We study the impact of cyclical systemic risks on banks’ profitability in the euro

area within a panel quantile local projection setting, with the ultimate goal to inform

the calibration of the Countercyclical Capital buffer (CCyB). Compared to previous

studies, we augment our model to control for unobserved bank-specific characteristics

and year-fixed effects and find a lower degree of heterogeneity in the estimated effects

across the conditional distribution of bank returns on assets. We propose a simple

yet intuitive framework to calibrate the CCyB through the cycle, including the so

called "positive neutral" rate. The model suggests a target positive neutral rate for

the euro area ranging from 1.1% to 1.8%. Furthermore, the calibrated CCyB rates

are consistent with the evolution of domestic cyclical systemic risks in the countries

considered. The results further show that the adoption of a positive neutral CCyB

approach allows for an earlier and more gradual build-up of the buffer, but does not

lead to higher CCyB requirements at the peak of the cycle. Importantly, a positive

neutral CCyB strategy would have implied that most euro area countries would have

had a positive CCyB in place at the onset of the COVID-19 pandemic.

Recent technological innovations and demographic trends have fueled an unprecedented surge in private, non-bank credit, driven in part by distributed ledger technology (DLT) and tokenization. In contrast to traditional bank lending which expands the money supply through fractional reserve banking this new mode of nancing reallocates existing funds without creating additional money. Using a stylized Quantity Theory of Money framework, we demonstrate how this shift triggers a critical decoupling between money supply and real output, generating de ationary pressures that could ultimately sti e economic growth. Our ndings raise important questions about the e cacy of conventional monetary policy in an era increasingly dominated by DLT-driven private credit. We conclude by proposing policy options designed to recalibrate money creation mechanisms, ensuring monetary stability and sustained growth.

We analyse the effect of platform fragmentation due to the introduction of fast payment systems (FPS) -- such as FedNow, Pix and UPI -- on the cost of processing payments. Funds sent using FPS offer the recipient greater convenience because they are available for use in real time. However, this could come at the cost of making legacy payment platforms more expensive, owing to the reduction in netting opportunities arising from payment migration to the FPS. We highlight that whether the benefits of providing a choice of payment platforms outweigh the negative externalities imposed by each platform on the other depend on the cost and benefit of immediate access to funds, as well as the settlement protocols of the payment platforms.

Using a proprietary gilt market dataset, this paper identifies how market microstructure frictions affect system-wide market liquidity. Starting from the structure of the repo market, individual dealer constraints and intermediation frictions generate inefficiencies with welfare implications through three independent, albeit related, mechanisms. More broadly, we provide a new perspective on how financial imperfections in the money market, measured by market power and relationship trading, affect the real economy through shocks to individual dealer capacity.

We examine the nuanced relationship between financial inclusion and happiness, emphasizing on the mediating role of financial resilience. Using data across three domains from the Global Findex and the Household Financial and Consumption Survey we investigate the mechanisms through which access to finance affects life satisfaction at various levels. At the country level, our findings indicate that it associates with a 17.1%-21.7% increased levels of reported life satisfaction, with even higher gains (27.8%-32%) among financially vulnerable populations. At the household level, data from 17 European Union countries reveal that access to formal finance enhances life satisfaction by 9.8 to 23.3 percentage points, with credit access playing a significant role. At the individual level, data from the latest Global Findex show that access to credit reduces financial stress by 5.6 to 14 percentage points. Our results highlight that financial resilience, particularly through formal sources of emergency funds, strengthens the positive effects of financial inclusion on reducing financial stress and improving well-being. This study contributes to understanding how financial inclusion mechanisms enhance life satisfaction by examining the critical role of financial resilience.

We assess the impact of Borrower-Based Measures (BBMs) frameworks on household macroeconomic indicators across a panel of European countries from 1995 to 2021. To derive causal estimates of the Average Treatment Effect on the Treated (ATT), we employ the staggered Difference-in-Differences (DiD) estimation method proposed by Callaway and Sant’Anna (2021). Our results indicate that the introduction of BBMs is associated with a significant decline in the growth rate of house prices and house-price-to-income ratios (-7 pp), as well as a reduction in household credit growth (-5 pp). The peak of the effect occurs between 7 and 10 quarters, and reverts back around three years from introduction. We are unable to find effects on home ownership or private credit growth.

