Proxy For Risk Research Articles

Gender and risk – do public listing and private ownership bank matter?

Purpose This study aims to empirically test the influence of board member gender on insolvency risk in a two-tier system. Design/methodology/approach This study considers 91 banks operating in Indonesia from 2009 to 2021. It uses a two-step system generalized moment method to analyze the data. Findings This study reports that appointing women to the board of commissioners and as directors reduces the insolvency risk. Women’s role in reducing risk is more effective in listed and private-owned banks (POB) than in others. Practical implications The presence of women in boardrooms is crucial for improving financial performance and reducing financial distress in banks, particularly listed and POB. The findings of this study are expected to provide insights into board selection and appointment. Originality/value The extant literature on the impact of appointing women to boards on bank risk lacks a consensus because of differences in proxies in measuring gender diversity (GD). This paper conducts scenarios using four GD proxies, namely, dummy, percentage of women, Blau index and Shannon index, and two insolvency risk proxies.

How (Not) to Measure Companies' Climate Transition Risk: A Framework and Categorized Literature Review

ABSTRACTThe climate transition is increasingly acknowledged as a major risk driver for companies, financial institutions, and investors. At the same time, there is widespread confusion about how to best measure climate transition risk, since different measurements lead to significantly different risk profiles. I show empirically that, to date, the two most common proxies for climate transition risk are CO2 and E(SG) score data. I further contribute to the transition risk literature by proposing a comprehensive 10‐category framework, specifically tailored toward assessing climate transition risk proxies. I apply the framework by executing the first category‐led literature review on the quality of both CO2 data and E‐scores as proxies for climate transition risk. I find that both data types are dynamic and strong in terms of granularity as well as usability; but have shortcomings across a multitude of categories: bias, availability, comparability, the backward‐looking nature of the metrics, and transition risk specificity being the most severe issues. I urge scholars to reflect on these shortcomings as they could significantly distort results. As a minimum, scholars should test for the robustness of their results when relying solely on ESG or CO2 data to classify transition risk. Therefore, I propose both within and between transition risk metric robustness tests, which are not yet commonly used in the literature. I close by introducing and discussing alternative proxies for climate transition risk, such as EU taxonomy alignment, sector/technology classifications, or innovative combinations of risk metrics. I argue that scholars should consider these alternatives since they are potentially less biased, more specific to transition risk, comparable, and available. I thereby contribute to a better measurement of companies' transition risk, which is a key prerequisite for accurately managing and correctly pricing climate transition risk.

The Modified Frailty 5-Factor Index Predicts Adverse Outcomes After Ventral Hernia Repair in a National Database.

Ventral hernia repair (VHR) is a common procedure performed on a comorbid patient population at risk for complications, necessitating effective preoperative risk assessment. Previous research suggests that frailty better predicts adverse outcomes compared with historical risk proxies including age. We examined the association between frailty as measured by the 5-factor modified frailty index and postoperative complications following VHR as reported in the National Surgical Quality Improvement Program database. A retrospective review of the National Surgical Quality Improvement Program database from 2015 to 2020 was performed for patients who underwent VHR with the component separation technique. Descriptive analyses were performed on demographics, comorbidities, American Society of Anesthesiologists class, and the modified frailty index score. Multivariable regression was conducted for frailty, age, other comorbidities, and hernia characteristics to determine the relationship to all-cause and surgical site complications, complication severity, complications with Clavien-Dindo score above 3, length of stay, readmission, and reoperation. All analyses were performed using R software. A P value less than 0.05 was considered statistically significant. A total of 14,575 patients were identified. Frailty was a significant predictor of all-cause complications, readmission, reoperation, and increasing length of stay. Increased age was a significant predictor for length of stay and severe systemic complications. Smoking status and American Society of Anesthesiologists class of 4 were associated with all outcomes. Body mass index predicted surgical site complications and reoperation. Frailty can predict many postoperative complications of VHR with component separation technique and is an important element of risk prediction for potential surgical candidates.

Responsible investing: Upside potential and downside protection?

