DEA model. The benefit of the paper is the identification of companies that are at risk of bankruptcy using the DEA method. The originality is the selection of key inputs and outputs to the DEA model by the PCA method. Full article
(This article belongs to the Special Issue Modern Methods of Bankruptcy Prediction)
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24 pages, 1668 KiB  
Review
A Systematic and Critical Review on the Research Landscape of Finance in Vietnam from 2008 to 2020
by Manh-Tung Ho, Ngoc-Thang B. Le, Hung-Long D. Tran, Quoc-Hung Nguyen, Manh-Ha Pham, Minh-Hoang Ly, Manh-Toan Ho, Minh-Hoang Nguyen and Quan-Hoang Vuong
J. Risk Financial Manag. 2021, 14(5), 219; https://doi.org/10.3390/jrfm14050219 - 12 May 2021
Cited by 2 | Viewed by 9533
Abstract
This paper endeavors to understand the research landscape of finance research in Vietnam during the period 2008 to 2020 and predict the key defining future research directions. Using the comprehensive database of Vietnam’s international publications in social sciences and humanities, we extract a [...] Read more.
This paper endeavors to understand the research landscape of finance research in Vietnam during the period 2008 to 2020 and predict the key defining future research directions. Using the comprehensive database of Vietnam’s international publications in social sciences and humanities, we extract a dataset of 314 papers on finance topics in Vietnam from 2008 to 2020. Then, we apply a systematic approach to analyze four important themes: Structural issues, Banking system, Firm issues, and Financial psychology and behavior. Overall, there have been three noticeable trends within finance research in Vietnam: (1) assessment of financial policies or financial regulation, (2) deciphering the correlates of firms’ financial performances, and (3) opportunities and challenges in adopting innovations and ideas from foreign financial market systems. Our analysis identifies several fertile areas for future research, including financial market analysis in the post-COVID-19 eras, fintech, and green finance. Full article
(This article belongs to the Special Issue Financial Markets—The Response in Crisis Moments)
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12 pages, 268 KiB  
Article
The Nexus between Financial Performance and Equilibrium: Empirical Evidence on Publicly Traded Companies from the Global Financial Crisis Up to the COVID-19 Pandemic
by Larissa Batrancea
J. Risk Financial Manag. 2021, 14(5), 218; https://doi.org/10.3390/jrfm14050218 - 12 May 2021
Cited by 16 | Viewed by 5310
Abstract
Financial performance and financial equilibrium are two key aspects that should be monitored by any business manager interested in passing the test of time and overcoming unpredictable events such as economic crises. The organic link between financial performance and financial equilibrium has rarely [...] Read more.
Financial performance and financial equilibrium are two key aspects that should be monitored by any business manager interested in passing the test of time and overcoming unpredictable events such as economic crises. The organic link between financial performance and financial equilibrium has rarely been studied in the long run for companies listed on the stock market. The present article fills this gap in the literature by examining the degree to which financial performance influenced long-term financial equilibrium using data from 34 major companies publicly traded on the New York Stock Exchange and operating around the world in a wide variety of industries and sectors. The period of analysis spread over a decade (2007Q1–2020Q3) in order to cover two major crises that have marked the dawn of the third millennium and occurred relatively close to one another: the 2008 financial meltdown and the COVID-19 pandemic crisis. By means of panel data modelling, the study showed that the short-term and long-term financial equilibria of these public companies measured by current ratio, quick ratio and debt to equity ratio were significantly impacted by different financial performance indicators. The study addresses various implications of the empirical results and lays out avenues for future research. Full article
(This article belongs to the Special Issue Risk and Financial Management of COVID-19 in Business, Economics and Finance)
14 pages, 301 KiB  
Article
The Impact of Bank Specific and Macro-Economic Factors on Non-Performing Loans in the Banking Sector: Evidence from an Emerging Economy
by Shakeel Ahmed, M. Ejaz Majeed, Eleftherios Thalassinos and Yannis Thalassinos
J. Risk Financial Manag. 2021, 14(5), 217; https://doi.org/10.3390/jrfm14050217 - 11 May 2021
Cited by 23 | Viewed by 10925
Abstract
The current study examines macro-economic and bank specific determinants of non-performing loans (NPLs) for commercial banks from 2008–2018. The Pakistani banking sector has observed a significant increase in NPLs. In addition, the current study is undertaken to fill this gap in the literature [...] Read more.