This paper investigates the investment behaviors of households in Türkiye, focusing on the impact of demographic factors such as age, gender, education. Utilizing micro data from the 2019 Household Financial Perception and Attitude Survey (HFPAS), we analyze how these factors influence investment choices, risk tolerance, and savings attitudes. The Turkish context, with its diverse population and evolving financial markets, provides a unique setting for this analysis. The analysis reveals that women, divorced and married asset owners are more likely to invest in low-risk assets compared to men, and single individuals who are more inclined towards high-risk investments, which aligns with findings that women are generally more risk-averse. Additionally, the likelihood of investing in low-risk assets increases with home ownership but declines as financial conditions worsen, while higher social and economic status boosts low-risk investment probability. Conversely, individuals with vocational school and university degrees are more inclined towards average and high risk investments, though larger household sizes tend to reduce the likelihood of investing in risky assets possibly due to the increased financial responsibilities and associated risks. Our findings offer valuable insights for policymakers, financial market participants, and academics, highlighting the importance of targeted financial education programs to enhance financial literacy and promote informed decision-making across different demographic segments.

Creating more efficient trading platforms can lower costs but also distort trade patterns. We model brokers’ tokenization decisions—i.e., whether to represent assets as tradable tokens on a shared, programmable ledger. Brokers with heterogeneous market power compete to attract investors and execute their trades intra-broker or over-the-counter. Moreover, coalitions of brokers can invest in creating a tokenized market with faster, cheaper inter-broker settlement. Due to trade diversion incentives, equilibrium coalition structures feature excessive investment or insufficient unification. Public-private cost-sharing or interoperability mandates fail to achieve first best in isolation, but succeed when implemented jointly. These results withstand incorporating an open-access ledger (e.g., a public blockchain).

In this paper, we examine the influence of monetary policy on nineteen clean energy stock indices for the period 2010-2023. We uncover the common factors among various sectors and sub-sectors of clean energy stocks and shed light on how monetary policy shocks propagate across different sectors. Our findings reveal that the panel of indices is influenced mainly by one common factor that accounts for up to 60% of the data, and this factor responds positively to monetary policy shocks. However, the responses of individual clean energy stock indices vary remarkably across different sectors and sub-sectors. Some sectors, such as energy storage, green IT, and wind stocks, respond positively, while others, like smart grid, green building, and transportation stocks, show negative responses to monetary tightening. Understanding this heterogeneity is crucial for effective investment strategies and underscores the need for targeted policy interventions considering the sensitivity of individual sectors.

A long tradition of macroeconomic analysis accords monetary policy only a transient role

in driving real outcomes. At the same time, a large body of evidence highlights the long-lasting impact of boom-bust cycles. We present a model where monetary policy, through

its impact on and reaction to the financial cycle, influences long-term economic trajectories. The core setup is an overlapping generations model featuring bank financing – the creation of bank loans and inside money – which is critical for production and consumption. Monetary policy attains the first-best allocation by sustaining an efficient flow of financing. We then introduce coordination-failure frictions among lenders, which give rise to an endogenous boom-bust cycle in bank financing and an intertemporal policy tradeoff. A forward-looking policymaker optimally leans against excessive risk-taking during the boom, trading off short-term activity with longer-term stability. An inordinate focus on short-term outcomes can lead to ‘monetary policy hysteresis’, where low interest rates increase the vulnerability to financial busts over successive cycles. As a result, low rates can beget lower rates.

The relative restrictiveness of a central bank’s supply of money predicts the raw and risk-adjusted returns of its currency—both next month and at least three years into the future. Ar-chived data, known by currency traders at the time, estimates central bank restrictiveness as a scaling of the residual from out-of-sample panel regressions of M1 on macroeconomic variables tied to domestic and international transaction requirements. Carry’s ability to forecast currency returns is subsumed by the central bank restrictiveness signal, which also forecasts inflation.