Conventional risk proxies are measured assuming that investors have symmetric risk preferences, with upside and downside deviations from the expectation being equivalently undesirable. Responsible investors, however, have dual financial aims of enhancing upside potential while reducing downside risk by actively incorporating environmental, social, and governance (ESG) aspects into the investment process. We utilize a non-symmetric option pricing research design to test whether responsible investors could live up to their ambitions. We find that those who are simply Principles for Responsible Investment (PRI) members do not deliver the desirable asymmetric performance, while financial firms with highly rated responsible investment processes can actually achieve both aims for their own shareholders: enhancing upside potentials and protecting themselves from downside risks.

Crypto resource management: solving the puzzle of bitcoin mining and climate policy uncertainty

PurposeThe purpose of this paper is to analyse the dynamic and evolving relationship between Bitcoin mining (BTC) and climate policy uncertainty. By using the newly developed U.S. Climate Policy Uncertainty (CPU) indicator by Gavriilidis (2021) as a proxy for global climate-related transition risk, this study aims to explore the complex bidirectional causality between these two critical phenomena in climate-related finance. Further, we explore how economic and market factors influence the cryptocurrency market, focusing on the relationship between CPU and Bitcoin mining.Design/methodology/approachWe employ a linear and non-linear rolling window sub-sample Granger causality approach combined with a probit model to examine the time-varying causalities between Bitcoin mining and the U.S. Climate Policy Uncertainty (CPU) indicator. This method captures asymmetric effects and dynamic interactions that are often missed by linear and static models. It also allows for the endogenous determination of key drivers in the BTC–CPU nexus, ensuring that the results are not influenced by ad-hoc assumptions but are instead grounded in the data’s inherent properties.FindingsThe findings indicate that Bitcoin mining is negatively impacted by climate policy uncertainty during periods of increased environmental concern, while its energy-intensive nature contributes to increasing climate policy uncertainty. In addition to market factors, such as Bitcoin halving, and alternative assets, such as green equity, five main macroeconomic factors influence these relationships: financial instability, economic policy uncertainty, rising oil prices and increasing industrial production. Furthermore, two non-linear dynamics in the relationship between climate policy uncertainty and Bitcoin (CPU-BTC nexus) are identified: the “anticipatory regulatory decline effect”, when miners boost activity ahead of expected regulatory changes, but this increase is unsustainable due to stricter regulations, compliance costs, investor scrutiny and reputational risks linked to high energy use.Originality/valueThis study is the first in the literature to examine the time-varying and asymmetric relationships between Bitcoin mining and climate policy uncertainty, aspects often overlooked by static causality and average-based coefficient models used in previous research. It uncovers two previously unidentified non-linear effects in the BTC-CPU nexus: the “anticipatory regulatory decline effect” and the “mining-driven regulatory surge”, and identifies major market factors macro-determinants of this nexus. The implications are substantial, aiding policymakers in formulating effective regulatory frameworks, helping investors develop more sustainable investment strategies and enabling industry stakeholders to better manage the environmental challenges facing the Bitcoin mining sector.

How Do the Political Connections and the Corporate Governance Mechanisms Affect the Firm Risk of Chinese Dual-listed Companies?

This research aims to assess the influences of political connections and corporate governance (CG) mechanisms on the firm risk of Chinese dual-listed companies (CDLC). CDLCs are Chinese companies that have core businesses in China and simultaneously list their shares in the Hong Kong and the China A-share markets. One hundred CDLCs are chosen for this research from 2003 to 2019. A binary variable of state control firm attribute (SCFA) is created to proxy the political connections to the Chinese government. The standard deviation of daily stock return and the mean of the natural logarithm of squared daily stock return are the two proxies of firm risk. The regression results show that the SCFA negatively influences the firm risk. Thus, the political connections, proxied by the SCFA, mitigate the firm risk. However, the state ownership’s influence on firm risk is insignificant. The regression results are supportive of our argument that SCFA and state ownership are two distinct concepts. Both the board size and independent director ratio insignificantly influence the firm risk. The CEO duality cannot be concluded to positively influences the firm risk and is not a destabilising factor in CDLCs. Evidence of the legal bonding effect is not observed in this research.

