Implications For Risk Management Research Articles

Three-Dimensional (3D) Printing: Implications for Risk Assessment and Management in Occupational Settings.

The widespread application of additive manufacturing (AM) technologies, commonly known as three-dimensional (3D) printing, in industrial and home-business sectors, and the expected increase in the number of workers and consumers that use these devices, have raised concerns regarding the possible health implications of 3D printing emissions. To inform the risk assessment and management processes, this review evaluates available data concerning exposure assessment in AM workplaces and possible effects of 3D printing emissions on humans identified through in vivo and in vitro models in order to inform risk assessment and management processes. Peer-reviewed literature was identified in Pubmed, Scopus, and ISI Web of Science databases. The literature demonstrated that a significant fraction of the particles released during 3D printing could be in the ultrafine size range. Depending upon the additive material composition, increased levels of metals and volatile organic compounds could be detected during AM operations, compared with background levels. AM phases, specific job tasks performed, and preventive measures adopted may all affect exposure levels. Regarding possible health effects, printer emissions were preliminary reported to affect the respiratory system of involved workers. The limited number of workplace studies, together with the great variety of AM techniques and additive materials employed, limit generalizability of exposure features. Therefore, greater scientific efforts should be focused at understanding sources, magnitudes, and possible health effects of exposures to develop suitable processes for occupational risk assessment and management of AM technologies.

Tail Risk in Credit Markets: A Dynamic Power-Law Model

I provide a measure of time-varying tail risk in credit markets based on a dynamic power-law model. Credit tail risk is estimated from extreme price fluctuations of credit default swaps (CDS) on government debt. Tail returns are described by a power-law for core and peripheral countries within the Economic and Monetary Union. I find that the tail distributions of returns for short- and long-term debt insurances exhibit the cubic law of returns on different time-scales, which implies the existence of a finite second moment. Sellers of short-term CDS protection bear a higher tail risk of more extreme returns than long-term CDS, especially on shorter time scales. Cross-sectionally, countries in the core region that have a lower probability of credit default imply a greater tail risk than in the peripheral region. This phenomenon can be explained by the significant impact of volatility on the tail distribution. My evidence suggests that credit default swaps of core countries with cheap insurance prices pay off in high-risk states and thus are valuable hedges against extreme events. I show that these findings have important implications for risk management, portfolio allocation, and asset pricing.

Historical Landslide Fatalities in British Columbia, Canada: Trends and Implications for Risk Management

According to a Canadian government database, landslides are the most common type of disaster that occurs in the province of British Columbia. Recently there has been a trend in British Columbia toward using quantitative risk assessments to estimate life-loss risk at landslide hazard sites, and to compare these estimates with risk tolerance thresholds to determine the necessity for, and extent of, risk management measures. These risk estimates are most often calibrated by so-called ‘expert judgment’ because historical landslide fatality data are not readily available. This article addresses this gap by summarizing available historical data to better inform expert judgment. It shows that fatalities caused by landslides in British Columbia are rare (approximately one fatality per year in the last decade) and have decreased with time despite rapid population growth. Approximately half of these fatalities in the last decade are related to debris flows and debris floods that impact houses, whereas the other half are related to rockfalls, debris flows, and debris floods that impact highways. A comparison with other hazard types in the Canadian government’s disaster database suggests that, while not particularly deadly, landslides are still important because of the economic damage and service disruptions they cause. Although the data are specific to British Columbia, the methods for identifying and presenting landslide risk trends could be modified and adopted in other world regions where landslide fatality data are collected and quantitative risk management methods are utilized.

Revenue Models for Digital Servitization: A Value Capture Framework for Designing, Developing, and Scaling Digital Services

Manufacturing companies are currently undergoing a digitalization transformation in which digitally enabled, new, and innovative advanced service offerings are being launched. These so-called “digital services” represent a shift in the business logic of manufacturing firms, from up-front product sales to advanced service contracts. This business model shift has profound implications for cost structures, risk management, and revenue streams, providing manufacturing companies with the key challenge of rethinking how to capture value. Using a multiple case study of 11 companies, the purpose of this article is to enhance knowledge on how to design new revenue models for digital services. Results reveal a revenue model design framework of key phases and activities that carries implications for the emerging literature on digital servitization, as well as the business model innovation literature. The findings reveal a highly customer-centric, iterative, and agile process where close collaboration with key customers during the early stages guides the framing of revenue models for digital services. For practitioners, it provides hands-on advice on how to implement the design, development, and scaling processes for revenue models in the context of new digital services.

