Risks

30 pages, 3674 KiB

Open AccessArticle

Estimating the Acceptance Probabilities of Consumer Loan Offers in an Online Loan Comparison and Brokerage Platform

by Renatas Špicas, Airidas Neifaltas, Rasa Kanapickienė, Greta Keliuotytė-Staniulėnienė and Deimantė Vasiliauskaitė

Risks 2023, 11(7), 138; https://doi.org/10.3390/risks11070138 - 24 Jul 2023

Viewed by 1326

Abstract

It is widely recognised that the ability of e-commerce businesses to predict conversion probability, i.e., acceptance probability, is critically important in today’s business environment. While the issue of conversion prediction based on browsing data in various e-commerce websites is broadly analysed in scientific [...] Read more.

It is widely recognised that the ability of e-commerce businesses to predict conversion probability, i.e., acceptance probability, is critically important in today’s business environment. While the issue of conversion prediction based on browsing data in various e-commerce websites is broadly analysed in scientific literature, there is a lack of studies covering this topic in the context of online loan comparison and brokerage (OLCB) platforms. It can be argued that due to the inseparable relationship between the operation of these platforms and credit risk, the behaviour of consumers in making loan decisions differs from typical consumer behaviour in choosing non-risk-related products. In this paper, we aim to develop and propose statistical acceptance prediction models of loan offers in OLCB platforms. For modelling, we use diverse data obtained from an operating OLCB platform, including on customer (i.e., borrower) behaviour and demographics, financial variables, and characteristics of the loan offers presented to the borrowers/customers. To build the models, we experiment with various classifiers including logistic regression, random forest, XGboost, artificial neural networks, and support vector machines. Computational experiments show that our models can predict conversion with good performance in terms of area under the curve (AUC) score. The models presented are suitable for use in a loan comparison and brokerage platform for real-time process optimisation purposes. Full article

(This article belongs to the Special Issue Credit Risk Management: Volume II)

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19 pages, 467 KiB

Open AccessArticle

Earnings Management and Sustainability Reporting Disclosure: Some Insights from Indonesia

by Sri Ningsih, Khusnul Prasetyo, Novi Puspitasari, Suham Cahyono and Khairul Anuar Kamarudin

Risks 2023, 11(7), 137; https://doi.org/10.3390/risks11070137 - 24 Jul 2023

Cited by 3 | Viewed by 1786

Abstract

Earnings manipulation is often associated with deceiving public information that is displayed in sustainability reports. Therefore, the current study aims to explore the nexus between earnings management and sustainability reporting practices in the context of Indonesia. This study employs 408 firm-year observations from [...] Read more.

Earnings manipulation is often associated with deceiving public information that is displayed in sustainability reports. Therefore, the current study aims to explore the nexus between earnings management and sustainability reporting practices in the context of Indonesia. This study employs 408 firm-year observations from listed companies in Indonesia during the 2010–2021 period to test the hypothesis using fixed effect regression analyses with standard error estimates. By examining their sustainability reports and financial statements over a specific period, the authors assess the extent to which earnings management influences sustainability reporting practices. This implies that companies engaging in earnings management practices are more likely to exhibit higher-quality sustainability reporting practices. The results contribute valuable and significant empirical insights into the interplay between earnings management and sustainability reporting specifically within the Indonesian context. Furthermore, this study goes beyond examining the relationship itself and delves into potential factors that may influence this relationship. Full article

(This article belongs to the Special Issue Quantitative Methods in Economics and Finance II)

15 pages, 1534 KiB

Open AccessArticle

Power Laws and Inequalities: The Case of British District House Price Dispersion

by David Paul Gray

Risks 2023, 11(7), 136; https://doi.org/10.3390/risks11070136 - 21 Jul 2023

Cited by 1 | Viewed by 986

Abstract

Descriptive statistics that are easy to generate and interpret are central to policy decision making. The GINI coefficient and the coefficient of variation are used widely when assessing inequality. In many areas of inequality, such as wealth and income holdings, the distribution is [...] Read more.

Descriptive statistics that are easy to generate and interpret are central to policy decision making. The GINI coefficient and the coefficient of variation are used widely when assessing inequality. In many areas of inequality, such as wealth and income holdings, the distribution is skewed. Here, simple power laws could provide useful ‘descriptive’ exponents. The Zipf-Pareto power law and Lavalette’s law are used to reveal a steepening in the distribution of district house prices in Britain that began before the financial crash of 2008. The time profiles indicate the exponents closely mirror those of the GINI coefficient and the coefficient of variation. As such, they are useful tools in the quantification of inequalities. Full article

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21 pages, 805 KiB

Open AccessArticle

Optimal Choice between Defined Contribution and Cash Balance Pension Schemes: Balancing Interests of Employers and Workers

by Vanessa Hanna and Pierre Devolder

Risks 2023, 11(7), 135; https://doi.org/10.3390/risks11070135 - 21 Jul 2023

Viewed by 790

Abstract

In the context of pension plans, the employer and the worker have distinct interests and face different risks. The worker seeks higher retirement benefits, while the employer aims to minimize the cost of fulfilling his obligations. To address these diverse needs, the defined [...] Read more.

