Journal Description
Risks
Risks
is an international, scholarly, peer-reviewed, open access journal for research and studies on insurance and financial risk management. Risks is published monthly online by MDPI.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High visibility: indexed within Scopus, ESCI (Web of Science), EconLit, EconBiz, RePEc, and other databases.
- Journal Rank: CiteScore - Q1 (Economics, Econometrics and Finance (miscellaneous))
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 20.4 days after submission; acceptance to publication is undertaken in 4.3 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers for a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done
Impact Factor:
2.2 (2022);
5-Year Impact Factor:
1.9 (2022)
Latest Articles
Key Determinants of Corporate Governance in Financial Institutions: Evidence from South Africa
Risks 2024, 12(6), 90; https://doi.org/10.3390/risks12060090 (registering DOI) - 30 May 2024
Abstract
The purpose of this study was to examine the key determinants of corporate governance in selected financial institutions. Using South African financial institutions as a unit of analysis, namely insurance companies and banks, the study employed a panel generalised method of moments (GMM)
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The purpose of this study was to examine the key determinants of corporate governance in selected financial institutions. Using South African financial institutions as a unit of analysis, namely insurance companies and banks, the study employed a panel generalised method of moments (GMM) model using a data set for the period from 2007 to 2020, to assess key determinants of corporate governance proxies identified for the study. The study sampled 21 South African financial institutions composed of Johannesburg Securities Exchange (JSE) listed and unlisted banks and insurance companies. To measure corporate governance, the study developed a composite index employing the principal components analysis (PCA) method. The findings revealed a positive and significant association between the corporate governance index and its lagged variables. Furthermore, a significant and positive link was found between the efficiency ratio and corporate governance index and capital adequacy ratio (CAR); corporate governance index and firm size; corporate governance index and leverage ratio (LEV); and corporate governance index and return on assets (ROA). However, a negative and significant correlation was found between financial stability and the corporate governance index. The link between return on equity (ROE) and corporate governance was insignificant. A small cohort of financial institutions was excluded because it was challenging to obtain complete annual reports to extract the required data. The study was limited to only five corporate governance measures, namely board diversity, board size, board composition (independent non-executive directors and non-executive directors), and board remuneration. The findings are anticipated to persuade developing countries to pay special attention to how corporate governance is measured.
Full article
(This article belongs to the Special Issue Risk Governance in the Finance and Insurance Industry)
Open AccessArticle
A Case Study of Bank Equity Valuation Methods Employed by South African, Nigerian and Kenyan Equity Researchers
by
Vusani Moyo and Ayodeji Michael Obadire
Risks 2024, 12(6), 89; https://doi.org/10.3390/risks12060089 - 27 May 2024
Abstract
The valuation of banks is inherently complicated because of the uncertainties arising from their information opaqueness and inherent risks. Unlike non-banking firms, banks require specialised equity-side valuation approaches. This study addresses a gap in the literature by examining valuation methods used by bank
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The valuation of banks is inherently complicated because of the uncertainties arising from their information opaqueness and inherent risks. Unlike non-banking firms, banks require specialised equity-side valuation approaches. This study addresses a gap in the literature by examining valuation methods used by bank equity researchers. The study used a total of 201 reports on South African banks (2018–2023), 56 reports on Nigerian banks (2018–2023), and 27 reports on Kenyan banks (2018–2023) to investigate the bank equity valuation methods utilised by analysts in the employ of Investec Ltd. and Standard Bank Group Ltd. The study’s findings show that Investec’s South African analysts predominantly used the warranted equity method, based on book value (BV), and return on equity (ROE), for valuing shares throughout the South African, Nigerian, and Kenyan banks surveyed. Furthermore, Standard Bank Group’s analysts employed this method, incorporating tangible net asset value (tNAV) and return on tangible equity (ROTE), for South African and Nigerian banks, but in Kenya their analysts used the residual income model to value the equities of the five Kenyan banks they covered. These findings suggest that the warranted equity method and the residual income model are the mostly used bank equity valuation methods in South Africa, Nigeria, and Kenya. The study concludes with relevant recommendations, offering significant insights for banks, regulators, and investors to make knowledgeable decisions concerning equity valuation.
