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
Cyber Risk in Insurance: A Quantum Modeling
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.
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(This article belongs to the Special Issue Advancements in Actuarial Mathematics and Risk Theory)
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Bitcoin Volatility and Intrinsic Time Using Double-Subordinated Lévy Processes
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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
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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?
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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
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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.
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(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
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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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Uncertainty Reduction in Operational Risk Management Process
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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
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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
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Kamer-Ainur Aivaz, Iulia Oana Florea and Ionela Munteanu
Risks 2024, 12(5), 74; https://doi.org/10.3390/risks12050074 - 30 Apr 2024
Abstract
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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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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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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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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Determining Safe Withdrawal Rates for Post-Retirement via a Ruin-Theory Approach
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Diba Daraei and Kristina Sendova
Risks 2024, 12(4), 70; https://doi.org/10.3390/risks12040070 - 19 Apr 2024
Abstract
To ensure a comfortable post-retirement life and the ability to cover living expenses, it is of utmost importance for individuals to have a clear understanding of how long their pre-retirement savings will last. In this research, we employ a ruin-theory approach to model
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To ensure a comfortable post-retirement life and the ability to cover living expenses, it is of utmost importance for individuals to have a clear understanding of how long their pre-retirement savings will last. In this research, we employ a ruin-theory approach to model the inflows and the outflows of retirees’ portfolios. We track all transactions within the portfolios of retired clients sourced by a registered investment provider to Canada’s Financial Wellness Lab at Western University. By utilizing an advanced ruin model, we calculate the mean and the median time it takes for savings to be exhausted, the probabilities of exhaustion of funds within the retirees’ expected remaining lifetime while accounting for the observed withdrawal rates, and the deficit at ruin if a retiree has used up all of their savings. We also account for gender as well as for the risk tolerance of retired clients using a K-Means clustering algorithm. This allows us to compare the financial outcomes for female and male retirees and to enhance some findings in the literature. In the final phase of our study, we compare the results obtained by our methodology to the 4% rule which is a widely used approach for post-retirement spending. Our results show that most retirees can withdraw safely more than they currently do (around 2.5%). A withdrawal rate of about 4.5% is proved to be safe, but it might not provide sufficient income for most retirees since it yields approximately CAD 20,000 per year for male retirees in the highest risk tolerance group who withdraw about 4.5% annually.
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(This article belongs to the Special Issue Optimal Investment and Risk Management)
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Effect of Capital Structure on the Financial Performance of Ethiopian Commercial Banks
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Seid Muhammed, Goshu Desalegn and Prihoda Emese
Risks 2024, 12(4), 69; https://doi.org/10.3390/risks12040069 - 18 Apr 2024
Cited by 2
Abstract
This study aimed to examine the effects of capital structure on the financial performance of Ethiopian commercial banks. The dependent variable, financial performance, is measured by Return on Assets (ROA), while factors such as loan-to-deposit ratio (LDR), asset-to-total equity ratio (ATER), total deposit-to-total
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This study aimed to examine the effects of capital structure on the financial performance of Ethiopian commercial banks. The dependent variable, financial performance, is measured by Return on Assets (ROA), while factors such as loan-to-deposit ratio (LDR), asset-to-total equity ratio (ATER), total deposit-to-total asset ratio (TDTAR), capital adequacy ratio (CAD), and asset growth ratio (GA) were used as proxy independent variables to gauge capital structure. Using a quantitative approach and an explanatory research design, this study analyzes 6 years of audited financial reports from 14 commercial banks in Ethiopia. This investigation employs a random effect regression model and Stata 14 software package to explore the relationships among these variables. The result revealed that both the loan-to-deposit ratio and the total deposit-to-total asset ratio have a positive and significant impact on financial performance, while the asset growth ratio showed a negative effect. Based on these findings, this study recommends that bank authorities concentrate on bolstering their deposit base, managing asset growth efficiently, maintaining adequate capital levels, and optimizing leverage levels to improve financial performance and ensure long-term sustainability in the banking sector. Additionally, this research is anticipated to inform policymakers about regulatory frameworks for banks and assist banking managers in formulating effective capital financing strategies within the Ethiopian commercial banking sector, thus enriching the existing literature on the relationship between capital structure and financial performance.