In the context of pension plans, the employer and the worker have distinct interests and face different risks. The worker seeks higher retirement benefits, while the employer aims to minimize the cost of fulfilling his obligations. To address these diverse needs, the defined contribution plan managed with participating life insurance (DC-PL) and the cash balance plan managed with unit-linked insurance (CB-UL) serve as suitable choices. The multi-criteria analysis is conducted using the cumulative prospect theory model to measure the utility of the parties involved toward a mixed product combining these two pension plans. By assigning weights to risk measures and maximizing utilities, the paper employs both additive utility and Nash equilibrium approaches. The results reveal that the CB-UL plan aligns with employers’ interests, offering potential financial gains, while the DC-PL plan attracts workers due to its profit-sharing aspect. Significantly, when equal importance is given to both parties, the CB-UL plan emerges as the prevailing choice. This study contributes to the understanding of pension plan design and decision-making dynamics between employers and workers, providing valuable insights for achieving a balance between retirement benefits and cost management. Full article

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3 pages, 267 KiB

Open AccessEditorial

Special Issue “Actuarial Mathematics and Risk Management”

by Annamaria Olivieri

Risks 2023, 11(7), 134; https://doi.org/10.3390/risks11070134 - 20 Jul 2023

Viewed by 697

Abstract

Among the most important implementations of the principles of enterprise risk management (ERM), the risk management process (RMP) involves various quantitative phases, usually encompassed under the label of quantitative risk management (QRM) [...] Full article

(This article belongs to the Special Issue Actuarial Mathematics and Risk Management)

15 pages, 901 KiB

Open AccessArticle

Is Additional CEO Remuneration a Performance Driver? DAX CEOs Evidence

by Magali Costa, Inês Lisboa and René Marzinzik

Risks 2023, 11(7), 133; https://doi.org/10.3390/risks11070133 - 17 Jul 2023

Viewed by 1095

Abstract

This study aims to understand the impact of the additional remuneration of the Chief Executive Officer (CEO) over the mean remuneration of the board of directors on firms’ financial performance. The objective is to understand if the highest compensation of the CEO is [...] Read more.

This study aims to understand the impact of the additional remuneration of the Chief Executive Officer (CEO) over the mean remuneration of the board of directors on firms’ financial performance. The objective is to understand if the highest compensation of the CEO is a firm performance driver. In addition to the impact of total remuneration, the different remuneration components were split and analyzed. An unbalanced panel data of listed companies in DAX–Germany over the period from 2006 until 2019 is analyzed. Using dynamic methodology to estimate the models, the results show that higher additional remuneration positively explains higher firm performance measured using both accounting and market measures. The impact is also evident when additional remuneration components are analyzed. These results support the tournament theory, since when CEOs feel rewarded, they are more efficient in increasing the firm’s performance. Moreover, the firms’ financial characteristics, as well as macroeconomic factors, are also relevant to explaining its performance. Full article

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19 pages, 724 KiB

Open AccessArticle

Financial Inclusion and Sustainable Growth in North African Firms: A Dynamic-Panel-Threshold Approach

by Wafa Khémiri, Ahmed Chafai and Faizah Alsulami

Risks 2023, 11(7), 132; https://doi.org/10.3390/risks11070132 - 17 Jul 2023

Cited by 8 | Viewed by 1692

Abstract

This paper investigates the impact of financial inclusion on sustainable firm growth in Northern African countries (Egypt, Morocco, and Tunisia) during the period of 2007–2020. To this end, this study employs a dynamic panel threshold regression (DPTR) model. This model is a panel-data [...] Read more.

This paper investigates the impact of financial inclusion on sustainable firm growth in Northern African countries (Egypt, Morocco, and Tunisia) during the period of 2007–2020. To this end, this study employs a dynamic panel threshold regression (DPTR) model. This model is a panel-data model that can capture different behaviors of data, depending on a threshold variable. The main results showed the existence of a threshold effect. This means that when financial inclusion is low (high), sustainable firm growth is limited (significant) due to the absence (presence) of appropriate financing, information, and financial tools. However, the levels of financial inclusion in North African countries are insufficient and require improvement. Therefore, it is essential for policymakers and managers to continue to promote the quality of financial inclusion by improving access to financial services and the regulatory environment to facilitate firms’ access to financing and support their sustainability. Full article

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17 pages, 1291 KiB

Open AccessArticle

AutoReserve: A Web-Based Tool for Personal Auto Insurance Loss Reserving with Classical and Machine Learning Methods

by Lu Xiong, Vajira Manathunga, Jiyao Luo, Nicholas Dennison, Ruicheng Zhang and Zhenhai Xiang

Risks 2023, 11(7), 131; https://doi.org/10.3390/risks11070131 - 14 Jul 2023

Viewed by 1750

Abstract

In this paper, we developed a Shiny-based application called AutoReserve. This application serves as a tool used for a variety of types of loss reserving. The primary target audience of the app is personal auto actuaries, who are professionals in the insurance industry [...] Read more.