We study the effect of financial deepening on carbon dioxide (CO2) emissions intensity in a global sample of 125 economies from 1990 to 2019. Using the local projections (LP) approach, we find that financial deepening leads to a relative increase in CO2 emissions intensity, indicating that financial institutions tend to finance relatively more carbon-intensive investments and consumption. However, we also find that a better institutional environment mitigates this adverse effect of financial deepening: conditional LPs reveal that in countries with more environmental regulations, a stronger rule of law, and a financial system that is relatively more market- than bank-based, financial deepening does not lead to higher CO2 emissions intensity. Furthermore, our results show that stronger rule of law and higher relative financial market development mitigate the effect of financial deepening in countries, which already have lower CO2 emissions intensity. By contrast, tighter environmental regulations are more effective in countries with an initially higher carbon intensity.

This policy paper explores Vietnam’s carbon transition through three distinct scenarios, each shaped by technological advancements, policy frameworks, market dynamics, and societal shifts. The Current Policies/Planned Policy Scenario envisions continued reliance on the existing framework, balancing economic growth through infrastructure investments with challenges like dependency on natural gas and limited financial resources. The Successful Carbon Market Scenario highlights the role of a robust carbon trading market, supported by strong Monitoring, Reporting, and Verification systems, to drive emissions reductions, clean energy innovation, and low-carbon investments, while addressing issues of market integrity and equitable benefits. The Subsidy-Driven Green Transition Scenario, inspired by the U.S. Inflation Reduction Act, focuses on government-led efforts leveraging fiscal spending, tax credits, and incentives to accelerate renewable energy adoption and low-carbon technologies. Together, these scenarios offer diverse pathways and potential macroeconomic impacts for Vietnam’s shift to a low-carbon future.

This paper investigates the effects that science-based targets (SBTs) have on corporate greenhouse gas (GHG) emissions. Using a difference-in-differences (DiD) methodology, we assess whether companies that commit to a Net Zero goal show a decrease in GHG emissions compared to similar companies that declare such goals later. Our results provide limited evidence that organizations with SBT reduce emissions, particularly for Scope 1 and Scope 2 emissions, as the findings lack strong statistical significance. We also examine whether the pace of emission reductions increases as companies approach their target year. Our results indicate that efforts to reduce emissions increase before the announcement, but may stabilize thereafter. These results highlight the challenges of accurately assessing the tangible impact of voluntary corporate commitments on climate goals and underscore the need for comprehensive and clear reporting to prevent misleading claims and promote confidence in climate finance.

The existing evidence for predictability of international bond risk premia raises questions

about whether significant statistical in-sample results can be translated into economic gains.

Moreover, limited information is provided for practical applicability of existing findings. This

study examines a broad set of existing bond risk premia models, extends it to international

markets, and highlights the benefits of using a global forecasting approach for investors. Such an

approach, combining information from multiple international markets, better captures drivers

in international bond risk premia than other approaches, including solely local information. The

out-of-sample findings show how government bond investors can utilize the presented approach

to improve their efficiency frontier, although achievable economic gains are rather limited.

This paper revisits and extends the dividend term structure model of Kragt et al. (2020), which consolidates dividend growth, the risk-free rate, and the risk premium into a unified discount factor. Employing recent data from four major indices (S&P 500, Eurostoxx 50, Nikkei 225, and FTSE 100), I evaluate the model’s performance under three specifications: an unconstrained version, a transversality-constrained version, and an unconstrained version excluding the pandemic period. Although the unconstrained estimates remain economically meaningful for the Eurostoxx 50, Nikkei 225, and FTSE 100, the US market persistently presents a puzzle: unconstrained estimates for the S&P 500 imply explosive long-run dividend projections unless constraints are imposed or pandemic data are removed. While either intervention restores theoretical coherence, it also uncovers a sharp divergence between model-implied and observed valuations, suggesting the omission of critical forces—long-run bubbles and convenience yields—that likely drive equity prices. Moreover, short-run dividend expectations exhibit strong sensitivity to inflationary shocks, whereas medium-term projections hinge on the yield curve and broader monetary policy.