Full article
Open AccessArticle
Some Results on Bivariate Squared Maximum Sharpe Ratio
by
Samane Al-sadat Mousavi, Ali Dolati and Ali Dastbaravarde
Risks 2024, 12(6), 88; https://doi.org/10.3390/risks12060088 - 24 May 2024
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The Sharpe ratio is a widely used tool for assessing investment strategy performance. An essential part of investing involves creating an appropriate portfolio by determining the optimal weights for desired assets. Before constructing a portfolio, selecting a set of investment opportunities is crucial.
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The Sharpe ratio is a widely used tool for assessing investment strategy performance. An essential part of investing involves creating an appropriate portfolio by determining the optimal weights for desired assets. Before constructing a portfolio, selecting a set of investment opportunities is crucial. In the absence of a risk-free asset, investment opportunities can be identified based on the Sharpe ratios of risky assets and their correlation. The maximum squared Sharpe ratio serves as a useful metric that summarizes the performance of an investment opportunity in a single value, considering the Sharpe ratios of assets and their correlation coefficients. However, the assumption of a normal distribution in asset returns, as implied by the Sharpe ratio and related metrics, may not always hold in practice. Non-normal returns with a non-linear dependence structure can result in an overestimation or underestimation of these metrics. Copula functions are commonly utilized to address non-normal dependence structures. This study examines the impact of asset dependence on the squared maximum Sharpe ratio using copulas and proposes a copula-based approach to tackle the estimation issue. The performance of the proposed estimator is illustrated through simulation and real-data analysis.
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Open AccessArticle
Integration of AI and IoT into Corporate Social Responsibility Strategies for Financial Risk Management and Sustainable Development
by
Anna Viktorovna Shkalenko and Anton V. Nazarenko
Risks 2024, 12(6), 87; https://doi.org/10.3390/risks12060087 - 23 May 2024
Abstract
This research explores the integration of artificial intelligence (AI) and the Internet of Things (IoT) within corporate social responsibility (CSR) strategies, focusing on financial risk management and sustainable development. Employing a novel Coevolutionary multi-paradigm approach to technological development, this study examines how these
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This research explores the integration of artificial intelligence (AI) and the Internet of Things (IoT) within corporate social responsibility (CSR) strategies, focusing on financial risk management and sustainable development. Employing a novel Coevolutionary multi-paradigm approach to technological development, this study examines how these technologies can be embedded into CSR practices to enhance sustainability and manage risks effectively. The findings reveal that successful integration depends significantly on the adaptability of institutional structures to support technological innovations. This study contributes to the literature by providing a comprehensive analysis of the intersection of AI, IoT, and CSR, highlighting the necessity for robust mechanisms and policies that ensure security, standardization, and sustainable use of emerging technologies. Through this investigation, this research offers a new perspective on leveraging advanced technologies to advance corporate sustainability and risk management objectives.
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(This article belongs to the Special Issue Managing Financial Risks Based on Corporate Social Responsibility for Sustainable Development II)
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Open AccessArticle
Commodity Market Risk: Examining Price Co-Movements in the Pakistan Mercantile Exchange
by
Falik Shear, Muhammad Bilal, Badar Nadeem Ashraf and Nasir Ali
Risks 2024, 12(6), 86; https://doi.org/10.3390/risks12060086 - 22 May 2024
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Commodity price co-movements significantly impact investment decisions. High correlations constrain portfolio diversification and limit risk mitigation potential. While international markets often exhibit strong price linkages, understanding national-level dynamics is crucial for effective portfolio optimization. In this paper, we examine the commodity price co-movements
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Commodity price co-movements significantly impact investment decisions. High correlations constrain portfolio diversification and limit risk mitigation potential. While international markets often exhibit strong price linkages, understanding national-level dynamics is crucial for effective portfolio optimization. In this paper, we examine the commodity price co-movements within three key sectors—energy, metals, and agriculture—in the specific context of Pakistan. Utilizing data from 13 January 2013 to 20 August 2020 and employing an autoregressive distributed lag (ARDL) model, we reveal a surprising finding: co-movement among these sectors is weak and primarily short-term. This challenges the conventional assumption of tight coupling in national markets and offers exciting implications for investors. Our analysis suggests that Pakistani commodities hold significant diversification potential, opening promising avenues for risk-reduction strategies within the national market.