In this paper, we developed a Shiny-based application called AutoReserve. This application serves as a tool used for a variety of types of loss reserving. The primary target audience of the app is personal auto actuaries, who are professionals in the insurance industry specializing in assessing risks and determining insurance premiums for personal vehicles. However, the app is not limited exclusively to actuaries. Other individuals or entities, such as insurance companies, researchers, or analysts, who have access to the necessary data and require insights or analysis related to personal auto insurance, can also benefit from using the app. It is the first web-based application of its kind that is free to use and deployable from the personal computer or mobile device. AutoReserve is a software solution that caters to the needs of insurance professionals where only a few existing web-based applications are available. The application is divided into three parts: a summary of the loss data, a classical loss reserving tool, and a machine learning loss reserving tool. Each component of the application functions differently and allows for inputs from the user to analyze the provided loss data. The user, in other words, individuals or entities who utilize the Auto Reserve application, can then use the outputs for these three sections to improve his or her risk management or loss reserving process. AutoReserve is unique compared to other loss reserving tools because of its ability to employ both traditional, spreadsheet-based and modern, machine-learning-based loss reserving tools. AutoReserve is accessible on the web. The app is currently usable and is still undergoing frequent updates with new features and bug fixes. Full article

(This article belongs to the Special Issue Computational Technologies for Financial Security and Risk Management)

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18 pages, 1290 KiB

Open AccessArticle

On the Identification of the Riskiest Directional Components from Multivariate Heavy-Tailed Data

by Miriam Hägele and Jaakko Lehtomaa

Risks 2023, 11(7), 130; https://doi.org/10.3390/risks11070130 - 13 Jul 2023

Viewed by 667

Abstract

In univariate data, there exist standard procedures for identifying dominating features that produce the largest number of observations. However, in the multivariate setting, the situation is quite different. This paper aims to provide tools and methods for detecting dominating directional components in multivariate [...] Read more.

In univariate data, there exist standard procedures for identifying dominating features that produce the largest number of observations. However, in the multivariate setting, the situation is quite different. This paper aims to provide tools and methods for detecting dominating directional components in multivariate data. We study general heavy-tailed multivariate random vectors in dimension d ≥ 2 and present procedures that can be used to explain why the data are heavy-tailed. This is achieved by identifying the set of the riskiest directional components. The results are of particular interest in insurance when setting reinsurance policies, and in finance when hedging a portfolio of multiple assets. Full article

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2 pages, 260 KiB

Open AccessEditorial

The Risk Landscape in the Digital Transformation of Finance and Insurance

by Ramona Rupeika-Apoga and Pierpaolo Marano

Risks 2023, 11(7), 129; https://doi.org/10.3390/risks11070129 - 12 Jul 2023

Viewed by 1012

Abstract

“The Risk Landscape in the Digital Transformation of Finance and Insurance” is a Special Issue that explores the opportunities and challenges arising from the integration of emerging technologies in the finance and insurance sectors [...] Full article

(This article belongs to the Special Issue The Risk Landscape in the Digital Transformation of Finance and Insurance)

31 pages, 938 KiB

Open AccessArticle

Building a Macroeconomic Simulator with Multi-Layered Supplier–Customer Relationships

by Takahiro Obata, Jun Sakazaki and Setsuya Kurahashi

Risks 2023, 11(7), 128; https://doi.org/10.3390/risks11070128 - 12 Jul 2023

Viewed by 906

Abstract

This study constructs an agent-based model suitable for analyzing the propagation of economic shocks based on a macroeconomic agent-based model structure that covers major economic entities. Instead of setting an upstream and downstream structure of firms in the inter-firm networks, our model includes [...] Read more.

