Journal of Risk and Financial Management

The COVID-19 crisis has revealed the economic vulnerability of various countries and, thus, has instigated the systematic exploration and forecasting of sovereign default risks. Multivariate statistical and stochastic process-based sovereign default risk forecasting has a 50-year developmental history. This article describes a continuous, non-homogeneous Markov chain method as the basis for a COVID-19-related sovereign default risk forecast model. It demonstrates the estimation of sovereign probabilities of default (PDs) over a five-year horizon period with the developed model reflecting the impact of the COVID-19 crisis. The COVID-19-adopted Markov model estimates PDs for most countries, including those that are advanced with AAA and AA ratings, to suggest that no sovereign nation’s economy is secure from the financial impact of the COVID-19 pandemic. The dynamics of the estimated PDs are indicative of contemporary evidence as experienced in the recent financial crisis. The empirical results of this article have policy implications for foreign investors, sovereign lenders, export finance institutions, foreign trade experts, risk management professionals, and policymakers in the field of finance. The developed model can be used to timely recognize potential problems with sovereign entities in the current COVID-19 crisis and to take appropriate mitigating actions. Full article

The cryptocurrency market has experienced stunning growth, with market value exceeding USD 1.5 trillion. We use a DCC-MGARCH model to examine the return and volatility spillovers across three distinct classes of cryptocurrencies: coins, tokens, and stablecoins. Our results demonstrate that conditional correlations are time-varying, peaking during the COVID-19 pandemic sell-off of March 2020, and that both ARCH and GARCH effects play an important role in determining conditional volatility among cryptocurrencies. We find a bi-directional relationship for returns and long-term (GARCH) spillovers between BTC and ETH, but only a unidirectional short-term (ARCH) spillover effect from BTC to ETH. We also find spillovers from BTC and ETH to USDT, but no influence running in the other direction. Our results suggest that USDT does not currently play an important role in volatility transmission across cryptocurrency markets. We also demonstrate applications of our results to hedging and optimal portfolio construction. Full article

I examine the relationship between democracy and the perceived risk of corruption in a panel of 130 countries. My panel model controls for country fixed effects and enables the estimation of a within-country relationship between democracy and corruption. My main finding is that democracy significantly reduces the risk of corruption, but only in countries where ethnic fractionalization is low. In strongly fractionalized countries a transition from autocracy to democracy does not significantly reduce corruption. One explanation for these findings is that the corruption-reducing effect of greater accountability of politicians under democracy is undermined by the common pool problem; fractionalization increases the severity of the common pool problem. Full article

This paper studies the impact of a high-frequency investor sentiment measure (New FEARS) on the returns of foreign securities listed in U.S. markets as American Depository Receipts (ADRs). We recreate a high-frequency investor sentiment measure by aggregating search volume indices (SVIs) for a set of negative economic search terms. We find that ADR aggregate market returns exhibit a negative reaction to increases in searches for negative economic terms such as “recession”, “crisis”, and “bankruptcy” by U.S. households. This is the first paper to measure the effects of high-frequency investor sentiment on cross-listed securities. Moreover, the results are consistent throughout our study regardless of the variation of sentiment and aggregate market return measure we use. We also explore ADR regional market indices and show that Latin American ADRs are more sensitive to this investor sentiment measure. Full article

Recent studies on decision analytics frequently refer to the topic of behavioral decision making (BDM), which focuses on behavioral components of decision analytics. This paper provides a critical review of literature for re-examining the relations between BDM and classical decision theories in both normative and descriptive reviews. We attempt to capture several milestones in theoretical models, elaborate on how the normative and descriptive theories blend into each other, thus motivating the mostly prescriptive models in decision analytics and eventually promoting the theoretical progress of BDM—an emerging and interdisciplinary field. We pay particular attention to the decision under uncertainty, including ambiguity aversion and models. Finally, we discuss the research directions for future studies by underpinning the theoretical linkages of BDM with fast-evolving research areas, including loss aversion, reference dependence, inequality aversion, and models of quasi-maximization mistakes. This paper helps to understand various behavioral biases and psychological factors when making decisions, for example, investment decisions. We expect that the results of this research can inspire studies on BDM and provide proposals for mechanisms for the development of D-TEA (decision—theory, experiments, and applications). Full article

The objective of this study is to investigate the nexus between financial development (FD) in economic growth (GROWTH) in developing countries. The study uses panel data from 138 developing countries during the period 1980–2018. The relationship between financial development and economic growth is investigated using four explanatory variables that are commonly used to measure the level of financial development and several other control variables, including a dummy variable representing the financial and banking crises. The sample of 138 developing countries is also classified into six geographic regions. We have carried out panel unit-root tests and panel cointegration tests before estimating the specified models using both Panel Least Squares (Panel LS) and Panel Fully Modified Least Squares (FMOLS) methods. In addition, panel Granger causality tests have been conducted to identify the direction of causality between FD and GROWTH for each of the regions. The results of the study provide evidence of a direct relationship between FD and GROWTH in developing countries. Furthermore, there is evidence of bi-directional causality running from FD to GROWTH and from GROWTH to FD in samples of Europe and Central Asia, South Asia, and all countries, but not in East Asia and Pacific, Latin America and the Caribbean, Middle East and North Africa, and Sub-Saharan Africa. Full article