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Open AccessArticle
Use of Prediction Bias in Active Learning and Its Application to Large Variable Annuity Portfolios
by
Hyukjun Gweon, Shu Li and Yangxuan Xu
Risks 2024, 12(6), 85; https://doi.org/10.3390/risks12060085 - 22 May 2024
Abstract
Given the computational challenges associated with valuing large variable annuity (VA) portfolios, a variety of data mining frameworks, including metamodeling and active learning, have been proposed in recent years. Active learning, a promising alternative to metamodeling, enhances the efficiency of VA portfolio assessments
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Given the computational challenges associated with valuing large variable annuity (VA) portfolios, a variety of data mining frameworks, including metamodeling and active learning, have been proposed in recent years. Active learning, a promising alternative to metamodeling, enhances the efficiency of VA portfolio assessments by adaptively improving a predictive regression model. This is achieved by augmenting data for model training with strategically selected informative samples. Successful application of active learning requires an effective metric in order to gauge the informativeness of data. Current sampling methods, which focus on prediction error-based informativeness, typically rely solely on prediction variance and assume an unbiased predictive model. In this paper, we address the fact that prediction bias can be nonnegligible in large VA portfolio valuation and investigate the impact of prediction bias in both the modeling and sampling stages of active learning. Our experimental results suggest that bias-based sampling can rival the efficacy of traditional ambiguity-based sampling, with its success contingent upon the extent of bias present in the predictive model.
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(This article belongs to the Special Issue Risks Journal: A Decade of Advancing Knowledge and Shaping the Future)
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Open AccessArticle
Multi-Timescale Recurrent Neural Networks Beat Rough Volatility for Intraday Volatility Prediction
by
Damien Challet and Vincent Ragel
Risks 2024, 12(6), 84; https://doi.org/10.3390/risks12060084 - 22 May 2024
Abstract
We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes with long memory or highly disparate timescales. We compare the ability of vanilla and extended long short-term memory
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We extend recurrent neural networks to include several flexible timescales for each dimension of their output, which mechanically improves their abilities to account for processes with long memory or highly disparate timescales. We compare the ability of vanilla and extended long short-term memory networks (LSTMs) to predict the intraday volatility of a collection of equity indices known to have a long memory. Generally, the number of epochs needed to train the extended LSTMs is divided by about two, while the variation in validation and test losses among models with the same hyperparameters is much smaller. We also show that the single model with the smallest validation loss systemically outperforms rough volatility predictions for the average intraday volatility of equity indices by about 20% when trained and tested on a dataset with multiple time series.
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(This article belongs to the Special Issue Advances in Volatility Modeling and Risk in Markets)
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Cyber Risk in Insurance: A Quantum Modeling
by
Claude Lefèvre, Muhsin Tamturk, Sergey Utev and Marco Carenzo
Risks 2024, 12(5), 83; https://doi.org/10.3390/risks12050083 - 20 May 2024
Abstract
In this research, we consider cyber risk in insurance using a quantum approach, with a focus on the differences between reported cyber claims and the number of cyber attacks that caused them. Unlike the traditional probabilistic approach, quantum modeling makes it possible to
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In this research, we consider cyber risk in insurance using a quantum approach, with a focus on the differences between reported cyber claims and the number of cyber attacks that caused them. Unlike the traditional probabilistic approach, quantum modeling makes it possible to deal with non-commutative event paths. We investigate the classification of cyber claims according to different cyber risk behaviors to enable more precise analysis and management of cyber risks. Additionally, we examine how historical cyber claims can be utilized through the application of copula functions for dependent insurance claims. We also discuss classification, likelihood estimation, and risk-loss calculation within the context of dependent insurance claim data.