This study constructs an agent-based model suitable for analyzing the propagation of economic shocks based on a macroeconomic agent-based model structure that covers major economic entities. Instead of setting an upstream and downstream structure of firms in the inter-firm networks, our model includes a mechanism that connects each firm through supplier–customer relationships and incorporates interactions between firms mutually buying and selling intermediate input materials. It is confirmed through the proposed model’s simulation analysis that, although a firm’s sales volume temporarily falls due to an economic shock of the type that causes a sharp decline in households’ final demand, the increase in assets held by households as they refrain from spending rather expands their capacity for consumption. As a result, after the economic shock ceases to exist, the firm’s sales volume tends to be even greater than that of the preceding periods of the shock. Furthermore, we found that when the sales volume of products in a final consumer goods sector falls during the shock, the falls in sales in the non-final consumer goods sectors are suppressed due to replacement demand, and the increase in sales volume for the non-final consumer goods sectors is moderated after the shock ceases to exist. Full article

(This article belongs to the Special Issue Corporate Finance and Intellectual Capital Management)

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20 pages, 676 KiB

Open AccessArticle

Dataset Analysis of the Risks for Russian IT Companies Amid the COVID-19 Crisis

by Tatiana M. Vorozheykina, Aleksei Yu. Shchetinin, Galina N. Semenova and Maria A. Vakhrushina

Risks 2023, 11(7), 127; https://doi.org/10.3390/risks11070127 - 11 Jul 2023

Viewed by 1138

Abstract

The motivation for this research was to strive towards specifying the risks for businesses under the conditions of the COVID-19 pandemic and crisis in the IT sector in Russia. This paper is aimed at performing a dataset analysis of the risks for Russian [...] Read more.

The motivation for this research was to strive towards specifying the risks for businesses under the conditions of the COVID-19 pandemic and crisis in the IT sector in Russia. This paper is aimed at performing a dataset analysis of the risks for Russian IT companies amid the COVID-19 crisis. The sample contains the top 100 largest IT companies in Russia in 2020 and covers the data on these companies for 2019–2020. The influence of the COVID-19 crisis pandemic on the risks for IT companies in Russia is assessed with the help of the authors’ methodological approach to the dataset analytics of companies’ risks with the use of the method of trend analysis, analysis of variance and the hierarchical synthesis concept by T. Saaty. A specific feature of the authors’ methodological approach is its taking into account of the pre-crisis level of risks for companies. Due to this, the authors’ methodological approach allows for the most precise and correct determination of the scale and character of the influence of the COVID-19 pandemic and crisis on the risks for companies. The role of human resources in the management of risks for IT companies under the conditions of the COVID-19 pandemic and crisis in Russia is determined with the help of regression analysis; the regularity of the change in revenue, and the position of the company in the ranking (its competitiveness) in terms of the growth of the number of employees, are described mathematically. The key conclusions are that the influence of the COVID-19 pandemic and crisis on the risks for IT companies in Russia in 2020 was weak, and human resources played an important role in risk management. The theoretical significance of the paper lies in its rethinking of human resources management of Russian IT companies from the position of risk amid the COVID-19 crisis. The practical significance of the authors’ conclusions lies in the discovery of the high risk resilience of Russian IT companies to the pandemic and the formation of their risk profile amid the COVID-19 crisis, in which the main, though low, risk is the risk of reduction in competitiveness, whilst the risk of reduction in revenue is minimal. Full article

(This article belongs to the Special Issue The COVID-19 Crisis: Datasets and Data Analysis to Reduce Risks)

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15 pages, 717 KiB

Open AccessArticle

Cox-Based and Elliptical Telegraph Processes and Their Applications

by Anatoliy Pogorui, Anatoly Swishchuk, Ramón M. Rodríguez-Dagnino and Alexander Sarana

Risks 2023, 11(7), 126; https://doi.org/10.3390/risks11070126 - 10 Jul 2023

Viewed by 847

Abstract

This paper studies two new models for a telegraph process: Cox-based and elliptical telegraph processes. The paper deals with the stochastic motion of a particle on a straight line and on an ellipse with random positive velocity and two opposite directions of motion, [...] Read more.

This paper studies two new models for a telegraph process: Cox-based and elliptical telegraph processes. The paper deals with the stochastic motion of a particle on a straight line and on an ellipse with random positive velocity and two opposite directions of motion, which is governed by a telegraph–Cox switching process. A relevant result of our analysis on the straight line is obtaining a linear Volterra integral equation of the first kind for the characteristic function of the probability density function (PDF) of the particle position at a given time. We also generalize Kac’s condition for the telegraph process to the case of a telegraph–Cox switching process. We show some examples of random velocity where the distribution of the coordinate of a particle is expressed explicitly. In addition, we present some novel results related to the switched movement evolution of a particle according to a telegraph–Cox process on an ellipse. Numerical examples and applications are presented for a telegraph–Cox-based process (option pricing formulas) and elliptical telegraph process. Full article

(This article belongs to the Special Issue Stochastic Modelling in Financial Mathematics II)

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26 pages, 493 KiB

Open AccessArticle

Optimal Reinsurance under the Linear Combination of Risk Measures in the Presence of Reinsurance Loss Limit

by Qian Xiong, Zuoxiang Peng and Saralees Nadarajah

Risks 2023, 11(7), 125; https://doi.org/10.3390/risks11070125 - 10 Jul 2023

Cited by 1 | Viewed by 997

Abstract

Optimal reinsurance problems under the risk measures, such as Value-at-Risk (

VaR

) and Tail-Value-at-Risk (

TVaR

), have been studied in recent literature. However, losses based on

VaR

may be underestimated and

TVaR

allows us to account better for catastrophic losses. In [...] Read more.