The current study examines macro-economic and bank specific determinants of non-performing loans (NPLs) for commercial banks from 2008–2018. The Pakistani banking sector has observed a significant increase in NPLs. In addition, the current study is undertaken to fill this gap in the literature as most of the prior studies focus on the developed markets. In the current study, we prefer the system GMM estimator. Its reliability depends on the validity of the instruments. To testing the second-order serial correlation, we apply the J test for testing the validity of the instruments and the Arellano–Bond AR (2) test. Using dynamic-GMM estimations, we find that credit growth, net interest margin, loan loss provision, and bank diversification significantly increase NPLs, while operating efficiency, bank size, and ROA lower NPLs. In addition, higher interest rates, exchange rates, and political risk significantly increase NPLs, while GDP growth decreases NPLs. This paper provides a timely insight to management and policy makers about the determinants of NPLs. The findings help management to take corrective actions and policy makers may take into consideration the significance of macro-economic conditions while formulating policy regarding NPLs. Likewise, the study provides insight to potential investors to consider the findings while selecting better investment opportunity. The current study is the first of its kind focusing on the link among bank specific, macroeconomic variables, and non-performing loans within the specific context of an emerging economy, Pakistan. Full article
(This article belongs to the Section Financial Markets)
20 pages, 860 KiB  
Article
Model of Assessing the Overdue Debts in a Commercial Bank Using Neuro-Fuzzy Technologies
by Dmytro Kovalenko, Olga Afanasieva, Nani Zabuta, Tetiana Boiko and Rosen Rosenov Baltov
J. Risk Financial Manag. 2021, 14(5), 216; https://doi.org/10.3390/jrfm14050216 - 10 May 2021
Cited by 3 | Viewed by 3583
Abstract
This article considers the problems of overdue credit debt and the creation of effective methods to manage problem debts in banks. The purpose of this paper is to study the problem of overdue credit debt and create effective methods to manage problem debts [...] Read more.
This article considers the problems of overdue credit debt and the creation of effective methods to manage problem debts in banks. The purpose of this paper is to study the problem of overdue credit debt and create effective methods to manage problem debts in financial institutions. Based on a combination of tools of fuzzy logic theory and artificial neural networks, an economic-mathematical model of collection scoring was built. Kohonen self-organizing maps were used to set the parameters of membership functions in the process of fuzzification of quantitative variables of the built model. Data were taken from the official websites of four Bulgarian banks for 2015–2019. The volume of the prepared sample amounted to 1000 credit agreements with active overdue payments. The practical value of the built model of collection scoring for the recovery of overdue debt lies in the possibility to make recommendations for work with each segment of the portfolio of overdue loans in accordance with the calculated level of credit risk. The introduction of credit risk assessment models based on neuro-fuzzy technologies in the work of financial institutions will have a positive impact on the financial results of lending activities of banks. Full article
(This article belongs to the Special Issue Mechanisms and Models of Risk Management)
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18 pages, 1344 KiB  
Article
New Dataset for Forecasting Realized Volatility: Is the Tokyo Stock Exchange Co-Location Dataset Helpful for Expansion of the Heterogeneous Autoregressive Model in the Japanese Stock Market?
by Takuo Higashide, Katsuyuki Tanaka, Takuji Kinkyo and Shigeyuki Hamori
J. Risk Financial Manag. 2021, 14(5), 215; https://doi.org/10.3390/jrfm14050215 - 10 May 2021
Cited by 3 | Viewed by 3463
Abstract
This study analyzes the importance of the Tokyo Stock Exchange Co-Location dataset (TSE Co-Location dataset) to forecast the realized volatility (RV) of Tokyo stock price index futures. The heterogeneous autoregressive (HAR) model is a popular linear regression model used to forecast RV. This [...] Read more.