Full article
(This article belongs to the Special Issue Advancements in Actuarial Mathematics and Risk Theory)
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Open AccessArticle
Bitcoin Volatility and Intrinsic Time Using Double-Subordinated Lévy Processes
by
Abootaleb Shirvani, Stefan Mittnik, William Brent Lindquist and Svetlozar Rachev
Risks 2024, 12(5), 82; https://doi.org/10.3390/risks12050082 - 20 May 2024
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We propose a doubly subordinated Lévy process, the normal double inverse Gaussian (NDIG), to model the time series properties of the cryptocurrency bitcoin. By using two subordinated processes, NDIG captures both the skew and fat-tailed properties of, as well as the intrinsic time
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We propose a doubly subordinated Lévy process, the normal double inverse Gaussian (NDIG), to model the time series properties of the cryptocurrency bitcoin. By using two subordinated processes, NDIG captures both the skew and fat-tailed properties of, as well as the intrinsic time driving, bitcoin returns and gives rise to an arbitrage-free option pricing model. In this framework, we derive two bitcoin volatility measures. The first combines NDIG option pricing with the Chicago Board Options Exchange VIX model to compute an implied volatility; the second uses the volatility of the unit time increment of the NDIG model. Both volatility measures are compared to the volatility based on the historical standard deviation. With appropriate linear scaling, the NDIG process perfectly captures the observed in-sample volatility.
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Open AccessArticle
Board Characteristics and Bank Stock Performance: Empirical Evidence from the MENA Region
by
Antoine B. Awad, Robert Gharios, Bashar Abu Khalaf and Lena A. Seissian
Risks 2024, 12(5), 81; https://doi.org/10.3390/risks12050081 - 14 May 2024
Abstract
This study examined the relationship between the board characteristics and stock performance of commercial banks. Our analysis is based on a sample of 65 banks across 10 MENA countries and their quantitative data extracted between 2013 and 2022. This research employed pooled OLS,
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This study examined the relationship between the board characteristics and stock performance of commercial banks. Our analysis is based on a sample of 65 banks across 10 MENA countries and their quantitative data extracted between 2013 and 2022. This research employed pooled OLS, and fixed and random effect regression to confirm the association between board size, board independence, number of board meetings, and CEO duality with stock performance measured by the bank’s share price and market-to-book ratio. Further, several control variables were utilized such as the bank’s capital adequacy, profitability, and size. The empirical findings reveal that board independence positively affects the bank stock performance while the board size shows a negative relationship. This suggests that banks with fewer board members and high independence levels have their shares outperforming others. However, we found that having frequent board meetings per year and separate roles for the CEO and chairman have no impact on bank stock performance. Moreover, the findings indicate that the bank’s capital adequacy, size, and profitability have a positive effect on the stock performance. To test the robustness of our analysis, we implemented a one-limit Tobit model, which enables lower-bound censoring, and obtained similar findings thus confirming our hypotheses. From a practical perspective, our findings highlight the importance of the board size and the directors’ independence to MENA regulators and policymakers in an effort to implement an effective corporate governance system. Specifically, MENA banks are advised to decrease the number of board members, and this should reduce the number of annual board meetings which, in turn, should maximize performance.
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Open AccessArticle
Trading Activity in the Corporate Bond Market: A SAD Tale of Macro-Announcements and Behavioral Seasonality?