Modern society has developed in such a way that social reality is characterized by the significant dynamics of all processes and their uncertainty. Under such conditions, risk accompanies any purposeful activity of the social subject, and, in turn, the latter is aimed at reducing the uncertainty of its results. The purpose of this paper is to form the basis of a comprehensive study of social risks in the labor sphere and to develop practical recommendations for minimizing their negative consequences. In order to determine the main factors influencing the probability for the unemployed not to work in the specialty in which they have trained, we used the data of a micro-level survey on economic activity of the population to build linear regression models based on structural variables. As a result of applying the method of economic-mathematical modeling, in particular the basics of probability theory, the models of social risks of unemployment in terms of occupational groups and employment of unemployed persons outside of the specialty they have trained in were developed. The models developed made it possible to formalize and identify patterns of supply and demand dynamics of labor in terms of professions, as well as to identify the main factors influencing the change in the probabilistic characteristics of employment of unemployed persons outside of the specialty they have trained in. Full article

This research is motivated by the Omani government’s desire to reduce tax avoidance and bolster tax revenue collected from financial institutions. The purpose of this paper is to examine the impact of overlapped audit committee (AC) chairs and other directors on tax avoidance practice and whether they play a monitoring or advisory role in tax avoidance practice. As a measure of overlapped AC chairs, we used a dummy variable to indicate whether an AC chair sits on other committees within a company or not. We used the proportion of AC members who serve on the AC and other committees within a company as our proxy for overlapped AC directors. We used a company’s cash effective tax rate as a proxy for tax avoidance. We regressed tax avoidance on overlapped AC membership and other control variables, using a sample of 204 firm-year observations from financial institutions listed on the Muscat Stock Exchange between 2014 and 2019. Our regression results show that a higher proportion of overlapped AC members and the presence of an overlapped AC chair were both associated with lower effective tax rates, which equated to more tax avoidance. This suggests that these directors play an advisory role in the Omani context. We found, however, that these directors play a monitoring role when firms take a loss. From these findings, we draw important implications for regulators who need to rethink the potential consequences of having overlapped AC chairs and AC directors. Our study focuses on Omani financial institutions, which are highly regulated and monitored by the central bank, and our findings may not be directly applicable to non-financial institutions that are less regulated, so caution is needed when interpreting the findings. Further research could employ a repeated measured research design, such as ours, and explore the same research question in non-financial institutions. Full article

In this study, we predicted the log returns of the top 10 cryptocurrencies based on market cap, using univariate and multivariate machine learning methods such as recurrent neural networks, deep learning neural networks, Holt’s exponential smoothing, autoregressive integrated moving average, ForecastX, and long short-term memory networks. The multivariate long short-term memory networks performed better than the univariate machine learning methods in terms of the prediction error measures. Full article

This is the first study to use the self-organisation (Kohonen) map technique, an artificial neural network based on a non-supervised learning algorithm, to categorise Vietnamese banks into super-class groups. Drawing on unbalanced yearly data from 2008 to 2017, this study identifies two super-class groups (one and two). While group one consists of joint stock banks, group two consists of commercial state and joint stock banks. Using the non-structural indicator, the Lerner index, to capture market power, and the data enveloped analysis technique to measure bank performance, our result shows significant differences in Lerner scores (which represent bank market power) of the two groups of banks. Differences in the Lerner scores provide evidence of a group of strong banks that is isolated from other banks. This implies that this strong bank group has the potential to be monopolist and impairs Vietnam’s competitive banking environment. The reason is that group two banks may be more profitable due to greater market power, whereas group one banks may struggle to cut costs to remain viable. These findings provide a better understanding for bank executives, policymakers and regulators of the Vietnam banking industry, and ensure an efficient and competitive Vietnam banking environment. Full article

This paper aims to contribute to the existing literature in portfolio management and strategy by investigating the performance, diversification, and hedging benefits arising from integrating Sharia-compliant stocks into a conventional portfolio. Thus, this paper tests the performance of a Combined Portfolio, resulting from the combination of conventional Islamic instruments, covering different macroeconomic scenarios in the last decade (2010–2020). The strategic asset allocation was designed following the Global Macro Anima (GMA) strategy, solving a risk-parity optimisation problem using a specifically developed MATLAB™ algorithm. The findings will contribute to answering the question related to the possibility of including alternative instruments to increase diversification with hedging benefits by building asset allocations that perform well across different macroeconomic scenarios. Full article

This paper proposes a novel measure of political risk that confirms some of the findings documented in the Foreign Direct Investments (FDI) literature. Particularly, we confirm the positive relationship between political stability and its components on FDI inflows, and the moderating effect of natural resources on this relationship. The proposed political risk measure contains relevant, unique and incremental information not observed in the literature. For example, although this measure is highly correlated with the political risk rating of the International Country Risk Guide (ICRG), it contains unique information that explains FDI inflows beyond what is explained by the ICRG rating. A link to the database for our political risk rating for 150 countries covering 2000 to 2015 has been provided. Full article

