Risks, Volume 10, Issue 12 (December 2022) – 20 articles

The purpose of this paper was to investigate technologies, methods, and approaches that can be used to effectively manage smart city risks in the context of the COVID-19 pandemic. The paper was based on a review of specialized literature sources and expert statements on smart cities in times of crisis, specifically during COVID-19. A systematic literature review served as the research’s methodological foundation; this was supplemented by conceptual data analysis techniques and a modeling method. Our initial search yielded 234 research articles, 38 of which met our inclusion criteria and were included in the review. A further 32 studies fell outside of the criteria for supporting smart cities’ crisis management. The main findings showed that technologies can respond quickly to pandemic crisis risks while also ensuring the availability of urban functionality and that there are numerous risks in implementing technologies to achieve effective management. The main risks were privacy concerns, social inclusion, political bias, misinformation and fake news, and technical difficulties with education and distance employment. The practical significance of the paper lay in proposing a model based on specific technologies and policies aimed at effective risk management in the days of COVID-19. Full article

Portfolio credit risk is often concerned with the tail distribution of the total loss, defined to be the sum of default losses incurred from a collection of individual loans made out to the obligors. The default for an individual loan occurs when the assets of a company (or individual) fall below a certain threshold. These assets are typically modeled according to a factor model, thereby introducing a strong dependence both among the individual loans, and potentially also among the multivariate vector of common factors. In this paper, we derive sharp tail asymptotics under two regimes: (i) a large loss regime, where the total number of defaults increases asymptotically to infinity; and (ii) a small default regime, where the loss threshold for an individual loan is allowed to tend asymptotically to negative infinity. Extending beyond the well-studied Gaussian distributional assumptions, we establish that—while the thresholds in the large loss regime are characterized by idiosyncratic factors specific to the individual loans—the rate of decay is governed by the common factors. Conversely, in the small default regime, we establish that the tail of the loss distribution is governed by systemic factors. We also discuss estimates for Value-at-Risk, and observe that our results may be extended to cases where the number of factors diverges to infinity. Full article

Liquid money controlled by a trustworthy central bank can serve as an insurance against external surprises such as stock market crashes, bank fails and other setbacks that endanger the yield of illiquid savings. In turbulent times, the insurance property of money is particularly accentuated. The paper constructs a life cycle framework for the analysis of rational and irrational motives to save money, answers to questions about the effects of saving liquid money on labor supply, illiquid saving and education, and examines the inherent cost of saving cash. The main findings are the following. The rational insurance motive to save money increases total savings by replacing deposit saving more than one-to-one. The share of deposit savings depends positively on the expected interest rate, while the share of cash savings is the higher the less there is inflation. Deposit saving correlates positively and education negatively with the expected market interest rate thus affecting the relative proportion of liquid and illiquid saving, but the implicit cost of cash insurance is independent of education. Money illusion adds an internal bias to consumers’ life-time optimization, thus making them save excessively in cash at the cost of market deposits and increasing the cost of using cash as insurance against external uncertainty. Full article

The time series movements of Bitcoin prices are commonly characterized as highly nonlinear and volatile in nature across economic periods, when compared to the characteristics of traditional asset classes, such as equities and commodities. From a risk management perspective, such behaviors pose challenges, given the difficulty in quantifying and modeling Bitcoin’s price volatility. In this study, we propose hybrid analytical techniques that combine the strengths of the non-stationary properties of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models with the nonlinear modeling capabilities of deep learning algorithms, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) algorithms with single, double, and triple layer network architectures to forecast Bitcoin’s realized price volatility. Our findings, both in-sample and out-of-sample, show that such hybrid models can generate accurate forecasts of Bitcoin’s price volatility. Full article

Efficient management of working capital is essential for firms to avoid overinvesting in short-term assets for maximum profitability while guaranteeing much-needed liquidity to run their operations. This study examines the impact of working capital management on firms’ profitability in the automotive industry in Europe before and during the COVID-19 pandemic period. The automotive industry is vital to the European economy, being a major component of the total industrial value added to the GDP of the continent. Existing research on this topic is inconclusive, and there is a gap in the literature exploring the working capital management effect on firm performance in periods of crisis. Unlike most research, this study focuses on a single industry to better capture the impact of working capital management on firm profitability. It also adds the COVID-19 dimension to stress the importance of proper working capital management, especially in periods of economic distress. The results show that the receivables collection period, inventory conversion period, accounts payable period, and cash conversion cycle have a significant negative impact on ROA for both the pre-pandemic and pandemic period, suggesting that managers must be prudent regarding their firm’s credit policy by not being overly generous with credit terms and making every effort to promptly collect their receivables. Moreover, excessive levels of inventory impair profitability by locking up valuable cash reserves, which are vital, especially in periods of crisis. Though seemingly counterintuitive, being profitable also means not postponing payables settlement unnecessarily. Full article