by
James J. Forest, Ben S. Branch and Brian T. Berry
Risks 2024, 12(5), 80; https://doi.org/10.3390/risks12050080 - 14 May 2024
Abstract
This study investigates the determinants of trading activity in the U.S. corporate bond market, focusing on the effects of Seasonal Affective Disorder (SAD) and macroeconomic announcements. Employing the General-to-Specific (Gets) Autometrics methodology, we identify distinct behavioral responses between retail and institutional investors to
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This study investigates the determinants of trading activity in the U.S. corporate bond market, focusing on the effects of Seasonal Affective Disorder (SAD) and macroeconomic announcements. Employing the General-to-Specific (Gets) Autometrics methodology, we identify distinct behavioral responses between retail and institutional investors to SAD, noting a significant impact on retail trading volumes but not on institutional trading or bond returns. This discovery extends the understanding of behavioral finance within the context of bond markets, diverging from established findings in equity and Treasury markets. Additionally, our analysis delineates the influence of macroeconomic announcements on trading activities, offering new insights into the market’s reaction to economic news. This study’s findings contribute to the broader literature on market microstructure and behavioral finance, providing empirical evidence on the interplay between psychological factors and macroeconomic information flow within corporate bond markets. By addressing these specific aspects with rigorous econometric techniques, our research enhances the comprehension of trading dynamics in less transparent markets, offering valuable perspectives for academics, investors, risk managers, and policymakers.
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(This article belongs to the Special Issue Risk Analysis in Financial Crisis and Stock Market)
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Non-Differentiable Loss Function Optimization and Interaction Effect Discovery in Insurance Pricing Using the Genetic Algorithm
by
Robin Van Oirbeek, Félix Vandervorst, Thomas Bury, Gireg Willame, Christopher Grumiau and Tim Verdonck
Risks 2024, 12(5), 79; https://doi.org/10.3390/risks12050079 - 14 May 2024
Abstract
Insurance pricing is the process of determining the premiums that policyholders pay in exchange for insurance coverage. In order to estimate premiums, actuaries use statistical based methods, assessing various factors such as the probability of certain events occurring (like accidents or damages), where
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Insurance pricing is the process of determining the premiums that policyholders pay in exchange for insurance coverage. In order to estimate premiums, actuaries use statistical based methods, assessing various factors such as the probability of certain events occurring (like accidents or damages), where the Generalized Linear Models (GLMs) are the industry standard method. Traditional GLM approaches face limitations due to non-differentiable loss functions and expansive variable spaces, including both main and interaction terms. In this study, we address the challenge of selecting relevant variables for GLMs used in non-life insurance pricing both for frequency or severity analyses, amidst an increasing volume of data and variables. We propose a novel application of the Genetic Algorithm (GA) to efficiently identify pertinent main and interaction effects in GLMs, even in scenarios with a high variable count and diverse loss functions. Our approach uniquely aligns GLM predictions with those of black box machine learning models, enhancing their interpretability and reliability. Using a publicly available non-life motor data set, we demonstrate the GA’s effectiveness by comparing its selected GLM with a Gradient Boosted Machine (GBM) model. The results show a strong consistency between the main and interaction terms identified by GA for the GLM and those revealed in the GBM analysis, highlighting the potential of our method to refine and improve pricing models in the insurance sector.
Full article
(This article belongs to the Special Issue Statistical Applications to Insurance and Risk)
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Open AccessFeature PaperArticle
Exploring Entropy-Based Portfolio Strategies: Empirical Analysis and Cryptocurrency Impact
by
Nicolò Giunta, Giuseppe Orlando, Alessandra Carleo and Jacopo Maria Ricci
Risks 2024, 12(5), 78; https://doi.org/10.3390/risks12050078 - 11 May 2024
Abstract
This study addresses market concentration among major corporations, highlighting the utility of relative entropy for understanding diversification strategies. It introduces entropic value at risk (EVaR) as a coherent risk measure, which is an upper bound to the conditional value at risk (CVaR), and
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This study addresses market concentration among major corporations, highlighting the utility of relative entropy for understanding diversification strategies. It introduces entropic value at risk (EVaR) as a coherent risk measure, which is an upper bound to the conditional value at risk (CVaR), and explores its generalization, relativistic value at risk (RLVaR), rooted in Kaniadakis entropy. Through extensive empirical analysis on both developed (i.e., S&P 500 and Euro Stoxx 50) and developing markets (i.e., BIST 100 and Bovespa), the study evaluates entropy-based criteria in portfolio selection, investigates model behavior across different market types, and assesses the impact of cryptocurrency introduction on portfolio performance and diversification. The key finding indicates that entropy measures effectively identify optimal portfolios, particularly in scenarios of heightened risk and increased concentration, crucial for mitigating negative net performances during low returns or high turnover. Bitcoin is primarily used for diversification and performance enhancement in the BIST 100 index, while its allocation in other markets remains minimal or non-existent, confirming the extreme concentration observed in stock markets dominated by a few leading stocks.