Geopolitical Risk and Foreign Direct Investment Inflows: The Moderating Role of Water and Energy Risks

ABSTRACTUsing a panel of 43 economies between 1985 and 2022, this article establishes a link between foreign direct investment (FDI) inflows and geopolitical risk. We distinguish the idiosyncratic component of geopolitical risk from the global geopolitical risk index provided by Caldara and Iacoviello (2022. American Economic Review, 112, 1194–1225.). We then incorporate both global and idiosyncratic risks into a static FDI regression model, controlling for other economic and institutional determinants. Our findings suggest that both global and idiosyncratic geopolitical risks pose significant obstacles to FDI inflows. Additionally, we investigate whether water and energy risks, when interacting with geopolitical risk, influence the behaviour of international investors. To test this hypothesis, we estimate the interaction effect of water stress and energy intensity as proxies for water and energy risks, respectively. Our findings imply that foreign investors perceive high energy intensity and water stress as moderating factors that could motivate host country policymakers to avoid involvement in geopolitical confrontations, thereby supporting peacebuilding to prevent or resolute conflicts.

Biodiversity monitoring with intelligent sensors: An integrated pipeline for mitigating animal-vehicle collisions

Transports of people and goods contribute to the ongoing 6th mass extinction of species. They impact species viability by reducing the availability of suitable habitat, by limiting connectivity between suitable patches, and by increasing direct mortality due to collisions with vehicles. Not only does it represent a threat for some species conservation capabilities, but animal vehicle collisions (AVC) is also a threat for human safety and security in transport and has a massive cost for transport infrastructure (TI) managers and users. Using the opportunities offered by the increasing number of sensors embedded into TI and the development of their digital twins, we developed a framework aiming at managing AVC by mapping the collision risk between trains and ungulates (roe deer and wild boar) thanks to the deployment of a camera trap network. The proposed framework uses population dynamic simulations to identify collision hotspots and assist with the design of sensors deployment. Once sensors are deployed, the data collected, here photos, are processed through deep learning to detect and identify species at the TI vicinity. Then, the processed data are fed to an abundance model able to map species relative abundance around the TI as a proxy of the collision risk. We implement the framework on an actual section of railway in south-western France benefiting from a mitigation and monitoring strategy. The implementation thus highlighted the technical and fundamental requirements to effectively mainstream biodiversity concerns in the TI digital twins. This would contribute to the AVC management in autonomous vehicles thanks to connected TI.

Exploring the Drivers of Bank Risk in State-Owned Commercial Banks

This paper investigates the factors that form bank risks in Bangladeshi state-owned banks. As a representation of bank risk, non-performing loans have been considered the dependent variable for this study. The relevant data have been extracted from six state-owned banks in Bangladesh from 2013 to 2022. The final dataset consists of 60 firm-year observations. The ordinary least square (OLS) regression with robust standard errors model is utilized to find the relationship of bank risks with determinants like asset size, efficiency, liquidity, profitability, leverage ratio, capital adequacy ratio, and loan to asset ratio. The findings say that bank size and loan-to-asset ratio have a highly significant positive relationship with bank risk, whereas profitability, capital adequacy ratio, and bank efficiency have a significant negative association with bank risk. On the other hand, leverage and liquidity are negatively and positively correlated with no statistical significance. To check the study's robustness, another proxy of bank risk, loan loss provision has been used and the results are almost similar. State-owned banks operate in every economy with a different mindset than the general commercial banks. Hence, policymakers should take necessary initiatives based on this study to bring down the high bank risks in state-owned banks in Bangladesh.

Identifying Different Immunoresistance Risk Profiles Among Experienced Aesthetic Botulinum Neurotoxin A Recipients: A Latent Class Analysis.