In this paper, we apply machine learning models to execute certain short-option strategies on the S&P500. In particular, we formulate and focus on a supervised classification task which decides if a plain short straddle on the S&P500 should be executed or not on a daily basis. We describe our used framework and present an overview of our evaluation metrics for different classification models. Using standard machine learning techniques and systematic hyperparameter search, we find statistically significant advantages if the gradient tree boosting algorithm is used, compared to a simple “trade always” strategy. On the basis of this work, we have laid the foundations for the application of supervised classification methods to more general derivative trading strategies. Full article

This analysis is the first to investigate the influence of index futures trading volume on spot market volatility for the Ho Chi Minh Stock Exchange (HOSE). The data utilized in this study are the daily VN30-Index futures contract trading volume starting at the inception date for the VN30-Index futures contract, 10 August 2017 and going through 10 August 2022. Using an autoregressive distributed lag (ARDL) bounds testing approach, the empirical findings reveal a positive relation between VN30-Index futures trading volume and the volatility of the spot market for the HOSE in the short-run. In addition, the results of the ARDL tests confirm in for the long-run, trading volume of futures contracts has a significant positive influence on spot market volatility. Moreover, the results derived from the error correction model (ECM) indicate that only 5.54% of the disequilibria from the previous trading day are converged and corrected back to the long-run equilibrium from the current day. Based on the findings, we recommend that Vietnamese policymakers establish relevant intervention polices on speculation of individual investors in order to provide stabilization safeguards for the underlying stock market. Full article

This paper seeks to identify the most important global drivers of credit-to-GDP gaps for 35 countries. The analysis is performed on a country-by-country basis for the sub-periods 2000Q1:2007Q2, 2007Q3:2013Q4, and 2014Q1:2021Q1 and is based on two state-of-the-art methods for variable selection in the time series framework: the one covariate at a time multiple testing (OCMT) and adaptive least absolute shrinkage and selection operator (LASSO). We find that the number of salient global factors tends to increase over time, reaching its maximum during the post-crisis period. This period is also marked by a pronounced role of the global factors capturing the stance of the US monetary policy, while in the preceding sub-periods, the most significant factors are global credit conditions (the TED spread) and world industrial production, respectively. Regardless of the sub-periods, advanced economies’ credit-to-GDP gaps appear more dependent on the global factors than the gaps in emerging markets. In addition, we identify country-specific variables which shape the susceptibility of the national credit-to-GDP gaps to the global factors. Full article

In this paper, we introduce a 3D finite dimensional Gaussian process (GP) regression approach for learning arbitrage-free swaption cubes. Based on the possibly noisy observations of swaption prices, the proposed ‘constrained’ GP regression approach is proven to be arbitrage-free along the strike direction (butterfly and call-spread arbitrages are precluded on the entire 3D input domain). The cube is free from static arbitrage along the tenor and maturity directions if swaption prices satisfy an infinite set of in-plane triangular inequalities. We empirically demonstrate that considering a finite-dimensional weaker form of this condition is enough for the GP to generate swaption cubes with a negligible proportion of violation points, even for a small training set. In addition, we compare the performance of the GP approach with the SABR model, which is applied to a data set of payer and receiver out-of-the-money (OTM) swaptions. The constrained GP approach provides better prediction results compared to the SABR approach. In addition, we show that SABR calibration is better when using the GP cube output as new observations (in terms of predictive error and absence of arbitrage). Finally, the GP approach is able to quantify in- and out-of-sample uncertainty through Hamiltonian Monte Carlo simulations, allowing for the computation of model risk Additional Valuation Adjustment (AVA). Full article

The main objective of this study is to highlight the internal risk factors associated with maritime transportation accidents and the important role of presenting them in the dataset at the time of the incident. Since the study period involves a pre- and post-pandemic timeline, we refer to COVID-19, although it is not part of our analysis. The issue at hand is the appropriate statistical analysis and investigation of the possible correlations between the cause of the incident and internal factors/indicators that may affect the safety of crews on sea routes. We developed a comprehensive study based on advanced econometric modeling, utilizing multifactorial models of robust regression, structural equation modeling (SEM), and Gaussian/mixed-Markov graphical models (GGMs, MGMs) and applying them to a newly compiled dataset covering the 2014–2022 period. Our results bring to the fore important factors that can determine the causes of various accidents and injuries suffered by workers, ranging from work location to work activity and even the rank of the seafarers on board. We do not consider the external factors associated with a maritime transportation accident, as the risk of an accident in this sector due to external factors (i.e., weather conditions, defaults, failures, etc.) is limited. Reducing the number of injuries to seafarers will result not only in better seafarer health but also a reduction in the operating costs of shipping companies due to the reduced insurance premiums they will have to pay. It will also lead to a reduction in the amounts disbursed by Protection and Indemnity (P&I) Clubs to compensate seafarers. In future research, we will use external factors to determine seafaring risks related to, for example, weather conditions, the quality of ships, technology, safety measures, regulations, and more. Full article