This study analyzes the importance of the Tokyo Stock Exchange Co-Location dataset (TSE Co-Location dataset) to forecast the realized volatility (RV) of Tokyo stock price index futures. The heterogeneous autoregressive (HAR) model is a popular linear regression model used to forecast RV. This study expands the HAR model using the TSE Co-Location dataset, stock full-board dataset and market volume dataset based on the random forest method, which is a popular machine learning algorithm and a nonlinear model. The TSE Co-Location dataset is a new dataset. This is the only information that shows the transaction status of high-frequency traders. In contrast, the stock full-board dataset shows the status of buying and selling dominance. The market volume dataset is used as a proxy for liquidity and is recognized as important information in finance. To the best of our knowledge, this study is the first to use the TSE co-location dataset. The experimental results show that our model yields a higher forecast out-of-sample accuracy of RV than the HAR model. Moreover, we find that the TSE Co-Location dataset has become more important in recent years, along with the increasing importance of high-frequency trading. Full article
(This article belongs to the Special Issue AI and Financial Markets)
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12 pages, 447 KiB  
Article
Firm Credit Scoring: A Series Two-Stage DEA Bootstrapped Approach
by Ioannis E. Tsolas
J. Risk Financial Manag. 2021, 14(5), 214; https://doi.org/10.3390/jrfm14050214 - 10 May 2021
Viewed by 2260
Abstract
This paper employs a cross-sectional research design to collect quantitative data for a group of Greek pharmaceutical companies in order to evaluate their credit risk. The data are processed using a variety of quantitative approaches, including series two-stage data envelopment analysis (DEA) combined [...] Read more.
This paper employs a cross-sectional research design to collect quantitative data for a group of Greek pharmaceutical companies in order to evaluate their credit risk. The data are processed using a variety of quantitative approaches, including series two-stage data envelopment analysis (DEA) combined with bootstrap and hierarchical clustering. The results of the two-stage DEA bootstrapped analysis indicate that the key problem with the firms’ performance is a lack of effectiveness rather than operating efficiency. The lack of a correlation between operating efficiency and effectiveness indicates that the firms’ performance metrics are unrelated. As a result, a bootstrapped DEA-based synthetic indicator is developed to be used with the other performance metrics as inputs to hierarchical clustering to divide sample firms into credit risk clusters. The series two-stage DEA bootstrapped approach used in this study could aid firms in evaluating their performance and increasing their competitive advantages. Full article
(This article belongs to the Special Issue Quantitative Risk)
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17 pages, 663 KiB  
Article
Stress Testing and Systemic Risk Measures Using Elliptical Conditional Multivariate Probabilities
by Tomaso Aste
J. Risk Financial Manag. 2021, 14(5), 213; https://doi.org/10.3390/jrfm14050213 - 10 May 2021
Cited by 1 | Viewed by 3061
Abstract
Systemic risk, in a complex system with several interrelated variables, such as a financial market, is quantifiable from the multivariate probability distribution describing the reciprocal influence between the system’s variables. The effect of stress on the system is reflected by the change in [...] Read more.
Systemic risk, in a complex system with several interrelated variables, such as a financial market, is quantifiable from the multivariate probability distribution describing the reciprocal influence between the system’s variables. The effect of stress on the system is reflected by the change in such a multivariate probability distribution, conditioned to some of the variables being at a given stress’ amplitude. Therefore, the knowledge of the conditional probability distribution function can provide a full quantification of risk and stress propagation in the system. However, multivariate probabilities are hard to estimate from observations. In this paper, I investigate the vast family of multivariate elliptical distributions, discussing their estimation from data and proposing novel measures for stress impact and systemic risk in systems with many interrelated variables. Specific examples are described for the multivariate Student-t and the multivariate normal distributions applied to financial stress testing. An example of the US equity market illustrates the practical potentials of this approach. Full article
(This article belongs to the Special Issue Frontiers in Quantitative Finance)
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23 pages, 3113 KiB  
Article
Empirical Estimation of Intraday Yield Curves on the Italian Interbank Credit Market e-MID
by Anastasios Demertzidis and Vahidin Jeleskovic
J. Risk Financial Manag. 2021, 14(5), 212; https://doi.org/10.3390/jrfm14050212 - 8 May 2021
Cited by 1 | Viewed by 2714
Abstract
This paper introduces a major novelty: the empirical estimation of spot intraday yield curves based on tick-by-tick data on the Italian electronic interbank credit market (e-MID). To analyze the consequences of the recent financial crisis, we split the data into four periods, which [...] Read more.