Photolytic degradation elevated the toxicity of polylactic acid microplastics to developing zebrafish by triggering mitochondrial dysfunction and apoptosis

Biodegradable plastics (BPs), as alternatives to conventional plastics, are increasingly consumed, but pose potential threats to aquatic ecosystems. In addition, the impact of natural aging on the toxicity of BPs is poorly understood. In this study, the photodegradation of polylactic acid (PLA, a typical BP) microplastics (MPs) under ultraviolet irradiation in water for 90 days was investigated, and the toxicities of virgin and degraded PLA to infantile zebrafish were compared. The results revealed that the size of MPs was reduced from ~25.56 to ~11.22 µm after degradation and nanoparticles were generated with a maximum yield of 7.13%. The formation of abundant oxygen–containing groups (i.e. C˭O and C–O–C) improved the hydrophilia and stability of MPs. Compared with pristine PLA, the efflux and detoxification of degraded PLA mediated by ABC transporters and P450 enzymes were slower, leading to higher bioaccumulation and skeletal development inhibition of zebrafish. Further, oxidative stress–triggered mitochondrial structural damage, depolarization, fission inhibition, and apoptosis were identified as crucial mechanisms underlying the elevated toxicity of PLA after degradation. These findings highlight the importance and necessity of considering natural degradation of BPs and related toxicity, which poses great implications for risk assessment and management of BPs.

Volatility spillovers between strategic commodity futures and stock markets and portfolio implications: Evidence from developed and emerging economies

This study examines the short-, intermediate-, and long-term volatility spillovers between developed (Australia, Canada, France, Germany, Japan, UK, and US) and emerging BRICS (Brazil, Russia, China, India, and South Africa) stock markets and strategic commodity futures markets (oil and gold). Using Baruník and Křehlík's (2018) methodology, we find evidence of time-varying volatility spillovers, which are intensified under major events. Moreover, the total volatility spillovers are higher in the short term than both the intermediate and long terms. A portfolio management analysis reveals that a mixed portfolio (commodity and stock markets) provides a higher level of hedging effectiveness for both emerging and developed markets. In addition, the hedging effectiveness in BRICS markets is more pronounced than in developed markets irrespective of frequencies. The hedging effectiveness is also higher using gold than oil and under the short term rather than both the intermediate and long terms. These results provide important implications for risk management and funds allocation.

Relational Risk and Collective Management: A Pathway to Transformational Risk Management.

Risk tends to be conceptualized at the individual scale, with global risk communication and governance efforts fixated on an individual's knowledge and behavior. While individuals are undoubtedly influenced by those who surround them, such human-human interactions tend to be excluded from empirical and field-based analyses of risk taking. This study diverges from prevailing analyses of risk as an individualized phenomenon, exploring the collective and relational practices that influence risk while fishing from hazardous rocky coasts. The aim is to counter the near-universal tendency to individualize risk in empirical analyses by instead using a mixed-methodology that can quantify and enable consideration of collective responses to risk, in real-time. We demonstrate that both rock fishing practice and many of the high-risk events that emerge while rock fishing are managed collectively. Compared to the tendency to individualize risk, we demonstrate that collective responses to risk are more representative of how risk is experienced and acted upon, with implications for risk management in countless contexts.

What drives risk perceptions? Revisiting public perceptions of food hazards associated with production and consumption

As food technology continues to advance, the potential for new food products to enter the food market grows, attracting considerable media interest. Whilst previous research has explored public perceptions of food-related hazards, much of this took place over 10 years ago. Continued technological developments have yielded new food products, for which there is no extant research on public perceptions. In light of this, there is a pressing need to update and extend research exploring public perceptions of food-related hazards. Using a psychometric approach, a nationally representative UK sample (n = 907) provided ratings of 11 old and new food hazards on a total of 12 risk characteristics (identified from previous research). Principal components analysis identified two main components: ‘dread’ and ‘knowledge’, which explained 80.8% of the variance in perceptions, consistent with past findings. Additives were perceived as the least dreaded and most known of the hazards considered, whereas ractopamine pork, atrazine corn and hormone beef were dreaded the most. 3D printed food and lab-grown meat were perceived as the least known. Our results highlight the importance of knowledge in shaping risk perceptions and have implications for risk management. An understanding of the factors which determine risk perceptions is vital for the development of effective risk management and risk communication strategies.