Explainable Artificial Intelligence (XAI) models allow for a more transparent and understandable relationship between humans and machines. The insurance industry represents a fundamental opportunity to demonstrate the potential of XAI, with the industry’s vast stores of sensitive data on policyholders and centrality in societal progress and innovation. This paper analyses current Artificial Intelligence (AI) applications in insurance industry practices and insurance research to assess their degree of explainability. Using search terms representative of (X)AI applications in insurance, 419 original research articles were screened from IEEE Xplore, ACM Digital Library, Scopus, Web of Science and Business Source Complete and EconLit. The resulting 103 articles (between the years 2000–2021) representing the current state-of-the-art of XAI in insurance literature are analysed and classified, highlighting the prevalence of XAI methods at the various stages of the insurance value chain. The study finds that XAI methods are particularly prevalent in claims management, underwriting and actuarial pricing practices. Simplification methods, called knowledge distillation and rule extraction, are identified as the primary XAI technique used within the insurance value chain. This is important as the combination of large models to create a smaller, more manageable model with distinct association rules aids in building XAI models which are regularly understandable. XAI is an important evolution of AI to ensure trust, transparency and moral values are embedded within the system’s ecosystem. The assessment of these XAI foci in the context of the insurance industry proves a worthwhile exploration into the unique advantages of XAI, highlighting to industry professionals, regulators and XAI developers where particular focus should be directed in the further development of XAI. This is the first study to analyse XAI’s current applications within the insurance industry, while simultaneously contributing to the interdisciplinary understanding of applied XAI. Advancing the literature on adequate XAI definitions, the authors propose an adapted definition of XAI informed by the systematic review of XAI literature in insurance. Full article

In contrarian trading, investors buy and sell loser stocks (lowest average historical prices) and winner stocks (highest average historical prices), respectively. This study examines whether (a) Thailand Sustainability Investment-listed companies outperform Stock Exchange of Thailand (SET)-listed companies (from 1 January 2016 to 31 December 2019) in contrarian profits, (b) the five-factor model outperforms their 1993 three-factor model in explaining contrarian profits, and (c) risk drives the earnings of contrarians. Companies were divided into portfolios of winners and losers based on the average of the daily historical prices held in various eras. The SET-listed companies perform better in generating profits. The root mean squared error and mean absolute error—measurements of model accuracy—report that the error from the three-factor model is smaller than the one from the five-factor model. Thus, the three-factor model is applied to estimate the risk-adjusted return. Zero contrarian profits after risk adjustment confirms that they are risk-driven. Full article

We study two credit risk models with occupation time and liquidation barriers: the structural model and the hybrid model with hazard rate. The defaults within the models are characterized in accordance with Chapter 7 (a liquidation process) and Chapter 11 (a reorganization process) of the U.S. Bankruptcy Code. The models assume that credit events trigger as soon as the occupation time (the cumulative time the firm’s value process spends below some threshold level) exceeds the grace period (time allowance). The hazard rate model extends the structural occupation time models and presumes that other random factors may also lead to credit events. Both approaches allow the firm to fulfill its obligations during the grace period. We derive new closed-from pricing formulas for credit derivatives containing the (risk-neutral) probability of defaults and credit default swap (CDS) spreads as special cases, which are derived analytically via a spectral expansion methodology. Our method works for any solvable diffusion, such as the geometric Brownian motion (GBM) and several state-dependent volatility processes, including the constant elasticity of variance (CEV) model. It allows us to write the pricing formulas explicitly as infinite series that converges rapidly. We then calibrate our models (assuming that GBM governs the firm’s value) to market CDS spreads from the Total Energy company. Our calibration results show that the computations are fast, and the fit is near-perfect. Full article

The curse of dimensionality problem refers to a set of troubles arising when dealing with huge amount of data as happens, e.g., applying standard numerical methods to solve partial differential equations related to financial modeling. To overcome the latter issue, we propose a Deep Learning approach to efficiently approximate nonlinear functions characterizing financial models in a high dimension. In particular, we consider solving the Black–Scholes–Barenblatt non-linear stochastic differential equation via a forward-backward neural network, also calibrating the related stochastic volatility model when dealing with European options. The obtained results exhibit accurate approximations of the implied volatility surface. Specifically, our method seems to significantly reduce the neural network’s training time and the approximation error on the test set. Full article

