Risks, Volume 10, Issue 6 (June 2022) – 21 articles

We introduce here a diffusion-type approximation of the ruin probability both in finite and infinite time for a two-dimensional risk process, where claims and premiums are shared with a predetermined proportion. This type of process is often called the insurer–reinsurer model. We assume that the flow of claims is governed by a general renewal process. A simple ruin probability formula for the model is known only in infinite time for the special case of the Poisson process and exponentially distributed claims. Therefore, there is a need for simple analytical approximations. In the literature, in the infinite-time case, for the Poisson process, a De Vylder-type approximation has already been introduced. The idea of the diffusion approximation presented here is based on the weak convergence of stochastic processes, which enables one to replace the original risk process with a Brownian motion with drift. By applying this idea to the insurer–reinsurer model, we obtain simple ruin probability approximations for both finite and infinite time. We check the usefulness of the approximations by studying several claim amount distributions and comparing the results with the De Vylder-type approximation and Monte Carlo simulations. All the results show that the proposed approximations are promising and often yield small relative errors. Full article

Since December 2019 we have been living with the virus known as SARS-CoV-2, a situation which has led to health policies being given prevalence over economic ones and has caused a paralysis in the demand for raw materials for several months due to the number confinements put in place around the world. Since the worst days of the pandemic caused by COVID-19, most commodity prices have been recovering. The main objective of this research work is to learn about the evolution and impact of COVID-19 on the prices of raw materials in order to understand how it will affect the behavior of the economy in the coming quarters. To this end, we use fractionally integrated methods and an Artificial Neural Network (ANN) model. During the COVID-19 pandemic episode, we observe that commodity prices have a mean reverting behavior, indicating that it will not be necessary to take additional measures since the series will return, by themselves, to their long term projections. Moreover, in our forecast using ANN algorithms, we observe that the Bloomberg Spot Commodity Index will recover its upward trend, increasing some 56.67% to the price from before the start of the COVID-19 pandemic episode. Full article

There are numerous risks associated with mining and owning cryptocurrencies, and exploring and producing oil and natural gas are highly risky, costly, and controversial. A marriage of digital mining and exploring and producing oil and natural gas has reduced the major risks and costs for both the crypto miner and the petroleum industry. On the one hand, crypto mining requires an enormous amount of electricity, which is not environmentally friendly. On the other hand, when drilling for petroleum resources, natural gas is often discovered, but due to a lack of resources or pipeline availability, a massive amount of natural gas is vented into the atmosphere or burned (called flaring). Today, however, this normally wasted gas (called stranded natural gas) is being used to create cheap electricity for mining server containers stationed near drilling rigs, which are used to create cryptocurrencies. This results in reduced CO₂ emissions, lower costs for drillers, and greater royalties going to landowners. Full article

This study quantifies the air quality impact on population mortality from an actuarial perspective, considering implications to the industry through the application of findings. The study focuses on the increase in mortality from air quality changes due to extreme weather impacts. We conduct an empirical study using monthly Californian climate and mortality data from 1999 to 2019 to determine whether adding PM2.5 as a factor improves forecast excess mortality. Expected mortality is defined using the rolling five-year average of observed mortality for each county. We compared three statistical models, namely a Generalised Linear Model (GLM), a Generalised Additive Model (GAM), and an Extreme Gradient Boosting (XGB) regression model. We find including PM2.5 improves the performance of all three models and that the GAM performs the best in terms of predictive accuracy. Change points are also considered to determine whether significant events trigger changes in mortality over extended periods. Based on several identified change points, some wildfires trigger heightened excess mortality. Full article

The objective of this paper is to analyze the volatility spillover effects in the Moroccan interbank sector before and during the COVID-19 pandemic crisis using the DY model. Specifically, this study assesses the impact of the recent COVID-19 outbreak on the transmission of volatility among Moroccan banks listed in the Moroccan stock market. The data sample frequency is daily and extends from 1 January 2012 to 31 December 2021, excluding holidays. The empirical results indicate that the volatility spillover index increased during the pandemic crisis. We also found varying degrees of interdependence and spillover effects between the six publicly traded Moroccan banks and the Moroccan banking sector stock index before and during the COVID-19 pandemic crisis. Full article