The Invasion Ecology of Sleeper Populations: Prevalence, Persistence, and Abrupt Shifts

ABSTRACTIt is well established that nonnative species are a key driver of global environmental change, but much less is known about the underlying drivers of nonnative species outbreaks themselves. In the present article, we explore the concept and implications of nonnative sleeper populations in invasion dynamics. Such populations persist at low abundance for years or even decades—a period during which they often go undetected and have negligible impact—until they are triggered by an environmental factor to become highly abundant and disruptive. Population irruptions are commonly misinterpreted as a recent arrival of the nonnative species, but sleeper populations belie a more complex history of inconspicuous occurrence followed by an abrupt shift in abundance and ecological impact. In the present article, we identify mechanisms that can trigger their irruption, and the implications for invasive species risk assessment and management.

Fundamental Pricing Laws and Long Memory Effects in the Day-Ahead Power Market

We apply linear and nonlinear panel models to investigate fundamentals pricing relationships in the Greek day-ahead power market. We link the persistence behaviour and other dynamic properties of power price trajectories to an extended array of fundamental variables reflecting market conditions and the price course of energy commodities in global markets. Assuming that the dynamics of the intraday power pricing curve is adequately specified within a panel modelling framework with long memory and common unobserved stochastic effects, we are able to provide consistent estimates of price responsiveness to the main fundamental drivers and thus avoid biases induced by other popular modelling approaches. We also device a panel variant of the standard smooth-transition regression model to investigate regime switching effects in the price responsiveness mechanism. Empirical results support that Greek day-ahead prices are in a fractional cointegrating relationship with hour-specific covariates (load, marginal generation capacity) as well as price determinants affecting the entire cross-section of hourly contracts (Brent price, emission allowance rights cost). The strength and type of cointegration often changes in a nonlinear way with the hour of power delivery or in response to market conditions. Our study has important implications for price risk management. The results of the nonlinear model demonstrate the ineffectiveness of linear models (either univariate or in a panel context) to accurately estimate the levels of price risk and associate it with changes in market conditions.

The energy transition, Trump energy agenda and COVID-19

Market participants and public policy makers around the world are working hard, attempting to move the world away from the use of carbon-intensive fossil fuels and towards the adoption of viable renewable energy sources. The Trump energy plan supports the production of fossil fuels by reversing this progress. The COVID-19 and the resulting lockdown measures come to worsen the situation by causing a noticeable disruption across the fossil fuel and renewable energy industries. Given these developments, this study seeks to address how and to what extent the Trump energy agenda is rolling back the plans for advancing renewable energy, and how the pandemic is changing the pace of energy transition. For this purpose, we compare the performances of renewable energy and fossil fuels in terms of volatility, efficiency and diversifications benefits for three different periods with varying-uncertainty levels, namely the pre- and the post- Trump’s inauguration periods and the period of rising anxiety over COVID-19. Our results reveal that in the period after the Trump inauguration, coal and oil (renewable energy) have become less (more) volatile but are relatively more (less) responsive to good news. The conditions however became worse with the onslaught of the coronavirus pandemic. COVID-19 adversely affects investment in oil, coal and renewable energy stock markets, though with varying levels. This virus persists to strongly hit fossil fuels demand because of the stringent containment measures. It also poses a huge threat to the timely deployment of renewables and their contributions to the renewable energy progress. These findings have relevant implications for risk management and policy designs.