This paper introduces a major novelty: the empirical estimation of spot intraday yield curves based on tick-by-tick data on the Italian electronic interbank credit market (e-MID). To analyze the consequences of the recent financial crisis, we split the data into four periods, which include events before, during, and after the recent financial crisis starting in 2007. Our first result is that, from a practical point of view, the intraday yield curve can be modeled by standard models for yield curves providing advantages for intraday trading on intraday interbank credit markets. Moreover, the estimates show that the systematic dynamics in the intraday yield curves during the turmoil were highly noticeable, resulting in a significantly better goodness-of-fit. Based on this fact, we infer that investors in the interbank credit market base their investment decisions on the effects of the intraday dynamics of intraday interest rates more intensively during a financial crisis. Therefore, the systematic impact on the e-MID appears to be stronger and econometric modeling of the intraday interest rate curve becomes even more attractive during a turmoil. Full article
(This article belongs to the Special Issue Economic Forecasting)
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15 pages, 1973 KiB  
Article
Modeling of Bank Credit Risk Management Using the Cost Risk Model
by Iryna Yanenkova, Yuliia Nehoda, Svetlana Drobyazko, Andrii Zavhorodnii and Lyudmyla Berezovska
J. Risk Financial Manag. 2021, 14(5), 211; https://doi.org/10.3390/jrfm14050211 - 7 May 2021
Cited by 12 | Viewed by 8049
Abstract
This article deals with the issue of managing bank credit risk using a cost risk model. Modeling of bank credit risk management was proposed based on neural-cell technologies, which expand the possibilities of modeling complex objects and processes and provide high reliability of [...] Read more.
This article deals with the issue of managing bank credit risk using a cost risk model. Modeling of bank credit risk management was proposed based on neural-cell technologies, which expand the possibilities of modeling complex objects and processes and provide high reliability of credit risk determination. The purpose of the article is to improve and develop methodical support and practical recommendations for reducing the level of risk based on the value-at-risk (VaR) methodology and its subsequent combination with methods of fuzzy programming and symbiotic methodical support. The model makes it possible to create decision support subsystems for nonperforming loan management based on the neuro-fuzzy approach. For this paper, economic and mathematical tools (based on the VaR methodology) were used, which made it possible to analyze and forecast the dynamics of overdue payment; assess the quality of the credit portfolio of the bank; determine possible trends in bank development. A scientific and practical approach is taken to assess and forecast the degree of credit problematicity by qualitative criteria using a mathematical model based on a fuzzy technology, which can forecast the increased risk of loan default at an early stage in the process of monitoring the loan portfolio and model forecasting changes in the degree of credit problematicity on change of indicators. A methodology is proposed for the analysis and forecasting of indicators of troubled loan debt, which should be implemented as software and included in the decision support system during the process of monitoring the risk of the bank’s credit portfolio. Full article
(This article belongs to the Special Issue Mechanisms and Models of Risk Management)
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12 pages, 436 KiB  
Article
How to Design Cryptocurrency Value and How to Secure Its Sustainability in the Market
by Soonduck Yoo
J. Risk Financial Manag. 2021, 14(5), 210; https://doi.org/10.3390/jrfm14050210 - 6 May 2021
Cited by 9 | Viewed by 4135
Abstract
The purpose of this study is to analyze the contents of cryptocurrency value design based on adaptability to the current market. It is also intended to provide a method of issuing cryptocurrency before its creation, and an operation method afterwards. Activities before the [...] Read more.
The purpose of this study is to analyze the contents of cryptocurrency value design based on adaptability to the current market. It is also intended to provide a method of issuing cryptocurrency before its creation, and an operation method afterwards. Activities before the creation of cryptocurrency must determine desirable behaviors and rewards to create value, and suggest countermeasures to prevent participants from engaging in undesirable behaviors. After the creation of a cryptocurrency, it is necessary to propose a method to induce scarcity and increase demand so that the value of the generated cryptocurrency can be sustained. To observe this, we looked at the contents of the value design of the eight types of cryptocurrencies currently in use in the market. Some cryptocurrencies, such as Bitcoin, are choosing mining as a reward, to secure scarcity for maintaining the value of cryptocurrency, limiting the amount of issuance, and burning the already issued cryptocurrency in the market. Also, increasing demand helps maintain the value of cryptocurrency. This study can contribute to supporting the growth of a healthy cryptocurrency market through cryptocurrency-related research. Full article
(This article belongs to the Special Issue Sustainability of Business Ecosystems)
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30 pages, 11173 KiB  
Article
The Heterogeneous Impact of Financialisation on Economic Growth in the Long Run
by Agne Setikiene and Mindaugas Butkus
J. Risk Financial Manag. 2021, 14(5), 209; https://doi.org/10.3390/jrfm14050209 - 5 May 2021
Cited by 1 | Viewed by 3736
Abstract
Financialisation, i.e., the process by which financial markets and their participants gain more influence over the functioning of enterprises/companies and the framework of the financial system, changes the functioning of the economic system, both at the macro- and microeconomic level. There is no [...] Read more.