While previous academic research highlights the potential of machine learning and big data for predicting corporate bond recovery rates, the operations management challenge is to identify the relevant predictive variables and the appropriate model. In this paper, we use meta-learning to combine the predictions from 20 candidates of linear, nonlinear and rule-based algorithms, and we exploit a data set of predictors including security-specific factors, macro-financial indicators and measures of economic uncertainty. We find that the most promising approach consists of model combinations trained on security-specific characteristics and a limited number of well-identified, theoretically sound recovery rate determinants, including uncertainty measures. Our research provides useful indications for practitioners and regulators targeting more reliable risk measures in designing micro- and macro-prudential policies. Full article

Research on cryptocurrencies has proliferated in recent years. Our research objective was to answer the question of whether macroeconomic news from the U.S. affects Bitcoin in the same way it affects other common investment assets such as gold, the S&P 500, 2-year Treasury bills, and 10-year Treasury bills. Following previous research, seven macroeconomic news announcements from the U.S. were selected, and an empirical analysis of the daily returns, volatility, and volume of the selected assets was conducted. The results show that while Bitcoin is the most volatile (i.e., riskiest) of all the assets, the expected direction of movement is visible after the official announcement of the macroeconomic news on that day, and is comparable to that of the 2-year Treasury bills. It is also evident that the trading volume of Bitcoin does not change, unlike other assets, suggesting that the price of Bitcoin is always moved by the same players, indicating the closed and, therefore, riskier nature of cryptocurrency markets. Finally, we found evidence that the impact of macroeconomic announcements on Bitcoin returns is stronger when the announcements are negative but, interestingly, the returns of Bitcoin, unlike those of other assets, are more volatile after positive announcements. Full article

Online activity increasing spreads with the power of technological development. Many studies reported the impact of online activities on decision making. From the statistical perspective, decision making is related to statistical inference. In this regard, it is interesting to propose a new method of statistical inference for online decisions. This method is built by the logarithm distribution of the likelihood function, which allows us to determine statistics using the normal statistical test approach iteratively. It means that the inference can be made in an online way every time new data arrive. Compared to classical methods (commonly, chi-squared), the advantage of this method is that it allows us to make decisions without storing large data. In particular, the novelty of this research is expressed in the algorithm, theorem, and corollary for the statistical inference procedure. In detail, this paper’s simulation discusses online statistical tests for multinomial cases and applies them to transportation data for item delivery, namely traffic density. Changes in traffic density resulted in changes to the strategy of item delivery. The goal is to obtain a minimum delivery time for the risk of losses. Full article

Pooled annuity products, where the participants share systematic and idiosyncratic mortality risks as well as investment returns and risk, provide an attractive and effective alternative to traditional guaranteed life annuity products. While longevity risk sharing in pooled annuities has received recent attention, incorporating investment risk beyond fixed interest returns is relatively unexplored. Incorporating equity investments has the potential to increase expected annuity payments at the expense of higher variability. We propose and assess a strategy for incorporating equity investments along with managed-volatility for pooled annuity funds. We show how the managed volatility strategy improves investment performance, while reducing pooled annuity income volatility and downside risk, as well as an investment strategy that reduces exposure to investment risk over time. We quantify the impact of pool size when equity investments are included, showing how these products are viable with relatively small pool sizes. Full article

Faced with the need to adjust public pension systems to meet changing demographic, economic and social conditions, most developed countries have created government reserve funds to ensure macroeconomic sustainability. This paper aims to study the importance that this reserve fund plays in the sustainability of the Spanish public pension system. Using data for the 2000 to 2019 period (20 observations) on the main variables impacting on the system, we calculate probabilities and other indicators of its unsustainability in relation to the reserve fund. Our model accurately reflects certain aspects of the data, and suggests that the probability of unsustainability is inversely associated with the size of the reserve fund, but that this relation is moderated by the heterogeneity of the members of the pension system. Moreover, the probability of unsustainability increases in line with the pension system deficit, the time elapsed until unsustainability is reached is shorter when the Reserve Fund balance falls, and the size of this fund at which the system becomes unsustainable diminishes with the probability of unsustainability. Full article

The aim of the paper is to identify groups of banks with similar environmental commitment, taking into account their direct environmental impact. The study, which employs the aggregation method, reveals that small banks with a relatively worse financial standing are characterised by the lowest level of disclosures within pro-ecological initiatives. At the same time, large international banks belong to clusters defined by the highest or the lowest disclosure rates. The above-mentioned phenomenon results from the dichotomy of integrating environmental policy into their strategies and business models. This study is the first comparative analysis of the extent to which all listed (and at the same time the biggest) banks operating in Poland have taken initiatives to reduce the negative environmental impact of their activities. Full article