Immunoresistance to botulinum neurotoxin A (BoNT-A) due to neutralizing antibodies (NAbs) can lead to partial or complete secondary nonresponse (SNR), potentially limiting individuals' aesthetic and/or medical therapeutic options in the short and/or long term. Understanding factors directly or indirectly influencing BoNT-A immunoresistance risk is crucial. This analysis explored patterns of latent risk factors (biological and behavioral) that may influence the risk of developing BoNT-A immunoresistance among experienced aesthetic BoNT-A recipients. Latent class analysis (LCA) was applied to survey data from 363 experienced BoNT-A recipients from six Asia-Pacific countries to identify distinct subgroups based on their patterns of risk factor or risk proxy variables. The five risk proxy variables used for modeling capture information on BoNT-A treatments (treatment indications/locations as proxies for dose), symptoms of declining efficacy, number of aesthetic treatments over the past 3 years, and clinic and BoNT-A formulation switching behaviors. These represent established risk factors and treatment-seeking behaviors suggested to influence immunoresistance risk. LCA identified 3 distinct profiles of individuals, which we described based on the observed patterns of risk proxies as: "lower-risk" (55%), "moderate-risk" (39%), and "higher-risk" (6%). Individuals in the "higher-risk" profile reported higher BoNT-A exposure, more symptoms of declining efficacy, and distinct patterns of knowledge and attitudes toward BoNT-A immunoresistance that could account for their treatment-seeking behaviors. This study suggests that individual behaviors (the "human factor") have a notable influence on BoNT-A immunoresistance risk. Gaining deeper insights into these factors could support more targeted and effective interventions to mitigate risk.

Modelling the effectiveness of antiviral treatment strategies to prevent household transmission of acute respiratory viruses.

Households are a major driver of transmission of acute respiratory viruses, such as SARS-CoV-2 or Influenza. Until now antiviral treatments have mostly been used as a curative treatment in symptomatic individuals. During an outbreak, more aggressive strategies involving pre- or post-exposure prophylaxis (PrEP or PEP) could be employed to further reduce the risk of severe disease but also prevent transmission to household contacts. In order to understand the effectiveness of such strategies and the factors that may modulate them, we developed a multi-scale model that follows the infection at both the individual-level (viral dynamics) and the population-level (transmission dynamics) in households. Using a simulation study we explored different antiviral treatment strategies, evaluating their effectiveness on reducing the transmission risk and the virological burden in households for a range of virus characteristics (e.g., secondary attack rate-SAR, or time to peak viral load). We found that when the index case can be identified and treated before symptom onset, both transmission and virological burden are reduced by > 75% for most SAR values and time to peak viral load, with minimal benefit to treat additionally household contacts. While treatment initiated after index symptom onset does not reduce the risk of transmission, it can still reduce the virological burden in the household, a proxy for severe disease and subsequent transmission risk outside the household. In that case optimal strategies involve treatment of both index case and household contacts as PEP, with efficacy > 50% when peak viral load occurs after symptom onset, and 30-50% otherwise. In all the considered cases, antiviral treatment strategies were optimal for SAR ranging 20-60%, and for larger household sizes. This study highlights the opportunity of antiviral drug-based interventions in households during an outbreak to minimize viral transmission and disease burden.

Quantitative modelling for dengue and Aedes mosquitoes in Africa: A systematic review of current approaches and future directions for Early Warning System development.