Optimal reinsurance problems under the risk measures, such as Value-at-Risk (

VaR

) and Tail-Value-at-Risk (

TVaR

), have been studied in recent literature. However, losses based on

VaR

may be underestimated and

TVaR

allows us to account better for catastrophic losses. In this paper, we propose a new family of flexible risk measures denoted by

LVaR

, which is a weighted combination of

VaR

and

TVaR

. Based on the new risk measures, we deal with the optimal reinsurance problem by minimizing the

LVaR

of the total risks of an insurer when two types of constraints for reinsurer’s risk exposure are considered. The results indicate that the two-layer reinsurance is always an optimal reinsurance policy with both types of constraints. Also, we find that the optimal reinsurance policy depends on the confidence level, the weight coefficient, the safety loading, the tolerance level, as well as the relations between them. Finally, we illustrate the results by numerical examples and compare them with the results in Lu et al. Full article

14 pages, 447 KiB

Open AccessArticle

Asymmetric Wealth Effect between US Stock Markets and US Housing Market and European Stock Markets: Evidences from TAR and MTAR

by Pedro Coelho, Luís Gomes and Patrícia Ramos

Risks 2023, 11(7), 124; https://doi.org/10.3390/risks11070124 - 10 Jul 2023

Viewed by 992

Abstract

Evidence of the asymmetric wealth effect has important implications for investors and continues to merit research attention, not least because much of the evidence based on linear models has been refuted. Indeed, stock and house prices are influenced by economic activity and react [...] Read more.

Evidence of the asymmetric wealth effect has important implications for investors and continues to merit research attention, not least because much of the evidence based on linear models has been refuted. Indeed, stock and house prices are influenced by economic activity and react non-linearly to positive/negative shocks. This problem justifies our research. The objective of this study is to examine evidence of cointegrations between the US housing and stock markets and between the US and European stock markets, given the international relevance of these exchanges. Using data from 1989:Q1 to 2020:Q2, the Threshold Autoregression model as well as the Momentum Threshold Autoregression model were calculated by combining the US Freddie, DJIA, and SPX indices and the European STOXX and FTSE indices. The results suggest a long-term equilibrium relationship with asymmetric adjustments between the housing market and the US stock markets, as well as between the DJIA, SPX, and FTSE indices. Moreover, the wealth effect is stronger when stock prices outperform house prices above an estimated threshold. This empirical evidence is useful to portfolio managers in their search for non-perfectly related markets that allow investment diversification and control risk exposure across different assets. Full article

(This article belongs to the Special Issue Quantitative Methods in Economics and Finance II)

8 pages, 636 KiB

Open AccessCommunication

Credit Scoring for Peer-to-Peer Lending

by Daniel Felix Ahelegbey and Paolo Giudici

Risks 2023, 11(7), 123; https://doi.org/10.3390/risks11070123 - 07 Jul 2023

Viewed by 1769

Abstract

This paper shows how to improve the measurement of credit scoring by means of factor clustering. The improved measurement applies, in particular, to small and medium enterprises (SMEs) involved in P2P lending. The approach explores the concept of familiarity which relies on the [...] Read more.

This paper shows how to improve the measurement of credit scoring by means of factor clustering. The improved measurement applies, in particular, to small and medium enterprises (SMEs) involved in P2P lending. The approach explores the concept of familiarity which relies on the notion that the more familiar/similar things are, the closer they are in terms of functionality or hidden characteristics (latent factors that drive the observed data). The approach uses singular value decomposition to extract the factors underlying the observed financial performance ratios of SMEs. We then cluster the factors using the standard k-mean algorithm. This enables us to segment the heterogeneous population into clusters with more homogeneous characteristics. The result shows that clusters with relatively fewer number of SMEs produce a more parsimonious and interpretable credit scoring model with better default predictive performance. Full article

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18 pages, 997 KiB

Open AccessArticle

Cryptocurrency Trading and Downside Risk

by Farhat Iqbal, Mamoona Zahid and Dimitrios Koutmos

Risks 2023, 11(7), 122; https://doi.org/10.3390/risks11070122 - 06 Jul 2023

Cited by 1 | Viewed by 1905

Abstract

Since the debut of cryptocurrencies, particularly Bitcoin, in 2009, cryptocurrency trading has grown in popularity among investors. Relative to other conventional asset classes, cryptocurrencies exhibit high volatility and, consequently, downside risk. While the prospects of high returns are alluring for investors and speculators, [...] Read more.