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(This article belongs to the Special Issue Portfolio Theory, Financial Risk Analysis and Applications)
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Open AccessArticle
Uncertainty Reduction in Operational Risk Management Process
by
Guy Burstein and Inon Zuckerman
Risks 2024, 12(5), 77; https://doi.org/10.3390/risks12050077 - 11 May 2024
Abstract
This paper proposes a new framework to reduce the variance and uncertainty in the risk assessment process. Today, this process is susceptible to background noise from sources of human factor biases and erroneous measurements. Our new framework consists of deconstructing the likelihood of
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This paper proposes a new framework to reduce the variance and uncertainty in the risk assessment process. Today, this process is susceptible to background noise from sources of human factor biases and erroneous measurements. Our new framework consists of deconstructing the likelihood of failure function into its sub-factor and then reconstructing it in a formula that can reduce the variance and biases of a human auditor judgment. We tested our new framework on both a questionnaire study and a simulation of the risk assessment process, and the improvement in reducing the variance is significant.
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(This article belongs to the Special Issue Financial Analysis, Corporate Finance and Risk Management)
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Test of Volatile Behaviors with the Asymmetric Stochastic Volatility Model: An Implementation on Nasdaq-100
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Elchin Suleymanov, Magsud Gubadli and Ulvi Yagubov
Risks 2024, 12(5), 76; https://doi.org/10.3390/risks12050076 - 3 May 2024
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The present study aimed to investigate the presence of asymmetric stochastic volatility and leverage effects within the Nasdaq-100 index. This index is widely regarded as an important indicator for investors. We focused on the nine leading stocks within the index, which are highly
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The present study aimed to investigate the presence of asymmetric stochastic volatility and leverage effects within the Nasdaq-100 index. This index is widely regarded as an important indicator for investors. We focused on the nine leading stocks within the index, which are highly popular and hold significant weight in the investment world. These stocks are Netflix, PayPal, Google, Intel, Microsoft, Amazon, Tesla, Apple, and Meta. The study covered the period between 3 January 2017 and 30 January 2023, and we employed the EViews and WinBUGS applications to conduct the analysis. We began by calculating the logarithmic difference to obtain the return series. We then performed a sample test with 100,000 iterations, excluding the first 10,000 samples to eliminate the initial bias of the coefficients. This left us with 90,000 samples for analysis. Using the results of the asymmetric stochastic volatility model, we evaluated both the Nasdaq-100 index as a whole and the volatility persistence, predictability, and correlation levels of individual stocks. This allowed us to evaluate the ability of individual stocks to represent the characteristics of the Nasdaq-100 index. Our findings revealed a dense clustering of volatility, both for the Nasdaq-100 index and the nine individual stocks. We observed that this volatility is continuous but has a predictable impact on variability. Moreover, apart from Intel, all the stocks in the model exhibited both leverage effects and the presence of asymmetric relationships, as did the Nasdaq-100 index. Overall, our results show that the characteristics of stocks in the model are like the volatility characteristic of the Nasdaq-100 index and can represent it.