Financialisation, i.e., the process by which financial markets and their participants gain more influence over the functioning of enterprises/companies and the framework of the financial system, changes the functioning of the economic system, both at the macro- and microeconomic level. There is no doubt that financialisation impacts economic growth. Still, research does not substantiate the heterogeneity of financialisation effects and does not provide a comprehensive analysis of the sources of heterogeneity. In most cases, researchers provide only theoretical insights into what may lead to different effects of financialisation on economic growth. This study empirically examines whether institutional quality and economic development intermediate the relationship between financialisation and economic growth using a panel of 96 countries over the period of 1996–2017 and least squares dummy variables (LSDV) estimator. We found that the impact of financialisation on economic growth differs across countries and that institutional quality and economic development are the sources of the heterogeneous impact of financialisation on economic growth. Full article
(This article belongs to the Special Issue Financial Development and Economic Growth)
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17 pages, 4338 KiB  
Article
Evaluation of Market with Accommodation Facilities Considering Risk Influence—Case Study Slovakia
by Adriana Csikosova, Katarina Culkova, Erik Weiss and Maria Janoskova
J. Risk Financial Manag. 2021, 14(5), 208; https://doi.org/10.3390/jrfm14050208 - 5 May 2021
Cited by 3 | Viewed by 2521
Abstract
Tourism currently contributes significantly to the national economy. When investing in the accommodation facility on the real-estate market, the tourism sector also represents a certain risk due to a high level of seasonality. This paper investigates the risks related to prices, income and [...] Read more.
Tourism currently contributes significantly to the national economy. When investing in the accommodation facility on the real-estate market, the tourism sector also represents a certain risk due to a high level of seasonality. This paper investigates the risks related to prices, income and occupancy of accommodation facilities for selected regions in Slovakia. The value of accommodation facilities is estimated using discounted cash flow, probabilistic distribution of rental prices and occupancy of accommodation facilities in selected Slovak regions. The results provide information for potential and profitable investments in exposed regions in tourism. The information can be used in the field of risk management to avoid or reduce the risk of risk investments. Although the resulting values were calculated only for some selected regions, the proposed procedure can be used for any region and compared with the current values. Full article
(This article belongs to the Special Issue Feature Papers on Tourism Economics, Finance, and Management)
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11 pages, 1051 KiB  
Article
Effects of the 2008 Financial Crisis and COVID-19 Pandemic on the Dynamic Relationship between the Chinese and International Fossil Fuel Markets
by Chaofeng Tang and Kentaka Aruga
J. Risk Financial Manag. 2021, 14(5), 207; https://doi.org/10.3390/jrfm14050207 - 5 May 2021
Cited by 12 | Viewed by 4241
Abstract
This study examines whether the dynamic relationship between the Chinese and international fossil markets changed during the 2008 financial crisis and is changing during the COVID-19 pandemic. The impact of the crises are analyzed by including the periods affected by the crises as [...] Read more.
This study examines whether the dynamic relationship between the Chinese and international fossil markets changed during the 2008 financial crisis and is changing during the COVID-19 pandemic. The impact of the crises are analyzed by including the periods affected by the crises as dummy variables in the VAR and VECM models. Monthly data for the 2000:1–2020:12 period were used in the study. Our results suggest that the effects of the COVID-19 on the linkages between the Chinese and international fossil fuel markets are not as evident compared to the 2008 financial crisis. The study identifies that the effects of the 2008 financial crisis and the COVID-19 pandemic on the linkages are mostly driven by the impacts of these crises on the Chinese fossil fuel markets. The study indicates the importance of controlling the risk involved in the Chinese fossil fuel market when events like the 2008 financial crisis and the COVID-19 pandemic are changing the linkages between the Chinese and international fossil fuel markets. Full article
(This article belongs to the Special Issue The Impact of COVID-19 on Economy, Energy, and Environment)
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11 pages, 287 KiB  
Article
Gamesmanship and Seasonality in U.S. Stock Returns
by Lucy F. Ackert and George Athanassakos
J. Risk Financial Manag. 2021, 14(5), 206; https://doi.org/10.3390/jrfm14050206 - 3 May 2021
Viewed by 2930
Abstract
We re-examined the seasonal pattern in the excess returns of highly visible American firms. In contrast to the seasonality for risky, less visible firms, we found that highly visible stocks display return seasonality that shows the opposite trend. Fund managers are prone to [...] Read more.