Since the debut of cryptocurrencies, particularly Bitcoin, in 2009, cryptocurrency trading has grown in popularity among investors. Relative to other conventional asset classes, cryptocurrencies exhibit high volatility and, consequently, downside risk. While the prospects of high returns are alluring for investors and speculators, the downside risks are important to consider and model. As a result, the profitability of crypto market operations depends on the predictability of price volatility. Predictive models that can successfully explain volatility help to reduce downside risk. In this paper, we investigate the value-at-risk (VaR) forecasts using a variety of volatility models, including conditional autoregressive VaR (CAViaR) and dynamic quantile range (DQR) models, as well as GARCH-type and generalized autoregressive score (GAS) models. We apply these models to five of some of the largest market capitalization cryptocurrencies (Bitcoin, Ethereum, Ripple, Litecoin, and Steller, respectively). The forecasts are evaluated using various backtesting and model confidence set (MCS) techniques. To create the best VaR forecast model, a weighted aggregative technique is used. The findings demonstrate that the quantile-based models using a weighted average method have the best ability to anticipate the negative risks of cryptocurrencies. Full article

(This article belongs to the Special Issue Technology, Digital Transformation, and Financial Economics)

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16 pages, 976 KiB

Open AccessArticle

Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models

by Abdullah H. Alenezy, Mohd Tahir Ismail, Sadam Al Wadi and Jamil J. Jaber

Risks 2023, 11(7), 121; https://doi.org/10.3390/risks11070121 - 04 Jul 2023

Viewed by 1065

Abstract

We enhance the precision of predicting daily stock market price volatility using the maximum overlapping discrete wavelet transform (MODWT) spectral model and two learning approaches: the heuristic gradient descent (FS.HGD) and hybrid neural fuzzy inference system (HyFIS). The FS.HGD approach iteratively updates the [...] Read more.

We enhance the precision of predicting daily stock market price volatility using the maximum overlapping discrete wavelet transform (MODWT) spectral model and two learning approaches: the heuristic gradient descent (FS.HGD) and hybrid neural fuzzy inference system (HyFIS). The FS.HGD approach iteratively updates the model’s parameters based on the error function gradient, while the HyFIS approach combines the advantages of neural networks and fuzzy logic systems to create a more robust and accurate learning model. The MODWT uses five mathematical functions to form a discrete wavelet basis. The dataset used includes the daily closing prices of the Tadawul stock market from August 2011 to December 2019. Inputs were selected based on multiple regression, tolerance, and variance inflation factor tests, and the oil price (Loil) and repo rate (Repo) were identified as input variables. The output variable is represented by the logarithm of the Tadawul stock market price (LSCS). MODWT-LA8 (ARIMA(1,1,0) with drift) outperforms other WT functions on the 80% dataset, with an ME of (0.00000532), MAE of (0.003214182), and MAPE of (0.06449683). The addition of WT functions to the FS.HGD and HyFIS models increases their forecasting ability. Based on the reduced RMSE (0.048), MAE (0.038), and MAPE (0.538), the MODWT-LA8-FS.HGD outperforms traditional models in predicting the remaining 20% of datasets. Full article

(This article belongs to the Special Issue Time Series Modeling for Finance and Insurance)

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18 pages, 2418 KiB

Open AccessArticle

The Silicon Valley Bank Failure: Application of Benford’s Law to Spot Abnormalities and Risks

by Anurag Dutta, Liton Chandra Voumik, Lakshmanan Kumarasankaralingam, Abidur Rahaman and Grzegorz Zimon

Risks 2023, 11(7), 120; https://doi.org/10.3390/risks11070120 - 03 Jul 2023

Cited by 1 | Viewed by 2890

Abstract

Data are produced every single instant in the modern era of technological breakthroughs we live in today and is correctly termed as the lifeblood of today’s world; whether it is Google or Meta, everyone depends on data to survive. But, with the immense [...] Read more.