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Open AccessFeature PaperArticle
Analyzing the Influence of Risk Models and Investor Risk-Aversion Disparity on Portfolio Selection in Community Solar Projects: A Comparative Case Study
by
Mahmoud Shakouri, Chukwuma Nnaji, Saeed Banihashemi and Khoung Le Nguyen
Risks 2024, 12(5), 75; https://doi.org/10.3390/risks12050075 - 30 Apr 2024
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This study examines the impact of risk models and investors’ risk aversion on the selection of community solar portfolios. Various risk models to account for the volatility in the electrical power output of community solar, namely variance (Var), SemiVariance (SemiVar), mean absolute deviation
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This study examines the impact of risk models and investors’ risk aversion on the selection of community solar portfolios. Various risk models to account for the volatility in the electrical power output of community solar, namely variance (Var), SemiVariance (SemiVar), mean absolute deviation (MAD), and conditional value at risk (CVaR), were considered. A statistical model based on modern portfolio theory was employed to simulate investors’ risk aversion in the context of community solar portfolio selection. The results of this study showed that the choice of risk model that aligns with investors’ risk-aversion level plays a key role in realizing more return and safeguarding against volatility in power generation. In particular, the findings of this research revealed that the CVaR model provides higher returns at the cost of greater volatility in power generation compared to other risk models. In contrast, the MAD model offered a better tradeoff between risk and return, which can appeal more to risk-averse investors. Based on the simulation results, a new approach was proposed for optimizing the portfolio selection process for investors with divergent risk-aversion levels by averaging the utility functions of investors and identifying the most probable outcome.
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Open AccessFeature PaperReview
Economic Fraud and Associated Risks: An Integrated Bibliometric Analysis Approach
by
Kamer-Ainur Aivaz, Iulia Oana Florea and Ionela Munteanu
Risks 2024, 12(5), 74; https://doi.org/10.3390/risks12050074 - 30 Apr 2024
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This study offers a comprehensive insight into the realms of economic fraud and risk management, underscoring the necessity of adaptability to evolving technologies and shifts in financial market dynamics. Through the application of bibliometric methodologies, this study meticulously maps the relevant literature, delineating
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This study offers a comprehensive insight into the realms of economic fraud and risk management, underscoring the necessity of adaptability to evolving technologies and shifts in financial market dynamics. Through the application of bibliometric methodologies, this study meticulously maps the relevant literature, delineating influential works, notable authors, collaborative networks, and emerging trends. It reviews key research contributions within the field, alongside reputable journals and institutions engaged in academic research. The examination highlights the logical, conceptual, and social interconnections that define the landscape of economic fraud and associated risks, elucidating how these findings inform the understanding, mitigating, and combating of the risk of fraud. Our bibliometric analysis methodology is grounded in the utilization of the Scopus database, employing rigorous filtering and extraction processes to obtain a substantial corpus of pertinent articles. Through a fusion of performance analysis and science mapping, our investigation elucidates central themes and visually represents the interrelationships between studies. Our research outcomes underscore the frequency of paper publications across diverse regions, with particular emphasis on the predominant scientific output from the US and China. Additionally, trends in academic citations are identified, indicative of the significant impact of papers on academic research and the formulation of public policies. By means of bibliometric analysis, this study not only consolidates existing knowledge but also catalyzes the exploration of future research trajectories, emphasizing the imperative of addressing these issues with heightened scientific rigor.
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(This article belongs to the Special Issue Risk Analysis in Financial Crisis and Stock Market)
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Open AccessArticle
Estimation and Prediction of Commodity Returns Using Long Memory Volatility Models
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Kisswell Basira, Lawrence Dhliwayo, Knowledge Chinhamu, Retius Chifurira and Florence Matarise
Risks 2024, 12(5), 73; https://doi.org/10.3390/risks12050073 - 23 Apr 2024
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Modelling the volatility of commodity prices and creating more reliable models for estimating and forecasting commodity price returns are crucial. The body of research on statistical models that can fully reflect the empirical characteristics of commodity price returns is lacking. The main aim
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Modelling the volatility of commodity prices and creating more reliable models for estimating and forecasting commodity price returns are crucial. The body of research on statistical models that can fully reflect the empirical characteristics of commodity price returns is lacking. The main aim of this research was to develop a modelling framework that could be used to accurately estimate and forecast commodity price returns by combining long memory models with heavy-tailed distributions. This study employed dual hybrid long-memory generalised autoregressive conditionally heteroscedasticity (GARCH) models with heavy-tailed innovations, namely, the Student-t distribution (StD), skewed-Student-t distribution (SStD), and the generalised error distribution (GED). Based on the smallest forecasting metrics values for mean absolute error (MAE) and mean squared error (MSE) values, the best performing LM-GARCH-type model for lithium is the ARFIMA (1, , 1)-FIAPARCH (1, , 1) with normal innovations. For tobacco, the best model is ARFIMA (1, , 1)-FIGARCH (1, , 1) with SStD innovations. The robust performing model for gold is the ARFIMA (1, , 1)-FIGARCH (1, , 1)-GED model. The best performing forecasting model for crude oil and cotton returns are the model and model, respectively. The results obtained from this study would be beneficial to those concerned with financial market modelling techniques, such as derivative pricing, risk management, asset allocation, and valuation.