The rapid spread and growing number of dengue cases worldwide, alongside the absence of comprehensive vaccines and medications, highlights the critical need for robust tools to monitor, prevent, and control the disease. This review aims to provide an updated overview of important covariates and quantitative modelling techniques used to predict or forecast dengue and/or its vector Aedes mosquitoes in Africa. A systematic search was conducted across multiple databases, including PubMed, EMBASE, EBSCOhost, and Scopus, restricted to studies conducted in Africa and published in English. Data management and extraction process followed the 'Preferred Reporting Items for Systematic Reviews and Meta-Analyses' (PRISMA) framework. The review identified 30 studies, with the majority (two-thirds) focused on models for predicting Aedes mosquito populations dynamics as a proxy for dengue risk. The remainder of the studies utilized human dengue cases, incidence or prevalence data as an outcome. Input data for mosquito and dengue risk models were mainly obtained from entomological studies and cross-sectional surveys, respectively. More than half of the studies (56.7%) incorporated climatic factors, such as rainfall, humidity, and temperature, alongside environmental, demographic, socio-economic, and larval/pupal abundance factors as covariates in their models. Regarding quantitative modelling techniques, traditional statistical regression methods like logistic and linear regression were preferred (60.0%), followed by machine learning models (16.7%) and mixed effects models (13.3%). Notably, only 36.7% of the models disclosed variable selection techniques, and a mere 20.0% conducted model validation, highlighting a significant gap in reporting methodology and assessing model performance. Overall, this review provides a comprehensive overview of potential covariates and methodological approaches currently applied in the African context for modelling dengue and/or its vector, Aedes mosquito. It also underscores the gaps and challenges posed by limited surveillance data availability, which hinder the development of predictive models to be used as early warning systems in Africa.

Quantile volatility connectedness among themes and sectors: Novel evidence from China

Against the backdrop of increasing interest in factor investing, this paper explores volatility connectedness among theme factors and sector indices in the Chinese stock market using the Diebold-Yilmaz approach with quantile factor VAR. Our static analysis reveals significant similarities at extreme quantiles, contrasting with the conditional median. We find that higher connectedness measures at extreme quantiles correspond to improved performance of portfolios based on sectors and themes. Additionally, dynamic analysis indicates a strong link between total connectedness and major risk events in China. Moreover, variations in connectedness between the right and left tails serve as a market-level risk proxy, significantly influencing the performance of both themes and sectors. These findings underscore the importance of understanding volatility connectedness for devising effective investment strategies and enhancing risk management practices in the Chinese stock market.

Bidirectional Mendelian Randomization Analysis of Genetic Proxies of Plasma Fatty Acids and Pre-Eclampsia Risk.

Previous studies have reported associations between fatty acids and the risk of pre-eclampsia. However, the causality of these associations remains uncertain. This study postulates a causal relationship between specific plasma fatty acids and pre-eclampsia or other maternal hypertensive disorders (PE-HTPs). To test this hypothesis, two-sample bidirectional Mendelian randomization (MR) analyses were employed to determine the causality effects. Single-nucleotide polymorphisms associated with PE-HTPs and fatty acids were obtained from a genome-wide association study (GWAS) of European ancestry. Bidirectional MR analyses were conducted using methods such as inverse variance weighted, MR-Egger, weighted median, simple mode, and weighted mode. Sensitivity analyses, including tests for heterogeneity, horizontal pleiotropy, and co-localization, were conducted to assess the robustness of MR results. The analyses revealed causal relationships between PE-HTPs and several fatty acids, including monounsaturated fatty acid (MUFA), omega-6 fatty acid (n-6 FA), linoleic acid (LA), docosahexaenoic acid (DHA), and the PUFA/MUFA ratio. Genetically predicted higher risk of PE-HTPs was significantly associated with lower plasma n-6 FA (OR = 0.96, 95% CI: 0.93-0.99), particularly LA (OR = 0.95, 95% CI: 0.92-0.98). Conversely, increased DHA (OR = 0.86, 95% CI: 0.78-0.96) and a higher PUFA/MUFA ratio (OR = 0.86, 95% CI: 0.76-0.98) were associated with a reduced risk of PE-HTPs. Elevated MUFA levels (OR = 1.12, 95% CI: 1.00-1.25) were related to an increased risk. This study provides robust genetic evidence supporting bidirectional causal relationships between PE-HTPs and specific plasma fatty acids, underscoring the critical role of fatty acid metabolism in maternal hypertensive disorders.

