Risks, Volume 10, Issue 11 (November 2022) – 22 articles

Cybersecurity breach probability functions describe how cybersecurity investments impact the actual vulnerability to cyberattacks through the probability of success of the attack. They essentially use mathematical models to make cyber-risk management choices. This paper provides an overview of the breach probability models that appear in the literature. For each of them, the form of the mathematical functions and their properties are described. The models exhibit a wide variety of functional relationships between breach probability and investments, including linear, concave, convex, and a mixture of the latter two. Each model describes a parametric family, with some models have a single parameter, and others have two. A sensitivity analysis completes the overview to identify the impact of the model parameters: the estimation of the parameters which have a larger influence on the breach probability is more critical and deserves greater attention. Full article

This paper proposes a two-step LASSO based vector autoregressive (2-LVAR) model to forecast mortality rates. Within the VAR framework, recent studies have developed a spatial–temporal autoregressive (STAR) model, in which age-specific mortality rates are related to their own historical values (temporality) and the rates of the neighboring cohorts (spatiality). Despite its desirable age coherence property and the improved forecasting accuracy over the widely used Lee–Carter (LC) model, STAR employs a rather restrictive structure that only allows for non-zero cohort effects of the same cohorts and the neighboring cohorts. To address this limitation, the proposed 2-LVAR model adopts a data-driven principle, as in a sparse VAR (SVAR) model, to offer more flexibility in the parametric structure. A two-step estimation strategy is developed accordingly to resolve the challenging objective function of 2-LVAR, which consists of non-standard L2 and LASSO-type penalties with constraints. Using empirical data from Australia, the United Kingdom, France, and Switzerland, we show that the 2-LVAR model outperforms the LC, STAR, and SVAR models in most of our forecasting results. Further simulation studies confirm this outperformance, and analyses based on life expectancy at birth empirically support the existence of age coherence. The results of this paper will help researchers understand the mortality projections in the long run and improve the reserving/ratemaking accuracy for life insurers. Full article

This paper introduces a model for intraday copper futures prices based on a stochastic differential equation (SDE). In particular, we derive an SDE that fits the model to the data and that is based on the whitening filter approach, a method characterizing linear time-variant systems. This method is applied to construct a model able to simulate the trajectories of copper futures prices, statistically described by means of an empirical autocorrelation approach. We show that the predictability of copper futures prices is rather weak. In fact, the developed model produces trajectories close to the actual data only in the short term. Consequently, the investment risk for copper futures is high. We also show that the performance of the model improves significantly if the time series satisfy particular conditions, e.g., those with a determinism measure. Full article

In this paper, we investigated rates of admission to hospitals (or other health facilities) due to respiratory diseases in a United States working population and their dependence on a number of demographic and health insurance-related factors. We employed neural network (NN) modelling methodology, including a combined actuarial neural network (CANN) approach, and model admission numbers by embedding Poisson and negative binomial count regression models. The aim is to explore the gains in predictive power obtained with the use of NN-based models, when compared to commonly used count regression models, in the context of a large real data set in the area of healthcare insurance. We used nagging predictors, averaging over random calibrations of the NN-based models, to provide more accurate predictions based on a single run, and also employed a k-fold validation process to obtain reliable comparisons between different models. Bias regularisation methods were also developed, aiming at addressing bias issues that are common when fitting NN models. The results demonstrate that NN-based models, with a negative binomial distributional assumption, provide improved predictive performance. This can be important in real data applications, where accurate prediction can drive both personalised and policy-level interventions. Full article

In this paper, the insurance reserve period will be classified according to the claim frequency domain, such as high- or low-frequency periods. We use the clustering method to create and group claims data according to their frequency period. Meanwhile, we use a risk process to mimic and predict the movement of the reserve from time to time in each group of claim period that is formed. The risk process model used here is the Hawkes process, which is a one-dimensional simple point process and a special type of self-exciting process. Based on this process, we will estimate the reserve at a certain date in the future and the average historical reserve for each group period. Full article

The performance of the Ghana Stock Exchange (GSE) over the years has been susceptible to both crises and country-specific factors reflected in its macroeconomic fundamentals. Accordingly, the GSE composite index (GSECI) has experienced rapid fluctuations across time, coupled with a declining market capitalisation from a reduction in the number of existing firms. The plunge in the number of firms is partly linked to the banking sector clean-up in 2017, which induced the collapse and consolidation of some financial institutions as well as weaknesses in other macroeconomic variables. This ignites an investigation into whether the synergistic impact of listed firms that represent the financial sector and the soundness of the banking sector measures are dominant factors that could drive or respond to shocks. Hence, the study investigates the lead-lag relationships and degree of integration among two indicators of the GSE—GSECI and GSE financial index (GSEFI), seven banking financial soundness indicators and eight interest rate measures. The wavelet approaches (biwavelet and wavelet multiple) are utilised to address the research problem. The DCC-GARCH connectedness approach is then employed as a robustness check. We found high interconnectedness between the indicators of the GSE and banking sector financial soundness, relative to the interest rates. Notwithstanding, the Treasury bill measures drive the GSE indicators in the short-, and medium-terms. In comparison with the two indicators of the GSE, significant comovements are dominant between the GSEFI and the two forms of selected macroeconomic variables. We advocate that the comovements among the indicators of the GSE, banking sector financial soundness and interest rate measures are heterogeneous and adaptive, especially during crises, but more significant comovements are germane to the GSEFI. The study provides further implications for policy, practice, and theory. Full article