In recent years it has become possible to collect GPS data from drivers and to incorporate these data into automobile insurance pricing for the driver. These data are continuously collected and processed nightly into metadata consisting of mileage and time summaries of each discrete trip taken, and a set of behavioral scores describing attributes of the trip (e.g, driver fatigue or driver distraction), so we examine whether it can be used to identify periods of increased risk by successfully classifying trips that occur immediately before a trip in which there was an incident leading to a claim for that driver. Identification of periods of increased risk for a driver is valuable because it creates an opportunity for intervention and, potentially, avoidance of a claim. We examine metadata for each trip a driver takes and train a classifier to predict whether the following trip is one in which a claim occurs for that driver. By achieving an area under the receiver–operator characteristic above 0.6, we show that it is possible to predict claims in advance. Additionally, we compare the predictive power, as measured by the area under the receiver–operator characteristic of XGBoost classifiers trained to predict whether a driver will have a claim using exposure features such as driven miles, and those trained using behavioral features such as a computed speed score. Full article

This paper aims to study the international experience (in the aspect and taking into account the specifics of regions of the world) integrating the SDGs into corporate strategies and to identify the following: (1) supported SDGs (UN standards); (2) implemented measures of corporate social responsibility to support the SDGs and (3) approach from the positions of risks for profit. Based on a sample of 193 countries (seven regions of the world) from 2020–2021 (386 observations) based on the method of structural equation modelling (SEM), it was discovered that the SDGs (UN standards) are supported by companies to a different extent in the different world regions, but, on the whole, they are strongly integrated into the corporate strategies in each region. The largest support of the SDGs from business is observed in the Organisation for Economic Co-operation and Development (OECD). The risks of integrating the SDGs (UN standards) into corporate strategies for profit are low (moderate in the OECD). The commercial approach to integrating the SDGs into corporate strategies is implemented in all regions of the world. The theoretical significance of the results consists in the fact that the discovered differences pointed at the necessity for and set the foundation for the transition from global to regional management of the integration of the SDGs (UN standards) into corporate strategies. The practical significance of the authors’ conclusions and developments consists in the fact that they allow increasing the effectiveness of risk management of the practices of corporate social responsibility for profit. Full article

We consider the optimal dividend problem in the so-called degenerate bivariate risk model under the assumption that the surplus of one branch may become negative. More specific, we solve the stochastic control problem of maximizing discounted dividends until simultaneous ruin of both branches of an insurance company by showing that the optimal value function satisfies a certain Hamilton–Jacobi–Bellman (HJB) equation. Further, we prove that the optimal value function is the smallest viscosity solution of said HJB equation, satisfying certain growth conditions. Under some additional assumptions, we show that the optimal strategy lies within a certain subclass of all admissible strategies and reduce the two-dimensional control problem to a one-dimensional one. The results are illustrated by a numerical example and Monte Carlo simulated value functions. Full article

This study aims at developing econometric models to manage the price risk of Dry and Wet Cocoa beans with the help of ARIMA (Autoregressive Integrated Moving Average) and VAR (Vector Auto Regressive). The monthly price of Cocoa beans is collected for the period starting from April 2009 to March 2020 from the office of CAMPCO Limited, Mangalore, and the ICE Cocoa futures price from the website of investing.com. The augmented dickey fuller test is used to test the stationarity of the series. The ACF and PACF correlograms are used to identify the tentative ARIMA model. Akaike information criterion (AIC) and Schwarz criterion (SBIC), Sigma square, and adjusted R² are used to decide on the optional AR and MA terms for the models. Durbin–Watson statistics and correlograms of the residuals are used to decide on the model’s goodness of fit. Identified optimal models were ARIMA (1, 1, 0) for the Dry Cocoa beans price series and ARIMA (1, 1, 2) for the Wet Cocoa beans price series. The multivariate VAR (1) model found that the US and London Cocoa futures prices traded on the ICE platform will influence the price of Dry Cocoa in India. This study will be helpful to forecast the price of Cocoa beans to manage the price risk, precisely for Cocoa traders, Chocolate manufacturers, Cocoa growers, and the government for planning and decision-making purposes. Full article