Circuit Breakers and the COVID-19 Crisis

While designed to curb extraordinary volatility, I find that (stock-level) circuit breakers lead to unnecessary trading halts, which have been prevalent during the recent COVID-19 crisis. More importantly, although created to accommodate the dissemination of fundamental information to market participants, most halts turn out to be illiquidity events. I find that halts further lead to decreases in liquidity and impede short-term price efficiency, with more pronounced results in smaller, less liquid securities, and during the crisis. I also find increased dark trading activity in large stocks, and increased usage of Intermarket Sweep Orders (ISOs) in small stocks, when circuit breakers get activated during the crisis, which further deplete liquidity in the market. Finally, trading halts in (some of) the S&P500 constituents are associated with jumps in the SPY (SPY is an ETF that tracks the S&P500), a finding that has further implications for risk management, as most halts are not informationally driven. I conclude by providing guidance on how to refine the existing market mechanism design to improve market quality and better deal with rare events such as the COVID-19 crisis.

Predicting Enterprise Performance by Network-Based Risk Profiling Approach

Deriving the firms’ risk profile based on specific features has important implications in risk controlling and investment. Recently, much research demonstrates that the firms’ ownership networks substantially impact the firms’ risk profile. In this paper, we propose a framework of risk profiling approach built upon information retrieved from the firm's ownership networks. The method considers the non-linear relationships between firm fundamentals with network structures. To test the performance of the proposed method, we construct a new dataset of Chinese listed firms with their financials and network parameters in the period between 2005 and 2020. We show that the proposed method significantly outperforms traditional ones in predicting a firm's market value changes. Specifically, we first use the conventional linear method, like logistic regression and linear discriminant analysis, as the performance benchmark. Then, the more advanced technique based on information theory like Gradient Boosting is adopted and has shown remarkable performance with at least 85% area under the curve (AUC) compared with the 60% AUC of the traditional linear model. The proposed method has implications in risk management, portfolio management, and corporate finance. As a special implication example in risk management, we demonstrate that a network-based approach can effectively detect duplication of individual names in a unique dataset.

Could 'Lehman Sisters' Reduce Bank Risk-Taking? Cross-Country Evidence

Since the global financial crisis triggered by the collapse of Lehman Brothers, board gender diversity has attracted growing attention among academia and policy makers. The “Lehman Sisters” hypothesis argues for more women representation on bank director board based on the stereotyped gender gap in risk preference that has been widely supported by empirical studies on nonfinancial firms. However, due to the constraint of data unavailability, empirical research on board gender diversity and bank risk-taking is scarce and mostly confined to individual developed markets with inconclusive findings. To our best knowledge, this research is the first cross-country empirical study to examine this issue using a large hand-collected dataset covering 492 commercial banks across 18 developed and 21 developing countries over the period 2007-2016. We find lower bank risk-taking is associated with greater board gender diversity, lending support to the “Lehman Sisters” hypothesis in the international context; however, this effect is significantly weakened in countries with more hostile perception toward working women. Our findings help to reconcile the existing contradictory empirical evidence by highlighting the importance of cultural effect. We also confirm the critical threshold of three female directors to play a significant role in reducing bank risk taking, providing novel international evidence in support of the critical mass theory from banking sector. Our findings have important implications for policy makers and bank risk management.

Cyclical Dependence in Market Neutral Hedge Funds

We examine linear correlation and tail dependence between market neutral hedge funds and the market portfolio conditional on the financial cycle. We document that the low correlation between these funds and the S&P 500 consists of a negative correlation during bear periods and a positive one during bull periods. In contrast, the remaining styles present a positive correlation across cycles. We also find that these funds present tail dependence only during bull periods. We study their implications for market timing and risk management.

Higher-order Comoment Contagion Among G20 Equity Markets During the COVID-19 Pandemic

We study the distribution of equity returns in the G20 equity markets to test for contagion following the first official report of a COVID19 case in China in December 2019 and the subsequent announcement of a global pandemic in March 2020. We find evidence of contagion of Chinese equity market tail risk in early 2020 followed by widespread evidence of contagion across multiple channels from the U.S. to G20 equity markets after the pandemic announcement. Our results suggest that global equity markets may be exposed to unpriced pandemic risk factors with implications for portfolio diversification, risk management and financial stability.

Time-Varying Mixture Copula Models with Copula Selection

Modeling the joint tails of multiple financial time series has many important implications for risk management. Classical models for dependence often encounter a lack of fit in the joint tails, calling for additional flexibility. This paper introduces a new semiparametric time-varying mixture copula model, in which both weights and dependence parameters are deterministic and unspecified functions of time. We propose penalized time-varying mixture copula models with group smoothly clipped absolute deviation penalty functions to do the estimation and copula selection simultaneously. Monte Carlo simulation results suggest that the shrinkage estimation procedure performs well in selecting and estimating both constant and time-varying mixture copula models. Using the proposed model and method, we analyze the evolution of the dependence among four international stock markets, and find substantial changes in the levels and patterns of the dependence, in particular around crisis periods.