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(This article belongs to the Special Issue Corporate Finance and Intellectual Capital Management)
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Optimising Portfolio Risk by Involving Crypto Assets in a Volatile Macroeconomic Environment
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Attila Bányai, Tibor Tatay, Gergő Thalmeiner and László Pataki
Risks 2024, 12(4), 68; https://doi.org/10.3390/risks12040068 - 17 Apr 2024
Abstract
Portfolio diversification is an accepted principle of risk management. When constructing an efficient portfolio, there are a number of asset classes to choose from. Financial innovation is expanding the range of instruments. In addition to traditional commodities and securities, other instruments have been
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Portfolio diversification is an accepted principle of risk management. When constructing an efficient portfolio, there are a number of asset classes to choose from. Financial innovation is expanding the range of instruments. In addition to traditional commodities and securities, other instruments have been added. These include cryptocurrencies. In our study, we seek to answer the question of what proportion of cryptocurrencies should be included alongside traditional instruments to optimise portfolio risk. We use VaR risk measures to optimise the process. Diversification opportunities are evaluated under normal return distributions, thick-tailed distributions, and asymmetric distributions. To answer our research questions, we have created a quantitative model in which we analysed the VaR of different portfolios, including crypto-diversified assets, using Monte Carlo simulations. The study database includes exchange rate data for two consecutive years. When selecting the periods under examination, it was important to compare favourable and less favourable periods from a macroeconomic point of view so that the study results can be interpreted as a stress test in addition to observing the diversification effect. The first period under examination is from 1 September 2020 to 31 August 2021, and the second from 1 September 2021 to 31 August 2022. Our research results ultimately confirm that including cryptoassets can reduce the risk of an investment portfolio. The two time periods examined in the simulation produced very different results. An analysis of the second period suggests that Bitcoin’s diversification ability has become significant in the unfolding market situation due to the Russian-Ukrainian war.
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(This article belongs to the Special Issue Risk Analysis in Financial Crisis and Stock Market)
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Risk Management in the Area of Bitcoin Market Development: Example from the USA
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Laeeq Razzak Janjua, Iza Gigauri, Agnieszka Wójcik-Czerniawska and Elżbieta Pohulak-Żołędowska
Risks 2024, 12(4), 67; https://doi.org/10.3390/risks12040067 - 15 Apr 2024
Abstract
This paper explores the relationship between Bitcoin returns, the consumer price index, and economic policy uncertainty. Employing the QARDL method, this study examines both short- and long-term dynamics between macroeconomic factors and Bitcoin returns. Our analysis of monthly time series data from January
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This paper explores the relationship between Bitcoin returns, the consumer price index, and economic policy uncertainty. Employing the QARDL method, this study examines both short- and long-term dynamics between macroeconomic factors and Bitcoin returns. Our analysis of monthly time series data from January 2011 to November 2023 reveals that volatile US economic policy indicators, such as high economic policy uncertainty, volatile inflation, and rising interest rates, have recently exerted a negative impact on Bitcoin returns. This study shows that these results are true not only for traditional money but also for cryptocurrencies such as Bitcoin, despite their cardinal features. Its decentralized nature, indicating that it has no physical representation, is not tied to any authority or national economy and relies on a complex algorithm to track transactions. Further, it yields volatile returns that depend on macroeconomic indicators.