A constant in the business world is the frequent movement of customers joining or abandoning companies’ services and products. The customer is one of the company’s most important assets. Reducing the customer abandonment rate has become a matter of survival and, at the same time, the most efficient way to maintain the customer base, since the replacement of dropouts by new customers costs, on average, 40% more. Aiming to mitigate the churn (customer evasion) phenomenon, this study compared predictive models to discover the most efficient method to identify customers who tend to drop out in the context of a banking organization. A literature review of related works on the subject found the neural network, decision tree, random forest and logistic regression models were the most cited, and thus the models were chosen for this work. Quantitative analyses were carried out on a sample of 200,000 credit operations, with 497 explanatory variables. The statistical treatment of the data and the developments of predictive models of churn were performed using the Orange data mining software. The most expressive results were achieved using the random forest model, with an accuracy of 82%. Full article

The purpose of this research is to identify the risks and deficiencies that affect the performance of companies that provide vehicle after-sales services. Thus, this paper highlights the results of a comparative study based on a questionnaire conducted at the level of six brands in the automotive industry. A model was developed to investigate the factors that affect the global performance of the after-sales sector and the authenticity of the information related to the issue studied. Moreover, based on the collected data, this study evaluates the strategies related to performance management used by the organizations studied. In the end, even if the results showed a score of 81% on the questionnaire, we found that companies that provide vehicle after-sales services have not implemented and do not maintain totally the strategies related to performance management. Consequently, the need for change can be emphasized. Based on the analyzed data in the second part of the paper, we identified deficiencies and risks in terms of the organization, operation and management of the service units. These results confirm that the vehicle repair service has a significant influence on employee and customer satisfaction, on the quality of the vehicles repaired and the repair completion time. Full article

Counterparty risk in the form of investment fraud damages a retiree’s nest egg. Does fraud negatively impact portfolios that are both stock and bond-heavy equally? This study uses Monte Carlo analysis within the Trinity Study framework to determine the average reduction in portfolio success of a retiree who experiences fraud. Findings suggest that each incidence of fraud results in a loss of three percentage points in retirement success. However, portfolios containing some bonds (75/25, 50/50, and 25/75) outperform all equity (and all bond) allocations, particularly when fraud is present. On average, each incident of fraud reduces the chance the victim will enjoy a successful retirement by nearly 3%. Various limitations, implications, and future research possibilities are discussed. Full article

Using monthly zip-code level data on credit card transactions covering 16 U.S. cities, this paper investigates changes in consumption at local commercial places during the early coronavirus disease 2019 (COVID-19) era. Since using aggregate-level data can suppress valuable information on consumption patterns coming from zip codes, the main contribution is achieved by estimating common factors across zip codes that are controlled for factors that are zip-code and time specific as well as those that are zip-code and sector specific. The estimation results based on common factors across zip codes show that relative consumption of products and services that can be consumed at home (e.g., grocery, pharmacy, home maintenance) has increased up to 56% amid COVID-19 compared to the previous year, whereas relative consumption of products and services that cannot be consumed at home (e.g., fuel, transportation, personal care services, restaurant) has decreased up to 51%. Similarly, after controlling for the corresponding factors, online shopping has increased up to 21%, while its expenditure share has increased by up to 16% compared to the pre-COVID-19 period. Full article

Recent value factor underperformance has called into question whether the value factor payoff is cyclically low, or if there are more structural challenges. We use a new approach to explore a link between the well-known macroeconomic exposures of traditional asset classes and those of value premia in a multi-asset context, focusing on country equities, bonds, and currencies in developed markets. Taking advantage of the cross-country inflation and growth expectations implicit in every value portfolio, we derive the net inflation and real growth characteristics embedded in each asset class carry portfolio at each point in time. Our analysis provides several insights: (1) Multi-asset value payoff is only weakly related to the global business cycle. (2) However, we find that the payoff to value portfolios is strongly linked to relative growth and inflation expectations across countries. (3) Over the last decade, we find that cheaper assets have had much lower net relative macro exposures compared to earlier time periods. This characteristic coincides with the period of unconventional central bank policies designed to lift global growth after the Global Financial Crisis (GFC). Full article

The world has been severely challenged by the Coronavirus Disease (COVID-19) outbreak since the early 2020s. Worldwide, there have been more than 66 million cases of infection and over 3,880,450 deaths caused by this highly contagious disease. All sections of the population including those who are affected, those who are not affected and those who have recovered from this disease, are suffering physiologically, psychologically or psychophysiologically. In this paper we briefly discuss the consequences of COVID-19 on physiological, psychological and psychophysiological vulnerability. We also attempt to provide evidence in support of exercise management as a prevention strategy for improving and minimizing the physiological, psychological and psychophysiological effects of COVID-19. Moderate exercise including walking, yoga and tai-chi to name but a few exercise regimes are critical in preventing COVID-19 and its complications. Governments, public health authorities and the general population should maintain physical activity during the COVID-19 pandemic to prevent additional physical and mental distress. Full article