We introduce a method to determine the investor's optimal portfolio size that maximizes the expected out-of-sample utility under parameter uncertainty. This portfolio size trades off between accessing investment opportunities and limiting the number of estimated parameters. Unlike sparse methods such as lasso that exclude assets during the optimization step, our approach fixes the optimal number of assets before computing the portfolio weights, which improves robustness and provides greater flexibility in practical implementations. Empirically, our restricted portfolios outperform their counterparts applied to all available assets. Our methodology renders portfolio theory valuable even when the dataset dimension and sample size are comparable.

This study evaluates the effectiveness of specialised large language models (LLMs) developed for accounting and finance. Empirical analysis demonstrates that these domain-specific models, despite being nearly 50 times smaller, consistently outperform state-of-the-art general-purpose LLMs in return prediction. By pre-training the models on year-specific financial datasets from 2007 to 2023, the study also mitigates look-ahead bias, a common limitation of general-purpose LLMs. The findings highlight the critical importance of addressing look-ahead bias to ensure reliable results. Extensive robustness checks further validate the superior performance of these models.

Since July 2022, European Central Bank (ECB) increased its interest rates for the first time in eleven years to bring inflation back to target. This has huge implication on the credit decision for firms, especially the small and medium enterprises (SME), instrumental in supporting employment, innovation and income. Using ECB's `Survey on Access to Finance of Enterprises' (SAFE) from 2015 to 2023, this paper assesses if the ECB's monetary policy tightening bears any relationship with SME's substituting away from bank credit towards alternative sources of finance. Our results show that contractionary monetary policy shocks were positively associated with the likelihood of SME's substituting away from bank credit. We find this behaviour across SMEs with larger turnover, employee size, age, as well as credit-quality; indicating a much stronger reliance and stickiness to bank credit for relatively smaller, younger, and riskier firms despite increases in the cost of credit following contractionary monetary policy shocks.

How does risk perception in credit markets change after observing a nearby catastrophic event? We combine detailed geospatial data on ex-ante flood risk of German firms with credit register data and show that after a major flood in 2021, loan rates \emph{decrease} for high-flood risk firms that were not directly affected. This negative indirect effect is strongest for banks with a large loan portfolio exposure to the flood. Firms that were affected by earlier, but similar floods do not experience rate reductions. The decrease is also strongest in areas with low climate change belief, while high climate change belief areas experience rate increases. Overall, our evidence points to a novel near-miss effect in lending markets after natural disasters, where a close disaster ``miss'' may be misinterpreted as a reduction in fundamental risk.

The phenomenon of political populism and its financial determinants have proved elusive. We utilise the sudden and uneven change in credit conditions during the COVID-19 pandemic and the unprecedented government credit guarantee programme in France to investigate whether liquidity support to firms affects political preferences. Drawing on credit registry data – which provides the universe of loans and credit lines to firms – we build a postcode-municipality-level dataset and show that government-guaranteed credit reduced the support for the far right but increased it for the incumbent. The underlying economic channel shows that credit guarantees preserved employment, which in turn influenced political preferences. Effects are driven by microenterprises, predominantly self-employed businesses in which the employee-owner-voter is fully aware of the government financial support, i.e., where government support is more salient. This study does not aim to evaluate policies to address the popularity of populist politics.

This paper examines the impact of remittances on credit and provides the first evidence of their effect on delinquency rates. Using a dataset of more than 34 million consumer loans in Mexico and instrumenting for remittances with U.S. unemployment exposure at the municipality level, we uncover two effects overlooked in the literature. First, a “substitution” effect where low-income borrowers, especially women, use remittances to avoid delinquency. Second, a “complementary” effect where remittances increase credit but solely for securing loans with favorable terms. These findings highlight remittances’ role in reducing credit risk and supporting financial development in low- and middle-income economies.

We introduce a new Economic Distress Index (EDI), which incorporates information from all economic sectors as a device for real-time monitoring of financial stability risks. Our approach is based on structural models of credit risk and incorporates market and balance sheet information from which we derive distance-to-defaults as uniform risk indicators across economic sectors, which form the basis of the EDI. Monetary financial institutions are the largest contributors to the EDI over the period from 1999 to 2023. In the post-Global Financial Crisis period, non-bank financial intermediaries emerge as the largest contributors to the EDI, consistent with broader developments that have contributed to the growth of non-bank financial intermediation. Using local projections, we show that the EDI also has significant predictive power for macroeconomic developments that originate primarily from high-stress regimes. Finally, we unpack that volatility is clearly the most important driver of the raw risk indicators, accounting on average for almost 80% of the explained variation.