Development of a Flexible Produce Supply Chain Food Safety Risk Model: Comparing Tradeoffs Between Improved Process Controls and Additional Product Testing for Leafy Greens as a Test Case

The produce industry needs a tool to evaluate food safety interventions and prioritize investments and future research. A model was developed in R for a generic produce supply chain and made accessible via Shiny. Microbial contamination events, increases, reductions, and testing can be modeled. The output for each lot was the risk of one, 300-gram sample testing positive, described by two industry-relevant risk metrics, the overall risk of a positive test (proxy for recall risk) and the number of lots with the highest risk (>1 in 10 chance) of testing positive (proxy for public health risk). A leafy green supply chain contaminated with Shiga-toxin-producing Escherichia coli was modeled with a mean of 1 pathogen cell per pound (µ = 1 CFU/lb or −2.65 Log(CFU/g)) under high (σ = 0.8 Log(CFU/g)) and low (σ = 0.2 Log(CFU/g)) variability. Baseline risk of a positive test in the low-variability scenario (1 in 20,000) was lower than for high-variability (1 in 4,500), showing rare high-level contamination drives risk. To evaluate tradeoffs, we modeled two well-studied, frequently used interventions: additional product testing (8 of 375-gram tests/lot) and improved process controls (additional −0.87 ± 0.32 Log(CFU/g) reduction). Improved process controls better-reduced recall risk (to 1 in 115,000 and 1 in 26,000 for low- and high-variability, respectively), compared to additional product testing (to 1 in 21,000 and 1 in 11,000 for low- and high-variability, respectively). For low variability contamination, no highest-risk lots existed. Under high variability contamination, both interventions removed all highest-risk lots (about 0.05% of total). Yet, additional product testing rejected more lower-risk lots (about 1% of total), suggesting meaningful food waste tradeoffs. This model evaluates tradeoffs between interventions using industry-relevant risk metrics to support decision-making and can be adapted to assess other commodities, process stages, and less-studied interventions.

Are metabolically healthy obese individuals really a benign cardiovascular group? An analysis of 162,590 participants with measures of arterial stiffness in UK BIOBANK

Abstract Background There is conflicting evidence as to whether so called metabolically healthy but overweight and obese individuals are at increased cardiovascular risk or mortality. Purpose We assessed whether metabolic health correlates with vascular health among these individuals using severity of arterial stiffness as a proxy for vascular health and cardiovascular risk. Methods Participants in UK Biobank with body-mass index (BMI) and arterial stiffness index (ASI) recorded at initial assessment between 2006-2010, and free from stroke and myocardial infarction and not underweight (BMI<18.5kg/m2) were included. Participants were stratified by BMI (normal, overweight, obese) and presence of metabolic abnormalities (hypertension, diabetes, dyslipidaemia). The primary outcome was the severity of ASI analysed using multivariate-adjusted linear regression. Results Of 162,590 participants, 55.0% were female, mean age was 57 years (SD 8), 42.5% were overweight and 24.4% obese. Within the normal BMI strata, 50.7% had ≥1 metabolic abnormality. Compared to individuals with normal BMI and no metabolic abnormalities (ref group), increased weight or metabolic abnormalities were similarly associated with higher ASI; adjusted β-coefficient [β] 0.35, 95%CI 0.30 to 0.40 for participants with normal BMI and ≥1 metabolic abnormality; β 0.32, 95%CI 0.26 to 0.37 for overweight participants without metabolic abnormalities. Obese individuals with no metabolic abnormalities had higher ASI (β 0.65; 95%CI 0.57 to 0.74) but was lower than overweight participants with metabolic abnormalities (β 0.80; 95%CI 0.75 to 0.84). Obese participants with metabolic abnormalities had the highest ASI (β 1.07; 95%CI 1.02 to 1.12) among all six metabolic combinations, p<0.001 for each vs ref group. Sensitivity analysis suggested higher ASI with increasing number of metabolic abnormalities within BMI categories. Conclusion Excess weight and metabolic abnormalities, are independently associated with adverse measures of vascular health to a similar degree, suggesting that the so called metabolically healthy obese are not a benign group. Primary prevention strategies should be considered in overweight, or obese individuals even if metabolic abnormalities are absent.