We re-examined the seasonal pattern in the excess returns of highly visible American firms. In contrast to the seasonality for risky, less visible firms, we found that highly visible stocks display return seasonality that shows the opposite trend. Fund managers are prone to gamesmanship, putting downward pressure on prices for highly visible firms at the beginning of the year, which is reversed later with buying pressure. Due to the bonus culture, fund managers start the year by buying small, risky stocks in order to beat benchmarks. Once targets are met, they adjust toward visible, less risky stocks to lock in returns, providing them with a seasonal returns pattern opposite to that of small firms. A re-examination is warranted because the world has become increasingly globalized, and some argue that managers’ incentives are aligned with investors due to increased scrutiny. We used analyst following as a proxy for visibility and examined the seasonal pattern for 1997–2018. Though the anomaly was first reported twenty years ago, it persists in recent data. Rational investors may be limited in their ability to arbitrage mispricing because institutional investors who drive the market are self-interested. Future research may examine the seasonal pattern in countries with more stringent regulation of financial professionals or with high-frequency data. Full article
(This article belongs to the Special Issue Financial Markets, Financial Volatility and Beyond)
25 pages, 356 KiB  
Article
The Effect of Industry Restructuring on Peer Firms
by Alex Holcomb and Paul Mason
J. Risk Financial Manag. 2021, 14(5), 205; https://doi.org/10.3390/jrfm14050205 - 3 May 2021
Viewed by 2224
Abstract
We study the bond price reaction of a merged firms peers, in order to better understand how the market responds to a restructuring. We argue that a merger announcement may signal the possibility of a merger wave to the industry, and in doing [...] Read more.
We study the bond price reaction of a merged firms peers, in order to better understand how the market responds to a restructuring. We argue that a merger announcement may signal the possibility of a merger wave to the industry, and in doing so, increase the conditional probability that peer firms might themselves be acquired in the future. However, while peer firm equity holders expect a direct benefit from a potential acquisition—in the form of a price premium—peer firm bond holders can only expect an indirect benefit—in the form of a risk reduction. Consistent with these hypotheses, we show that price reactions are stronger for firms that have a higher unconditional probability of being acquired ex-ante. In addition, we document that, cross-sectionally, the abnormal returns we observe from peer bondholders are concentrated among firms that have the highest expected risk reduction benefit from a potential acquisition. In order to distinguish a potential reduction in risk as the explicit return driver, we show that abnormal bond returns within firm (between different bond issues) are also concentrated among issues that have the highest expected risk reduction benefit. Full article
(This article belongs to the Special Issue Feature Papers on Financial Markets)
16 pages, 936 KiB  
Article
Month-End Regularities in the Overnight Bank Funding Markets
by Ahmed S. Baig and Drew B. Winters
J. Risk Financial Manag. 2021, 14(5), 204; https://doi.org/10.3390/jrfm14050204 - 3 May 2021
Cited by 1 | Viewed by 2422
Abstract
The money market rates in the United States exhibit various calendar patterns that are grounded in institutional and regulatory factors. In this paper, we document a new regularity in the overnight fed funds market. Specifically, we identify patterns of decreased volatility along with [...] Read more.