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Open AccessArticle
The Impact of Firm Risk and the COVID-19 Crisis on Working Capital Management Strategies: Evidence from a Market Affected by Economic Uncertainty
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Hossein Tarighi, Grzegorz Zimon, Mohammad Javad Sheikh and Mohammad Sayrani
Risks 2024, 12(4), 72; https://doi.org/10.3390/risks12040072 - 22 Apr 2024
Abstract
The present study aims to investigate the impact of the COVID-19 crisis and firm risk on working capital management policies among manufacturing firms listed on the Tehran Stock Exchange (TSE). The study sample consists of 1200 observations and 200 companies listed on the
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The present study aims to investigate the impact of the COVID-19 crisis and firm risk on working capital management policies among manufacturing firms listed on the Tehran Stock Exchange (TSE). The study sample consists of 1200 observations and 200 companies listed on the TSE over a six-year period from 2016 to 2021; furthermore, the statistical method used to test the hypotheses is ordinary least squares (OLS). The results show that the COVID-19 pandemic has led managers to increase current assets to total assets ratio (CATAR), current ratio (CR), quick ratio (QR), net working capital (NWC), cash to current assets (CTCA) ratio, while it has caused a decrease in operational cycle (OC), days account receivables (DAR), and current liabilities to total assets ratio (CLTAR). Furthermore, we find that the higher the company’s risk, the more managers are motivated to embrace the working capital investment policy, net working capital, cash to current assets ratio, and cash conversion efficiency (CCE). In general, our findings indicate that during times of crisis, Iranian companies tend to adopt conservative working capital policies to ensure sufficient liquidity to respond appropriately to unforeseen events. In this study, the theory of liquidity preference aligns with the observed behavior of firms in response to the COVID-19 crisis and firm risk, where the emphasis on liquidity and short-term financial stability becomes paramount.
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Open AccessArticle
Volatility Spillovers among Sovereign Credit Default Swaps of Emerging Economies and Their Determinants
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Shumok Aljarba, Nader Naifar and Khalid Almeshal
Risks 2024, 12(4), 71; https://doi.org/10.3390/risks12040071 - 22 Apr 2024
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This paper aims to investigate the volatility spillovers among selected emerging economies’ sovereign credit default swaps (SCDSs), including those of Saudi Arabia, Russia, China, Indonesia, South Africa, Brazil, Mexico, and Turkey. Using data from January 2010 to July 2023, we apply the time-domain
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This paper aims to investigate the volatility spillovers among selected emerging economies’ sovereign credit default swaps (SCDSs), including those of Saudi Arabia, Russia, China, Indonesia, South Africa, Brazil, Mexico, and Turkey. Using data from January 2010 to July 2023, we apply the time-domain and the frequency-domain connectedness approaches.Empirical results show that (i) Indonesia, followed by China and Mexico, are the main transmitters of sovereign credit risk volatility. (ii) Among global factors, the volatility index (VIX), economic policy uncertainty (EPU), and global political risk (GPR) positively impacted spillover on lower and higher quantiles. The results offer critical insights for international investors, policymakers, and researchers, emphasizing the importance of risk-aware investment strategies and cautious policy formulation in the context of financial crises and political events.
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