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(This article belongs to the Special Issue Risk Management in Economics and Finance for Sustainable Development in the Digital Ecosystem)
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Open AccessFeature PaperArticle
Quantum Computing Approach to Realistic ESG-Friendly Stock Portfolios
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Francesco Catalano, Laura Nasello and Daniel Guterding
Risks 2024, 12(4), 66; https://doi.org/10.3390/risks12040066 - 12 Apr 2024
Abstract
Finding an optimal balance between risk and returns in investment portfolios is a central challenge in quantitative finance, often addressed through Markowitz portfolio theory (MPT). While traditional portfolio optimization is carried out in a continuous fashion, as if stocks could be bought in
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Finding an optimal balance between risk and returns in investment portfolios is a central challenge in quantitative finance, often addressed through Markowitz portfolio theory (MPT). While traditional portfolio optimization is carried out in a continuous fashion, as if stocks could be bought in fractional increments, practical implementations often resort to approximations, as fractional stocks are typically not tradeable. While these approximations are effective for large investment budgets, they deteriorate as budgets decrease. To alleviate this issue, a discrete Markowitz portfolio theory (DMPT) with finite budgets and integer stock weights can be formulated, but results in a non-polynomial (NP)-hard problem. Recent progress in quantum processing units (QPUs), including quantum annealers, makes solving DMPT problems feasible. Our study explores portfolio optimization on quantum annealers, establishing a mapping between continuous and discrete Markowitz portfolio theories. We find that correctly normalized discrete portfolios converge to continuous solutions as budgets increase. Our DMPT implementation provides efficient frontier solutions, outperforming traditional rounding methods, even for moderate budgets. Responding to the demand for environmentally and socially responsible investments, we enhance our discrete portfolio optimization with ESG (environmental, social, governance) ratings for EURO STOXX 50 index stocks. We introduce a utility function incorporating ESG ratings to balance risk, return and ESG friendliness, and discuss implications for ESG-aware investors.
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(This article belongs to the Special Issue Portfolio Theory, Financial Risk Analysis and Applications)
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Relationship between Occupational Pension, Corporate Social Responsibility (CSR), and Organizational Resilience: A Study on Listed Chinese Companies
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Hao Wang, Tao Zhang, Xi Wang and Jiansong Zheng
Risks 2024, 12(4), 65; https://doi.org/10.3390/risks12040065 - 9 Apr 2024
Abstract
Numerous researchers acknowledge that the occupational pension protects employees. However, in China, the total cost of occupational pensions is shared between employees and employers, representing a significant financial commitment. This study aimed to explore the effect of the occupational pension on corporate social
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Numerous researchers acknowledge that the occupational pension protects employees. However, in China, the total cost of occupational pensions is shared between employees and employers, representing a significant financial commitment. This study aimed to explore the effect of the occupational pension on corporate social responsibility (CSR) and organizational resilience. Drawing on insights from cost-stickiness and resource-based theories, we developed a model that elucidated the influence of occupational pensions on firms’ approaches to CSR within the context of COVID-19 and how this, in turn, impacted organizational resilience. This study categorized CSR into strategic and responsive activities, employing the concept of cost stickiness as a framework. We analyzed a sample of 34,145 observations from Chinese A-share listed companies spanning the period 2010–2023 to examine the influence of occupational pension adjustments on CSR strategies. The findings of this study revealed that the cost pressure associated with contributions to occupational pensions prompted firms to decrease their engagement in responsive CSR activities while enhancing their strategic CSR initiatives. Furthermore, it was observed that strategic CSR contributed to improved organizational resilience, whereas responsive CSR did not exhibit the same effect. The relationship between occupational pension contributions and CSR was found to be significantly and negatively moderated by factors such as the minimum wage and population aging. Conversely, the relationship between CSR and organizational resilience was significantly and positively moderated by digital transformation and marketing capabilities.
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(This article belongs to the Special Issue Life Insurance and Pensions: Latest Advances and Prospects)
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Open AccessArticle
Asymptotic Methods for Transaction Costs
by
Eberhard Mayerhofer
Risks 2024, 12(4), 64; https://doi.org/10.3390/risks12040064 - 4 Apr 2024
Abstract
We propose a general approximation method for the determination of optimal trading strategies in markets with proportional transaction costs, with a polynomial approximation of the residual value function. The method is exemplified by several problems, from optimally tracking benchmarks and hedging the log
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We propose a general approximation method for the determination of optimal trading strategies in markets with proportional transaction costs, with a polynomial approximation of the residual value function. The method is exemplified by several problems, from optimally tracking benchmarks and hedging the log contract to maximizing utility from terminal wealth. Strategies are also approximated by practically executable, discrete trades. We identify the necessary trade-off between the trading frequency and trade size to ensure satisfactory agreement with the theoretically optimal, continuous strategies of infinite activity.
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(This article belongs to the Special Issue Optimal Investment and Risk Management)
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