The dynamics of the financial reliability of insurers show rather unstable and often unfavorable trends, which indicate an increase in the risks of their financial insecurity and requires searching for reserves to improve their financial condition in the context of digitalization. The aim of the present research is to develop approaches for managing financial risks and profitability in Russian insurance companies in the context of digitalization. Structurally, the study consisted of a comprehensive analysis of the insurance market in the Russian Federation, as well as an identification of the components of the risk management process of insurance companies in the context of digitalization. Documents containing key features of the risk management system were selected for the study. We determined that to optimize the structure of the insurance portfolio, the insurer must regulate its portfolio by increasing the share of insurance receipts for personal insurance, which is highly profitable but occupies a meager share in the insurance portfolio. To do this, it is necessary to carry out active work to expand the insurance field, in particular, in relation to voluntary personal insurance, attracting a significant number of policyholders by conducting explanatory mass work using advertising events and agency-broker networks regarding the need and effectiveness of such insurance. Further research prospects should include proposals for replenishing the insurance portfolio with new types of personal insurance, making adjustments to the tariff policy of insurers for all types of voluntary personal insurance, and determining optimal tariffs. Full article

The PRISM method is a risk assessment approach that focuses on hidden-risk identification and ranking. The combined AHP-PRISM method was created for strategic assessments based on pairwise comparisons. The PRISM and AHP-PRISM methods have remarkable visual decision support and control functions that make them useful in practical problem solving. However, the methods can be successfully applied with the same factor weights. To eliminate this significant disadvantage and enable an in-depth analysis of the alternatives based on the ideal best and ideal worst solutions, AHP-PRISM was integrated with TOPSIS in this study. As a result, the novel AHP-TOPSIS-based PRISM method can be configured more extensively for practical decision-making problems than the previous PRISM approaches. In addition, the novel method supports the ideal best and worst analysis of the alternatives without losing its ability to focus on identifying hidden risk. The method was tested on data related to strategic incident groups of incoming logistics business processes at a nuclear power plant. Full article

Trading in binary options is discussed using an approach based on expected profit (EP) and expected loss (EL) as metrics of reward and risk of trades. These metrics are reviewed and the role of the EL/EP ratio as an indicator of quality of trades, taking risk tolerance into account, is discussed. Formulas are derived for the EP and EL of call and put binaries assuming that the price of the underlying asset follows a geometric Brownian motion. The results are illustrated with practical data from the Nadex trading platform. The Black–Scholes notion of implied volatility is extended to wider notions of implied drift and volatility of the price process of the underlying asset. Illustrations show how these notions can be used to identify attractive binary trades, taking anticipated price movement into account. The problem of selecting portfolios of call and put binary options which maximize portfolio EP while constraining the portfolio EL to satisfy risk tolerance and diversification requirements, is formulated and solved by linear programming. This is also illustrated with the Nadex data under various scenarios. Full article

We consider a defined-contribution (DC)-pension-fund-management problem under partial information. The fund manager is allowed to invest the wealth from the fund account into a financial market consisting of a risk-free account, a stock and a rolling bond. The aim of the fund manager is to maximize the expected utility of the terminal wealth. In contrast to the traditional literature, we assume that the fund manager can only observe the stock-price process and the interest-rate process, but the expected return rate of the stock is unobservable, following a mean-reverting stochastic process. We apply a martingale approach and Clark’s formula to solve this problem and the closed-form representations for the optimal terminal wealth and trading strategy are derived. We further present the results for the constant relative risk aversion (CRRA) function as a special case. Full article