We lay out a signal-extraction statistical model to estimate a factor summarizing conditions in U.S. credit markets. The factor provides a real-time gauge of “sentiment” in credit markets, above and beyond that attributable to contemporaneous economic conditions. Fluctuations in the credit market sentiment factor have strong asymmetric and nonlinear effects on economic activity, depending not only on the magnitude and sign of the credit market sentiment shock, but also on the current state of the economy. A positive credit market sentiment shock has a stronger and more persistent effect on real economic activity at the onset of a crisis than amidst an expansionary period. Instead, negative sentiment shocks have more persistent effects on activity during expansions than in recessions. Our credit market sentiment measure is a robust predictor of aggregate credit dynamics that outperforms financial indicators in the literature, and correlates positively with forecast errors of quarterly corporate earnings growth 12 months ahead. Finally, our results suggest that tightening monetary policy shocks temporarily deteriorate credit market sentiment, while easing monetary policy shocks do not have an effect on credit sentiment in the short run but end up deteriorating it in the longer run. Our findings about the link between monetary policy shocks and credit sentiment are consistent with monetary easing leading to the build-up of vulnerabilities in credit markets.

The Chicago Plan, proposed by leading economists during the Great Depression, envisaged the separation of banks into money banks with 100% reserve backing for deposits and credit banks financed through non-monetary liabilities. Fisher (1936) claimed four advantages: (1) Reduction of public debt through a debt-to-equity swap. (2) Reduction of private debts as money creation no longer requires debt creation. (3) Elimination of runs on the payment system. (4) Better control of credit-driven business cycles. Using a DSGE model of the US economy, we find strong support for all four claims. Furthermore, steady state output gains approach 17 percent and monetary policy is much more effective in response to every shock. Monetary policy improves welfare by combining a conventional Taylor rule with a countercyclical rule for the interest rate on treasury loans to credit banks.

We propose a functional false discovery rate method that uses multiple informative covariates to evaluate the conditional performance of predictive models while controlling for data snooping. Our method exhibits superior power and is robust to data dependence, estimation errors, and correlated covariates. Applying this method to a large set of currency technical trading rules, we construct a dynamic 30-currency portfolio yielding a Sharpe ratio of about one for roughly 50 years. We find that technical trading profitability decreases with trader computational power and capital account openness, suggesting that such profitability is related to how fast foreign exchange traders can detect specific market patterns.

This article presents a general conceptual and statistical framework for measuring the severity of financial crises on a continuous scale and in real time. It results in a composite index that operationalises the concept of systemic financial stress. The framework nests many existing financial stress and systemic risk indicators as special cases. The Composite Indicator of Systemic Stress (CISS) is introduced as an index design that provides crisis signals which are timely, robust and free of look–ahead bias. The CISS aggregates a representative set of market–specific stress indicators using their time–varying cross–correlations as systemic risk weights. Confirming its nature as a crisis severity measure, empirical analysis shows that the CISS has strong short-term predictive and nowcasting power for economic activity, and that these effects are stronger in bad states of the economy.

This study investigates the dynamic and nonlinear propagation of systemic risk across global environmental and energy sectors using time-varying parameter vector autoregression (TVP-VAR) and quantile vector autoregression (QVAR) models. Employing data from eight indices representing global environmental, ecological, green energy, and brown energy markets, we examine how systemic risk transmission varies under bearish, normal, and bullish market conditions. The results reveal that environmental and ecological markets consistently act as net transmitters of systemic risk, while energy markets, encompassing both green and brown sectors, primarily serve as net receivers. Both climate transition and physical risk significantly influence systemic risk across environmental, ecological, green energy, and brown energy markets, with notable variations contingent on market conditions. Climate transition risk exerts a stronger influence on systemic risk than climate physical risk, particularly during extreme market conditions. The magnitude of climate transition risk is consistently higher across different quantiles of systemic risk. The nonlinear impacts of climate risks on systemic risk emphasize the potential of green swan to exacerbate systemic vulnerabilities of environmental and energy sectors, particularly under conditions of heightened market stress. The study underscores the urgent need for adaptive risk management frameworks to address transition uncertainty in green energy markets and the risk of stranded assets in brown energy markets. By providing actionable insights for policymakers, investors, and risk managers, this study contributes to a deeper understanding of how climate risks shape systemic risk dynamics across environmental and energy sectors, paving the way for more resilient and sustainable financial systems.