Data are produced every single instant in the modern era of technological breakthroughs we live in today and is correctly termed as the lifeblood of today’s world; whether it is Google or Meta, everyone depends on data to survive. But, with the immense surge in technological boom comes several backlashes that tend to pull it down; one similar instance is the data morphing or modification of the data unethically. In many jurisdictions, the phenomenon of data morphing is considered a severe offense, subject to lifelong imprisonment. There are several cases where data are altered to encrypt reliable details. Recently, in March 2023, Silicon Valley Bank collapsed following unrest prompted by increasing rates. Silicon Valley Bank ran out of money as entrepreneurial investors pulled investments to maintain their businesses afloat in a frigid backdrop for IPOs and individual financing. The bank’s collapse was the biggest since the financial meltdown of 2008 and the second-largest commercial catastrophe in American history. By confirming the “Silicon Valley Bank” stock price data, we will delve further into the actual condition of whether there has been any data morphing in the data put forward by the Silicon Valley Bank. To accomplish the very same, we applied a very well-known statistical paradigm, Benford’s Law and have cross-validated the results using comparable statistics, like Zipf’s Law, to corroborate the findings. Benford’s Law has several temporal proximities, known as conformal ranges, which provide a closer examination of the extent of data morphing that has occurred in the data presented by the various organizations. In this research for validating the stock price data, we have considered the opening, closing, and highest prices of stocks for a time frame of 36 years, between 1987 and 2023. Though it is worth mentioning that the data used for this research are coarse-grained, still since the validation is subjected to a larger time horizon of 36 years; Benford’s Law and the similar statistics used in this article can point out any irregularities, which can result in some insight into the situation and into whether there has been any data morphing in the Stock Price data presented by SVB or not. This research has clearly shown that the stock price variations of the SVB diverge much from the permissible ranges, which can give a conclusive direction on further investigations in this issue by the responsible authorities. In addition, readers of this article must note that the conclusion formed about the topic discussed in this article is objective and entirely based on statistical analysis and factual figures presented by the Silicon Valley Bank Group. Full article

(This article belongs to the Special Issue Financial Risk Management in Companies during the World Crisis)

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17 pages, 910 KiB

Open AccessArticle

Using US Stock Sectors to Diversify, Hedge, and Provide Safe Havens for NFT Coins

by Perry Sadorsky and Irene Henriques

Risks 2023, 11(7), 119; https://doi.org/10.3390/risks11070119 - 29 Jun 2023

Viewed by 999

Abstract

This paper explores risk management strategies for investments in Nonfungible Token (NFT) coins through their diversification within the S&P 500 industry sectors. Given the significant decline in NFT coin values in 2022, understanding these strategies is critical for investors. This study focused on [...] Read more.

This paper explores risk management strategies for investments in Nonfungible Token (NFT) coins through their diversification within the S&P 500 industry sectors. Given the significant decline in NFT coin values in 2022, understanding these strategies is critical for investors. This study focused on four major NFT coins (Enjin coin (ENJ), MANA, Theta coin (THETA), and the Tezos coin (XTZ)) and employed ETFs representing the major S&P 500 sectors for analysis. Dynamic conditional correlation GARCH models have been used, to estimate correlations between the NFT coins and US industry sector ETFs. Our findings showed that while most S&P 500 sectors offered diversification benefits in the pre-COVID period, all of them did during the COVID period. However, these sectors are generally weak safe havens and poor hedges. Portfolio analysis suggests an optimal NFT coin weighting of 10–30%, based on the Sharpe ratio. This study aims to pave the way for informed decision-making in the dynamic NFT market. Full article

(This article belongs to the Special Issue Technology, Digital Transformation, and Financial Economics)

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12 pages, 391 KiB

Open AccessArticle

Thermodynamic Approach to the Discount Rate and Discounted Cash Flow Method

by Mieczysław Dobija and Jurij Renkas

Risks 2023, 11(7), 118; https://doi.org/10.3390/risks11070118 - 29 Jun 2023

Viewed by 944

Abstract

Current theories of the discount rate have a theoretical basis focused on risk; risk-free rate and risk premium. The basic component of the discount rate, the risk-free rate as purely empirical has a natural infirmity which consequently weakens the final theory. Similarly, the [...] Read more.

Current theories of the discount rate have a theoretical basis focused on risk; risk-free rate and risk premium. The basic component of the discount rate, the risk-free rate as purely empirical has a natural infirmity which consequently weakens the final theory. Similarly, the risk premium category is not theoretically perfect. The fundamental shortcoming is that the theory of the discount rate does not relate to fundamental knowledge of capital and the natural rate of its potential growth. Therefore, the purpose of the discussion is to justify the discount rate structure with the constant of potential growth of capital; a = 0.08 [1/year] as the main component. It is proven that the theory of the discount rate is linked to the essence of time and the pace of its passage and is an essential component of the capital–labor–time triad. Full article

20 pages, 745 KiB

Open AccessArticle

Multiscale Volatility Analysis for Noisy High-Frequency Prices

by Tim Leung and Theodore Zhao

Risks 2023, 11(7), 117; https://doi.org/10.3390/risks11070117 - 26 Jun 2023

Cited by 1 | Viewed by 1303

Abstract

We present a multiscale analysis of the volatility of intraday prices from high-frequency data. Our multiscale framework includes a fractional Brownian motion and microstructure noise as the building blocks. The proposed noisy fractional Brownian motion model is shown to possess a variety of [...] Read more.