Related party transactions (RPT) are a common transaction conducted among companies and are the focus of the business world today. The purpose of this study is twofold, as follows: first, to provide empirical evidence for whether the RPT of related party loans in manufacturing companies in Indonesia is an opportunistic transaction or an efficient transaction, and second, to provide evidence for whether there are differences in company perspectives before and during the COVID-19 pandemic. This study employs data from all manufacturing companies listed on the Indonesia Stock Exchange (IDX). The data analysis techniques include descriptive statistical and hypothesis testing. The results of this study in the period 2018–2021 show that RPT has a positive effect on company value. During this period, that is, the years prior to the COVID-19 pandemic, RPT had a negative effect on company value. In contrast, the 2020–2021 period (during the COVID-19 pandemic) shows the opposite result: RPT has a positive effect on company value. The results of this study suggest that in the 2018–2021 and the pandemic period (2020–2021), companies conducted RPT for efficiency purposes, while prior to the pandemic (2018–2019) RPT was conducted for opportunistic purposes. Full article

Fintech allows investors to explore previously unavailable investment opportunities; it provides new return opportunities while also introducing new risks. The aim of this study is to investigate the relationship between risk and return in the fintech industry in the Indian stock market. This article is based on market-based research that focuses on demonstrating the volatility in the fintech market’s prices and demystifying the opportunities. Secondary data were collected from the Bombay Stock Exchange’s official fintech industry website from January 2017 to July 2022 to determine whether there is any dynamic link between risk and return in the Indian fintech market. The variance-based Mean-GARCH (GARCH-M) model was used to determine whether there is a dynamic link between risk and return in the Indian fintech market. The findings emphasize the importance of taking the risk of investing in India’s fintech industry. The implications for stock investors’ and fund managers’ portfolio composition and holding periods of equities or market exposure are significant. Finally, depending on their investment horizons, the Indian fintech industry may yield significant profits for risk-taking individuals. Full article

Technological innovation has changed the financial market significantly with the increasing application of high-frequency data in research and practice. This study examines the performance of intraday implied volatility (IV) in estimating currency options prices. Options quotations at a different trading time, such as the opening period, midday period and closing period of a trading day with one-month, two months’ and three months’ maturity, are employed to compute intraday IV for pricing currency options. We use the Mincer–Zarnowitz regression test to analyse the volatility forecast power of IV for three different forecast horizons (within a week, one week and one month). Intraday IV’s capability in estimating currency options price is measured by the mean squared error, mean absolute error and mean absolute percentage error measure. The empirical findings show that intraday IV is the key to accurately forecasting volatility and estimating currency options prices precisely. Moreover, IV at the closing period of the beginning of the week contains crucial information for options price estimation. Furthermore, the shorter maturity intraday IV is suitable for pricing options for a shorter horizon. In comparison, the intraday IV based on the longer maturity options subsumes appropriate information to price options with higher accuracy for the longer horizon. Our paper proposes a new approach to accurately pricing currency options using high-frequency data. Full article

Financing is the main source of Islamic bank income as a financial intermediary that will contribute to the bank’s profitability. There are two financing schemes, namely profit–loss-sharing financing and nonprofit–loss-sharing financing. The main purpose of this study is to analyze the impact of profit–loss-sharing financing on the Islamic bank’s profitability. We employ 31 Islamic commercial banks in Indonesia using quarterly data and spanning from 2016 Q1 to 2020 Q4. Dynamic panel regression using the two-step system GMM is applied. The results showed that profit–loss-sharing financing has a negative effect on profitability, suggesting that profit–loss financing discourages Islamic bank performance. Meanwhile, some control variables such as size and liquidity risk positively influence profitability and low efficiency, and financing quality negatively affects profitability. These findings have an important implication for Islamic banks. Islamic banks must conduct tight monitoring for PLS financing so that this ex-post scheme can encourage the performance of Islamic banks. Full article

The motivation for this study was a new context associated with the increased cyclical nature of the economy and, accordingly, the increased financial risks of the business, which complicated the implementation of corporate social responsibility. The purpose of the article is to explore the relationship of corporate social responsibility with the financial risks of the business and explain this relationship in terms of sustainable development (SDGs). The article contributes to the development of the concept of financial risks of the business by clarifying their connection with corporate social responsibility and substantiating the relationship between the financial risks of the business. Structural equation modeling (SEM) showed that in 2020–2021, financial risks have demonstrated a complex (in most cases negative) relationship with each other and a contradictory impact on corporate social responsibility. The complex systemic relationship between corporate social responsibility and financial risks of business from the point of view of sustainable development is substantiated. In the context of increased financial risks, by systematically implementing SDGs 8, 9, 11, and 12, responsible companies get the opportunity to restore and improve their position in the market. The significance of the findings for businesses is that they proposed the SDGs as a promising new benchmark for business financial risk management. This will allow responsible companies to find a new Pareto optimum in the current conditions of uncertainty and determine for themselves the preferred level of corporate social responsibility that contributes to the effective financial risks of business management in the long term. Full article