This paper examines the impact of climate-driven disasters on commodity prices. Using extensive archival sources including census data and declassified CIA intelligence reports, I develop a global geospatial dataset to identify the locations of key commodity-producing sites at subnational level since the 1970s. By linking these regions to climate disaster events, I find that, over time, production has become increasingly concentrated in high-risk areas. Leveraging this dataset, I analyze how commodity futures respond to climate-driven shocks and uncover significant cross-sectional differences. Specifically, a long-only portfolio of vulnerable commodities yields a significant monthly alpha of 0.90%, whereas that of resilient commodities, albeit still significant, is negative at -0.43%, reflecting a premium paid for protection against climate shocks. Furthermore, I find that vulnerable commodities experience slower recoveries from past shocks.

This study examines how firms adjust their inventory levels in response to climate risks. Using a large dataset, the research finds that firms tend to increase inventory levels as a precaution against climate-related disruptions, especially in countries with organised labour and high product market differentiation. Conversely, firms with strong environmental policies and greater climate risks awareness are more likely to reduce inventory levels. The relationship between climate risk and inventory management has intensified since the Paris Climate Agreement, particularly for firms with high agency costs, labour-intensive operations, or growth/maturity phases. The study provides valuable insights into corporate risk management and contributes to the literature on climate risk and managerial accounting regarding inventory based strategies.

The introduction of the EU Carbon Border Adjustment Mechanism (CBAM) has triggered statistically significant negative stock market responses for firms within the EU. Comparing EU customers that have non-EU suppliers in CBAM-affected industries with their nontreated peers in the control group, we find an extra cumulative abnormal return of up to -1.3 percentage points over our main five-day event window around December 13, 2022. Furthermore, we document substantial anticipatory market responses reflecting updated beliefs about broader climate policy developments going forward. This paper is the first to provide empirical evidence of carbon border tax impacts on firm valuations through international supply chains. Our findings contribute to the understanding of climate policy transmission through international trade networks and inform the debate on stranded assets resulting from environmental regulations.

This paper examines if equity funds’ investment in environment, social, and governance (ESG) affects their capital flows and performance. Utilizing a novel fund-level ESG metric, we find that fund-portfolio-level ESG negatively attracts money inflows, this effect is more pronounced for unsophisticated investors. Also, stocks with high ESG scores tend to underperform, while funds with more ESG investment do not generate inferior performance. It suggests that fund managers process active skills to cover the cost of ESG investment and may find the optimal level of it in their portfolios. Further tests show that the skills of fund managers with more ESG investment may be attributed to their experienced ESG information in stock investment. Our results also provide new insights into the mechanisms behind investing in socially responsible funds.

This study presents a method to analyse social media interactions and their relationship with investor decisions and stock market movements. It explores three questions: (1) the best method for classifying stock-related social media posts, (2) the influence of posts on stock returns and trading volumes, and (3) the impact of posts by opinion leaders. WallstreetBets board in Reddit social media and the GameStop stock provide an ideal laboratory for answering these questions. We analysed 135,000 posts, 5.6 million comments, and 90,000 users from November 2020 to June 2021. Using natural language processing and deep learning, posts and comments were classified by user intention (buy, sell, hold). We found that a BERT-based classifier achieved the highest performance with an F1 score of 80.2%. We then analysed the relationship between social media activity and stock market data, finding that posts are related to trading volumes but not stock returns. However, considering the popularity of posts, there is a significant relationship between social media opinions and stock returns and trading volumes. The study highlights the importance of "influencers": posts from a few influential users significantly impact stock returns and volumes. This research underscores the importance of social media dynamics in financial market behaviour.