This study examines the antecedents/determinants of behavioral intentions toward the utilization of Islamic financial technology for Middle Eastern customers. The study applied structural equation modeling (PLS-SEM). After robust research efforts were invested in the identification of factors, they and were converted into measures, and the results were analyzed. The results demonstrate that the independent variables shown in the UTAUT model have a significant impact on the behavior to adopt Islamic financial technology, which implies that the people are ready to use Islamic financial technology while making online transactions. The work in this study adds to the knowledge regarding the factors affecting behavioral intention to use Islamic fintech, as there is scarcity of studies in this domain, especially in the context of Middle Eastern online customers. Moreover, this study also considers the major categories of online payments. Full article

Dependent Tail Value-at-Risk, abbreviated as DTVaR, is a copula-based extension of Tail Value-at-Risk (TVaR). This risk measure is an expectation of a target loss once the loss and its associated loss are above their respective quantiles but bounded above by their respective larger quantiles. In this paper, we propose nonparametric estimators for DTVaR and establish their property of consistency. Moreover, we also propose the variability measure around this expected value truncated by the quantiles, called the Dependent Conditional Tail Variance (DCTV). We use this measure for constructing confidence intervals of the DTVaR. Both parametric and nonparametric approaches for DTVaR estimations are explored. Furthermore, we assess the performance of DTVaR estimations using a proposed backtest based on the DCTV. As for the numerical study, we take an application in the insurance claim amount. Full article

The Lee–Carter model, the dominant mortality projection modeling in the literature, was criticized for its homoscedastic error assumption. This was corrected in extensions to the model based on the assumption that the number of deaths follows Poisson or negative binomial distributions. We propose a new class of families of counting distributions, namely, the ABM class, which belongs to a wider class of natural exponential families. This class is characterized by its variance functions and contains the Poisson and the negative binomial distributions as special cases, offering an infinite class of additional counting distributions to be considered. We are guided by the principle that the choice of distribution should be made from a pool of distributions as large as possible. To this end, and following a data mining approach, a training set of historical mortality data of the population could be modeled using the ABM’s rich choice of distributions, and the chosen distribution should be the one that proved to offer superior projection results on a test set of mortality data. As an alternative to parameter estimation via the singular value decomposition used in the classical Lee–Carter model, we adopted Bayesian estimation, harnessing the Markov Chain Monte Carlo methodology. A numerical study demonstrates that when fitting mortality data using this new class of distributions, while traditional distributions may provide desirable projections for some populations, for others, alternative distributions within the ABM class can potentially produce superior results for the entire population or particular age groups, such as the oldest-old. Full article

Organizations are formed to gain long-term benefits. However, sometimes myopic management for feigned value enhancement led to the early demise of the firm. Further, to the best of our knowledge empirical role of financing constraints has not yet been explored between the relationship of leverage and earnings management practices. Therefore, the present study aims to empirically examine the impact of leverage on Real Earnings Management (REM) practices and how financing constraints influence this association. Employs a panel dataset of 3250 non-financial Chinese listed firms for a time period spanning from 2009 to 2018. Leverage is categorized into short-term, long-term, and total leverage to check the individual effects of each leverage category on REM practices. The data were analyzed through panel data fixed-effects and random-effects techniques as an econometric approach. First, consistent with positive accounting theory, the impact of total leverage on REM is positive. Second, compared to the long-term leverage, short-term leverage has more pronounced effects on managers’ opportunistic behavior towards using REM. Third, the influence of total leverage is higher (lower) on REM practices in financially unconstrained (constrained) firms. Fourth, the influence of short-term leverage on REM practices compared to long-term leverage is also weak in the financially constrained firms. These findings imply that, to avoid the consequences of managerial myopia, investors should abstain to invest in the firms that use higher amount of short-term debt and are financially unconstrained. This study is the first research to examine the impact of different leverage categories on REM practices in an emerging market, i.e., China, where the legal and financial structure is much poor. Full article

The economic and financial chaos caused by COVID-19 has been a discussion topic since the beginning of 2020. This study intends to provide a parallel comparison of volatility change and external shock persistence of the Islamic and conventional stock indexes of the Pakistan Stock Exchange. The daily stock index was extracted from Eikon Thomson Reuters for the conventional and Islamic stock index from Jan 2018 to April 2021, which was further divided in three periods, i.e., full, pre-, and post-pandemic period. The data have been analyzed using generalized autoregressive conditional heteroscedasticity (GARCH). An optimally parameterized GARCH (1,1) model is used to measure volatility change for both the pre- to post-pandemic periods. The results suggest that the magnitude of risk in a conventional index is significantly higher than that of the Islamic stock index for the period of study. However, the level of COVID shock persistence is longer in the KSE (conventional) index compared to the KMI (Islamic) index. Full article