We provide an axiomatic foundation for the measurement of correlation diversification in a one-period portfolio model. We propose a set of eight desirable axioms for this class of diversification measures. We name the measures satisfying these axioms coherent correlation diversification measures. We study the compatibility of our axioms with rank-dependent expected utility theory. We also test them against the two most frequently used methods for measuring correlation diversification in portfolio theory: portfolio variance and the diversification ratio. Lastly, we provide an example of a functional representation of a coherent correlation diversification measure. Full article

We analyse four stochastic claims reserving methods in terms of their capability to estimate reserve risk and how successful they are at predicting distributions and VaRs of claim developments in particular. Both actual data and hypothetical claim triangles support our results. The appropriateness of the Solvency II risk margin on a one-year horizon and of the IFRS 17 risk adjustment in the long run largely vary by the chosen risk model. Despite the fact that IFRS 17 does not uniquely prescribe the metric for risk adjustment, we expect that VaR will be widely applied by insurance firms. Overall, actual data suggest that VaRs are predominantly underestimated by the models. Nevertheless, the $99.5\%$-VaRs under Solvency II are mostly sufficient on a 10-year-horizon to cover liabilities. Full article

A corporate socially responsible-focused approach adds value to a firm in the form of financial benefits in addition to improving its corporate image. To meet the demands of various stakeholders, including consumers, employees, and shareholders, and to produce high-quality financial reporting, some managers participate in CSR initiatives. The investigation of the relationship between corporate social responsibility and earnings management in publicly listed Visegrad companies is the main aim of the paper. The purpose is to identify the correlation between the CSR concept (measured by ESG score) and earnings management behavior determined by discretionary accrual levels (using the modified Jones model). To ascertain the association between CSR and earnings/discretionary accrual levels or to describe the major changes in the development of these variables, several statistical techniques were applied (correlation analysis, one-way ANOVA, and one-way ANOVA with repeated measures). As this is a pioneering study in the Visegrad environment (analyzing 35 publicly listed enterprises reporting ESG score), the research findings may have significant policy implications for decision-makers, regulators, auditors, and investors in their efforts to restrict earnings management techniques and enhance the quality of financial reporting. Full article

This paper explores the properties of a family of bivariate copulas based on a new approach using the counter-monotonic shock method. The resulting copula covers the full range of negative dependence induced by one parameter. Expressions for the copula and density are derived and many theoretical properties are examined thoroughly, including explicit expressions for prominent measures of dependence, namely Spearman’s rho, Kendall’s tau and Blomqvist’s beta. The convexity properties of this copula are presented, together with explicit expressions of the mixed moments. Estimation of the dependence parameter using the method of moments is considered, then a simulation study is carried out to evaluate the performance of the suggested estimator. Finally, an application of the proposed copula is illustrated by means of a real data set on air quality in New York City. Full article

Introducing uncertainty under fiscal sustainability conditions for the public debt provides a framework for analyzing debt dynamics. Such methods are commonly used for fiscal projections, but our aim here is retrospective; we evaluate the sudden jump in the Hungarian public debt following the global financial crisis in 2008. Based on a traditional debt-deficit stock-flow identity combining the fiscal component (primary deficit) and the interactions among real sector components, we model the debt dynamics by a vector error correction model (VECM). Uncertainty is represented in the model by shocks that are identified in the VECM framework. Using this method for simulation starting from 2006, we found that the debt-to-GDP ratio in 2008 and after could not be ruled out by 90 percent probability. Such an event was coded in the Hungarian debt dynamics and very likely would have materialized even without the unfortunate events of the global financial crisis. Full article

Under-reporting in cyber incidents is a well-established problem. Due to reputational risk and the consequent financial impact, a large proportion of incidents are never disclosed to the public, especially if they do not involve a breach of protected data. Generally, the problem of under-reporting is solved through a proportion-based approach, where the level of under-reporting in a data set is determined by comparison to data that is fully reported. In this work, cyber insurance claims data is used as the complete data set. Unlike most other work, however, our goal is to quantify under-reporting with respect to multiple dimensions: company revenue, industry, and incident categorization. The research shows that there is a dramatic difference in under-reporting—a factor of 100—as a function of these variables. Overall, it is estimated that only approximately 3% of all cyber incidents are accounted for in databases of publicly reported events. The output of this work is an under-reporting model that can be used to correct incident frequencies derived from data sets of publicly reported incidents. This diminishes the “barrier to entry” in the development of cyber risk models, making it accessible to researchers who may not have the resources to acquire closely guarded cyber insurance claims data. Full article

In this research study, we show how existing approaches of using generative adversarial networks (GANs) as economic scenario generators (ESG) can be extended to an entire internal market risk model—with enough risk factors to model the full band-width of investments for an insurance company and for a time horizon of one year, as required in Solvency 2. We demonstrate that the results of a GAN-based internal model are similar to regulatory-approved internal models in Europe. Therefore, GAN-based models can be seen as an alternative data-driven method for market risk modeling. Full article