Inventory leanness requires that firms minimize inventory mistreatment and misuse. A firm performance deteriorates because of high inventory misuse, and because of such an issue, the effect on the firm’s credit rating can also be seen. This study examines the effect of inventory leanness on firms’ credit ratings. It aims to create an understanding of the relationship between inventory leanness and the firm’s financial performance and provides insight into the credit rating system of Pakistan. We analyze secondary Pakistan data between 2008 and 2017. Among the sixty firms on Pakistan Stock Exchange that are rated by PACRA, only thirty-eight have complete data available on their respective websites. By using panel data analysis, the results indicate that inventory leanness and credit ratings are positively related. In an added analysis, we evaluate the financial performance in the context of credit rating by using control variables (size, leverage, and capital intensity ratio) and dummy variables (loss and subordinate debt). Our results are consistent with earlier studies. Full article

The aim of this paper is to introduce a two-step trading algorithm, named TI-SiSS. In the first step, using some technical analysis indicators and the two NLP-based metrics (namely Sentiment and Popularity) provided by FinScience and based on relevant news spread on social media, we construct a new index, named Trend Indicator. We exploit two well-known supervised machine learning methods for the newly introduced index: Extreme Gradient Boosting and Light Gradient Boosting Machine. The Trend Indicator, computed for each stock in our dataset, is able to distinguish three trend directions (upward/neutral/downward). Combining the Trend Indicator with other technical analysis indexes, we determine automated rules for buy/sell signals. We test our procedure on a dataset composed of 527 stocks belonging to American and European markets adequately discussed in the news. Full article

Data breach incidents result in severe financial loss and reputational damage, which raises the importance of using insurance to manage and mitigate cyber related risks. We analyze data breach chronology collected by Privacy Rights Clearinghouse (PRC) since 2001 and propose a Bayesian generalized linear mixed model for data breach incidents. Our model captures the dependency between frequency and severity of cyber losses and the behavior of cyber attacks on entities across time. Risk characteristics such as types of breach, types of organization, entity locations in chronology, as well as time trend effects are taken into consideration when investigating breach frequencies. Estimations of model parameters are presented under Bayesian framework using a combination of Gibbs sampler and Metropolis–Hastings algorithm. Predictions and implications of the proposed model in enterprise risk management and cyber insurance rate filing are discussed and illustrated. We find that it is feasible and effective to use our proposed NB-GLMM for analyzing the number of data breach incidents with uniquely identified risk factors. Our results show that both geological location and business type play significant roles in measuring cyber risks. The outcomes of our predictive analytics can be utilized by insurers to price their cyber insurance products, and by corporate information technology (IT) and data security officers to develop risk mitigation strategies according to company’s characteristics. Full article

Movements in the volatility index of the Indian economy are influenced by global volatility indices (fear index). This study evaluates the influence of various global implied volatility indices in forecasting the day-to-day binary movements in the implied volatility index of India, denoted by the symbol ‘India VIX’. Historical daily data from 18 September, 2009, to 2 December, 2021, was acquired, and the target labels were created from changes in the India VIX. A set of classifiers, consisting of Logistic Regression, Random Forest and Extreme Gradient Boosting (XG Boost), were applied to rank the feature variables according to their importance. This study revealed that India’s VIX was impacted most by the previous day’s changes in the closing value of the US implied volatility indices, except for the Chicago Board Options Exchange (CBOE) Eurocurrency volatility index. Additionally, the Eurozone implied volatility index was also important. However, the implied volatility indices of Australian Hang Seng and Japan were the least important. This study’s outcomes help Indian traders in creating a watch list of important volatility indices. Full article

The area of digital technologies is currently the subject of many cyber threats, the frequency of which is increasing. One of the areas of cyber security is also the creation of models and estimates of the process of cyber threats and their possible financial impacts. However, some studies show that cyber-threat assessment to identify potential financial impacts for organizations is a very challenging process. A relatively large problem here is the detection of scenarios of cyber threats and their expression in time. This paper focuses on the design of an algorithm that can be applied to the field of cyber-threat assessment in order to express the financial impacts. The study is based on an in-depth analysis of the insurance industry. The results obtained in our research show the importance of the time perspective for determining the potential financial impacts of cyber threats for the field of insurance. Full article

We develop a novel quantum algorithm for approximating the price of a discrete floating-strike Asian option based on an underlying valuation tree. The paths of the tree are encoded in bit-representation into a qubit register, where quantum state preparation is used to load the corresponding distribution onto the states. We implement the expectation value of the option pricing formula as a composition of the price probabilities, the payout and an indicator function, mapping their respective values to amplitudes of additional qubits. Thus, the underlying no longer has to be discretized into the same bit values for different times, resulting in smaller quantum circuits. The algorithm may be used with quantum amplitude estimation, enabling a quadratic speed-up over classical Monte Carlo methods. Full article