We present a multiscale analysis of the volatility of intraday prices from high-frequency data. Our multiscale framework includes a fractional Brownian motion and microstructure noise as the building blocks. The proposed noisy fractional Brownian motion model is shown to possess a variety of volatility behaviors suitable for intraday price processes. Algorithms for numerical estimation from time series observations are then introduced, with a new Hurst exponent estimator proposed for the noisy fractional Brownian motion model. Using real-world high-frequency price data for a collection of U.S. stocks and ETFs, we estimate the parameters in the noisy fractional Brownian motion and illustrate how the volatility varies over different timescales. The Hurst exponent and noise level also exhibit an intraday pattern whereby the the noise ratio tends to be higher near market close. Full article

(This article belongs to the Special Issue Risks: Feature Papers 2023)

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13 pages, 585 KiB

Open AccessArticle

Risk Management in Electricity Markets: Dominant Topics and Research Trends

by Adriana A. Londoño and Juan D. Velásquez

Risks 2023, 11(7), 116; https://doi.org/10.3390/risks11070116 - 21 Jun 2023

Cited by 3 | Viewed by 2216

Abstract

Risk management in electricity markets is essential for decision making that involve uncertainty. This article researches the dominant themes and research trends in risk management in electricity markets using descriptive analysis, literature mapping, and data mining techniques. The proposed methodology generates the clusters [...] Read more.

Risk management in electricity markets is essential for decision making that involve uncertainty. This article researches the dominant themes and research trends in risk management in electricity markets using descriptive analysis, literature mapping, and data mining techniques. The proposed methodology generates the clusters within the dominant themes and provides a comprehensive view of the main authors, journals, and publications, as well as the main lines of research. The results reveal that the academic production of the subject is increasing and the research trends focused on financial risk management, energy resource management, and that climate coverage mechanisms are of great interest to the scientific community. Full article

(This article belongs to the Special Issue Multidimensionality of Financial and Non-financial Market: Risk, Insurance, Economy, Management, and Interdependencies)

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17 pages, 404 KiB

Open AccessArticle

Correlation Pitfalls with ChatGPT: Would You Fall for Them?

by Marius Hofert

Risks 2023, 11(7), 115; https://doi.org/10.3390/risks11070115 - 21 Jun 2023

Cited by 1 | Viewed by 1798

Abstract

This paper presents an intellectual exchange with ChatGPT, an artificial intelligence chatbot, about correlation pitfalls in risk management. The exchange takes place in the form of a conversation that provides ChatGPT with context. The purpose of this conversation is to evaluate ChatGPT’s understanding [...] Read more.

This paper presents an intellectual exchange with ChatGPT, an artificial intelligence chatbot, about correlation pitfalls in risk management. The exchange takes place in the form of a conversation that provides ChatGPT with context. The purpose of this conversation is to evaluate ChatGPT’s understanding of correlation pitfalls, to offer readers an engaging alternative for learning about them, but also to identify related risks. Our findings indicate that ChatGPT possesses solid knowledge of basic and mostly non-technical aspects of the topic, but falls short in terms of the mathematical rigor needed to avoid certain pitfalls or completely comprehend the underlying concepts. Nonetheless, we suggest ways in which ChatGPT can be utilized to enhance one’s own learning process. Full article

18 pages, 653 KiB

Open AccessArticle

Assessing the Impact of Syrian Refugee Influx on the Jordanian Stock Exchange Market

by Nadia Al-Rousan, Dana Al-Najjar and Hazem Al-Najjar

Risks 2023, 11(7), 114; https://doi.org/10.3390/risks11070114 - 21 Jun 2023

Cited by 1 | Viewed by 1024

Abstract

The past decade has witnessed significant turmoil and political conflicts in several Middle Eastern countries, such as Egypt, Syria, and Libya, called the Arab Spring. These revolutions did not only affect the countries mentioned previously; their neighboring countries were also directly affected. This [...] Read more.

The past decade has witnessed significant turmoil and political conflicts in several Middle Eastern countries, such as Egypt, Syria, and Libya, called the Arab Spring. These revolutions did not only affect the countries mentioned previously; their neighboring countries were also directly affected. This study explores the impact of the Syrian refugee influx on the stock exchange market of one of its neighboring countries, namely Jordan. The Syrian civil war represents a recent catastrophic event that has resulted in over three million refugees migrating to various countries worldwide. The main objective of this paper is to examine the effect of the Syrian war on Jordan’s stock exchange market. The study utilizes the stock exchange indices as indicators of the performance of the exchange market, including Financials, Services, Industries, and General indices as dependent variables, and seven dummy variables are defined as representatives of the main events occurring in the Syrian civil war during the period 2011–2018 as independent variables. Multiple statistical analysis techniques, including correlation coefficients, error functions, and stepwise regression, are employed to analyze the selected variables. The findings reveal an inverse influence of the Syrian war on Jordan’s stock market. These findings can potentially enhance the development of prediction models for stock indices in Jordan and other countries by incorporating relevant variables. Full article

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Risks, Volume 11, Issue 7 (July 2023) – 25 articles

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