Early Desertification Risk in Advanced Economies: Summarizing Past, Present and Future Trends in Italy

Being located in the middle of Southern Europe, and thus likely representing a particularly dynamic member of Mediterranean Europe, Italy has experienced a sudden increase in early desertification risk because of multiple factors of change. Long-term research initiatives have provided relatively well-known examples of the continuous assessment of the desertification risk carried out via multiple exercises from different academic and practitioner stakeholders, frequently using the Environmentally Sensitive Area Index (ESAI). This composite index based on a large number of elementary variables and individual indicators—spanning from the climate to soil quality and from vegetation cover to land-use intensity—facilitated the comprehensive, long-term monitoring of the early desertification risk at disaggregated spatial scales, being of some relevance for policy implementation. The present study summarizes the main evidence of environmental monitoring in Italy by analyzing a relatively long time series of ESAI scores using administrative boundaries for a better representation of the biophysical and socioeconomic trends of interest for early desertification monitoring. The descriptive analysis of the ESAI scores offers a refined representation of economic spaces in the country during past (1960–2010 on a decadal basis), present (2020), and future (2030, exploring four different scenarios, S1–S4) times. Taken as a proxy of the early desertification risk in advanced economies, the ESAI scores increased over time as a result of worse climate regimes (namely, drier and warmer conditions), landscape change, and rising human pressure that exacerbated related processes, such as soil erosion, salinization, compaction, sealing, water scarcity, wildfires, and overgrazing.

Does litigation risk matter for the choice between bank debt and public debt?

We examine the impact of liberal judge ideology, as an exogenous proxy for litigation risk, on firms' choice of debt structure. In line with the substitution of governance mechanisms hypothesis, we find that U.S. firms headquartered in circuits dominated by liberal judges rely less on bank debt financing. We also show that the substitution away from bank borrowing arising from liberal judge ideology leads to a greater reliance on other financing alternatives, such as public debt and equity financing. Additional analyses indicate that the effect of liberal judge ideology is amplified for firms operating in competitive markets, firms facing tighter financial constraints, and firms with more growth opportunities. The governance substitution effect is, however, less pronounced for firms with higher institutional ownership. Overall, our findings suggest that, by exacerbating litigation risk, liberal judge ideology induces firms to trade-off creditor governance stemming from bank debt with governance by litigation, thus decreasing their reliance on bank debt in favor of alternative financing sources with less strict constraints and lower monitoring of managerial behavior.

Geopolitical risk and energy price crash risk

This paper explores the link between geopolitical risks and energy prices crash risk. Studying energy price crashes is important given the sharp fall in oil prices in 2008 and 2014. The analysis focuses on three energy markets: natural gas, oil, and coal, while it employs two measures: the negative coefficient of skewness and the down-to-up volatility, to construct proxies for crash risks. The period of examination is January 2000 to December 2023, whereas that for coal is January 2010 to December 2023. The study employs a modified version of the smooth transition autoregressive model. The results show that, within the modelling framework, coal and oil crash risks are driven by the cyclical behavior of geopolitical acts, whereas natural gas crash risks by geopolitical threats. Causality tests confirm the prediction that geopolitical tensions cause crash risks in energy markets. The results also confirm that the “Economic Activity Channel” is only valid for energy markets driven by geopolitical threats. Energy market regulators should be concerned about crash risks, given that the energy supply shows cyclical boom and bust cycles in prices and production. Crash risks could also potentially cause a fall in investments required to enhance energy efficiency.

Funding Illiquidity Implied by S&P 500 Derivatives

Based on the typical positions of S&P 500 option market makers, we derive a funding illiquidity measure from quoted prices of S&P 500 derivatives. Our measure significantly affects the returns of leveraged managed portfolios; hedge funds with negative exposure to changes in funding illiquidity earn high returns in normal times and low returns in crisis periods when funding liquidity deteriorates. The results are not driven by existing measures of funding illiquidity, market illiquidity, and proxies for tail risk. Our funding illiquidity measure also affects leveraged closed-end mutual funds and, to an extent, asset classes where leveraged investors are marginal investors.