A Fitting Return to Fitting Returns: Cryptocurrency Distributions Revisited

This study fits 22 theoretical distribution functions, four of them originally derived, onto 772 cryptocurrency daily returns with goodness-of-fit evaluated using Cramer-von Mises, Anderson-Darling, Kuiper, Kolmogorov-Smirnov, and Chi-squared tests, as well as a harmonic mean p-value synthetic criterion. Most cryptocurrency return distributions can be sufficiently approximated with a Johnson SU function or an asymmetric power function. Johnson SU, asymmetric Student, and asymmetric Laplace distributions have better fit for larger cryptocurrencies, while error, generalised Cauchy, and Hampel (a Gaussian-Cauchy mixture) distributions are more characteristic of smaller cryptocurrencies, with larger coins demonstrating better overall fit. Less than 8% of sample coins and less than 4% of the top quartile by size do not fit into any of the investigated distributions, three largest “misbehaving” cryptocurrencies being Litecoin, Dogecoin, and Decred. Bitcoin and Ethereum are best modelled with error and asymmetric power law distributions, respectively, with asymmetric power law distributions stable through time. More than 30% of sample cryptocurrencies, and 26% from the top quartile, have infinite theoretical variance, severely limiting the diversification potential with such cryptoassets. Three most prominent infinite-variance coins are Bitcoin SV, Tezos, and ZCash. This study has substantial implications for risk management, portfolio management, and cryptocurrency derivative pricing.

THE ROLES OF ECONOMIC POLICY UNCERTAINTY AND THE COVID-19 PANDEMIC IN THE CORRELATION BETWEEN CRYPTOCURRENCY AND STOCK MARKETS

We are the first to explore the effect of economic policy uncertainty (EPU) and the COVID-19 pandemic on the correlation between the cryptocurrency index CRIX and the world stock market portfolio, as well as the hedging properties of CRIX. To this end, we mainly apply the dynamic conditional correlation model with mixed data sampling regressions, a threshold vector autoregressive model and the generalized impulse response function. We demonstrate that the correlation is influenced by the uncertainty stance of the economy and behaves differently in low-, medium- and high-uncertainty periods. Most of the abnormal market relations exist in high levels of EPU or during the COVID-19 period, and the impact of global EPU is greater than that of EPU originating in the United States, Europe, Russia and China. Moreover, the CRIX can serve as a hedge asset against the world stock market. The high (low) level of EPU has a significantly positive (negative) effect on the optimal hedge ratio of CRIX, which increases significantly during the COVID-19 period. Our findings have implications for risk management, portfolio allocations and hedging strategies.

Atmospheric tropical modes are important drivers of Sahelian springtime heatwaves

Heatwaves pose a serious threat to human health worldwide but remain poorly documented over Africa. This study uses mainly the ERA5 dataset to investigate their large-scale drivers over the Sahel region during boreal spring, with a focus on the role of tropical modes of variability including the Madden–Julian Oscillation (MJO) and the equatorial Rossby and Kelvin waves. Heatwaves were defined from daily minimum and maximum temperatures using a methodology that retains only intraseasonal scale events of large spatial extent. The results show that tropical modes have a large influence on the occurrence of Sahelian heatwaves, and, to a lesser extent, on their intensity. Depending on their convective phase, they can either increase or inhibit heatwave occurrence, with the MJO being the most important of the investigated drivers. A certain sensitivity to the geographic location and the diurnal cycle is observed, with nighttime heatwaves more impacted by the modes over the eastern Sahel and daytime heatwaves more affected over the western Sahel. The examination of the physical mechanisms shows that the modulation is made possible through the perturbation of regional circulation. Tropical modes thus exert a control on moisture and the subsequent longwave radiation, as well as on the advection of hot air. A detailed case study of a major event, which took place in April 2003, further supports these findings. Given the potential predictability offered by tropical modes at the intraseasonal scale, this study has key implications for heatwave risk management in the Sahel.