The money market rates in the United States exhibit various calendar patterns that are grounded in institutional and regulatory factors. In this paper, we document a new regularity in the overnight fed funds market. Specifically, we identify patterns of decreased volatility along with consistent and significant month-end rate drops in the fed fund rates. Our findings suggest that short-term liquidity requirements of the Basel III reforms are, in part, responsible for the regularity in fed funds. Full article
(This article belongs to the Special Issue Financial Markets in Times of Crisis)
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19 pages, 523 KiB  
Article
Artificial Intelligence Factory, Data Risk, and VCs’ Mediation: The Case of ByteDance, an AI-Powered Startup
by Peiyi Jia and Ciprian Stan
J. Risk Financial Manag. 2021, 14(5), 203; https://doi.org/10.3390/jrfm14050203 - 2 May 2021
Cited by 8 | Viewed by 9712
Abstract
The AI factory is an effective way of managing artificial intelligence (AI) processes, enabling broad AI deployment in a firm. The purpose of this study is to explore the role of the AI factory in an entrepreneurship context. How do AI-powered startups leverage [...] Read more.
The AI factory is an effective way of managing artificial intelligence (AI) processes, enabling broad AI deployment in a firm. The purpose of this study is to explore the role of the AI factory in an entrepreneurship context. How do AI-powered startups leverage AI to grow, and manage data risks? What is the role of venture capitalists in this process? We answer these research questions by conducting an in-depth study of an AI-powered startup: ByteDance. Our study extends both AI and entrepreneurship literature by showing that AI-powered startups adopt the AI factory approach to optimize scale, scope, and learning. Our discussion also emphasizes the critical role played by venture capitalists in assisting AI-powered startups in building AI factories and in reducing data risk. Full article
(This article belongs to the Special Issue Venture Capital and Private Equity)
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18 pages, 349 KiB  
Article
Large Deviations for a Class of Multivariate Heavy-Tailed Risk Processes Used in Insurance and Finance
by Miriam Hägele and Jaakko Lehtomaa
J. Risk Financial Manag. 2021, 14(5), 202; https://doi.org/10.3390/jrfm14050202 - 2 May 2021
Cited by 2 | Viewed by 2089
Abstract
Modern risk modelling approaches deal with vectors of multiple components. The components could be, for example, returns of financial instruments or losses within an insurance portfolio concerning different lines of business. One of the main problems is to decide if there is any [...] Read more.
Modern risk modelling approaches deal with vectors of multiple components. The components could be, for example, returns of financial instruments or losses within an insurance portfolio concerning different lines of business. One of the main problems is to decide if there is any type of dependence between the components of the vector and, if so, what type of dependence structure should be used for accurate modelling. We study a class of heavy-tailed multivariate random vectors under a non-parametric shape constraint on the tail decay rate. This class contains, for instance, elliptical distributions whose tail is in the intermediate heavy-tailed regime, which includes Weibull and lognormal type tails. The study derives asymptotic approximations for tail events of random walks. Consequently, a full large deviations principle is obtained under, essentially, minimal assumptions. As an application, an optimisation method for a large class of Quota Share (QS) risk sharing schemes used in insurance and finance is obtained. Full article
(This article belongs to the Special Issue Nonparametric Analysis of Economic and Financial Time Series Data)
12 pages, 1082 KiB  
Article
Portfolio Optimization Constrained by Performance Attribution
by Yuan Hu, W. Brent Lindquist and Svetlozar T. Rachev
J. Risk Financial Manag. 2021, 14(5), 201; https://doi.org/10.3390/jrfm14050201 - 2 May 2021
Cited by 4 | Viewed by 2714
Abstract
This paper investigates performance attribution measures as a basis for constraining portfolio optimization. We employ optimizations that minimize conditional value-at-risk and investigate two performance attributes, asset allocation (AA) and the selection effect (SE), as constraints on asset weights. The test portfolio consists of [...] Read more.
This paper investigates performance attribution measures as a basis for constraining portfolio optimization. We employ optimizations that minimize conditional value-at-risk and investigate two performance attributes, asset allocation (AA) and the selection effect (SE), as constraints on asset weights. The test portfolio consists of stocks from the Dow Jones Industrial Average index. Values for the performance attributes are established relative to two benchmarks, equi-weighted and price-weighted portfolios of the same stocks. Performance of the optimized portfolios is judged using comparisons of cumulative price and the risk-measures: maximum drawdown, Sharpe ratio, Sortino–Satchell ratio and Rachev ratio. The results suggest that achieving SE performance thresholds requires larger turnover values than that required for achieving comparable AA thresholds. The results also suggest a positive role in price and risk-measure performance for the imposition of constraints on AA and SE. Full article
(This article belongs to the Special Issue Frontiers in Quantitative Finance)
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