Does early-life disaster experience influence the way investors price financial assets? Using household registration information of bankers originating large corporate loans, we find unique evidence that bankers who had experienced higher number of major disaster events charge higher loan spreads than those experienced less or not. This effect holds regardless of bankers’ risk preference in the selection of borrowers. It becomes stronger for borrowing firms with lower profitability and leverage, and those with higher pre-existing lending relationship with individual bankers, and even those with higher climate risk exposure. Our results are not driven by bank, banker characteristics and borrower fundamentals, but suggest that investor conservatism, driven by early disaster experience, shapes assets price.

Using a large dataset of US firms over the period 2007–2022, I find that institutional ownership is associated with fewer biodiversity risk incidents, suggesting a significant role of institutional investors in combating biodiversity loss. The finding is robust to various fixed effects, extends to alternative measures of biodiversity risks, and persists even after adjusting for past incidents. Using plausibly exogenous variation in investors’ monitoring ability indicates causality. The effect is strongest for long-term and domestic institutional investors. Foreign institutions are generally associated with an increased risk of biodiversity, but this relationship is less pronounced when they originate from countries with a strong environmental and biodiversity awareness. Finally, biodiversity risk incidents are positively associated with the implied cost of equity capital, consistent with the notion that investors demand compensation for biodiversity risk exposure.

We study the propagation of firms' Environmental, Social, and Governance (ESG) scores through director networks. Using detailed director-network data and a panel regression approach, we show that a firm's ESG ratings positively respond to those of its peer-director-connected firms. This transmission of ESG ratings through peer director networks differs from that through locality, industry, and interlocks. Firms are likelier to adopt ESG practices from peer-director firms that are financially successful or have influential boards, suggesting that value and values matter for ESG decisions. ESG adoption is also used strategically among competitors. A difference-in-differences approach provides additional evidence of the causal nature of this effect across diverse ESG dimensions.

This study is the first in the literature to examine the relationship between ESG scores and stock market performance of companies listed on the Qatar Stock Exchange (QSE). The paper constructs ESG score by reviewing 776 annual reports published in the period of 2002 to 2022. Ranks were assigned based on the frequency of ESG-friendly words found in the annual reports. Investment simulation strategies were then used to compare the stock returns of value-weighted and ESG-rank weighted portfolios against the QSE benchmark index. The results show that high ESG-ranked portfolios generate superior returns compared to both the value-weighted portfolio and market index both in terms of raw and risk adjusted returns. Additionally, Fama-French-Carhart (FFC) factors were constructed for Qatar, and the estimations revealed that the ESG portfolios produce positive alpha values. Overall, the findings suggest that the Qatar market participants prioritise ESG friendliness. The paper highlights the need to establish a comprehensive database of ESG scores for Qatari companies.

This paper presents a novel investigation by studying the role of supply chain network on firms’ environmental, social, and governance (ESG) scores, as well as on financial performance. Our analysis employs financial, board, ESG and supply chain data, making an unbalanced panel of over 16,000 firm-year observations from 3,028 publicly traded US firms, spanning fiscal years from 2005 to 2021. We use two different supply chain network proxies; namely the number of nodes and the eigenvector centrality, while we use various financial performance measures such as ROA, ROE, ROS, Tobin’s Q and Stock Returns. Building on Resource Dependence Theory, our results indicate that a larger supply chain network has a positive impact on ESG scores, while there is insufficient evidence to support a direct relationship between supply chain networks and financial performance. Interestingly, the impact of the supply chain on financial performance appears to

be indirect, through ESG. Important practical implications are also discussed.

This study examines the effect that text-based sustainability communication,

framed in ESG dimensions, has on investment-based crowdfunding

outcomes. Using an exclusive dataset provided by OneCrowd, operator

of two investment-based crowdfunding platforms in Germany, we apply a

machine learning algorithm (ESGBERT) to assess campaign descriptions.

Our results show that overall ESG scoring does not significantly impact

success metrics. However, communicating environmental aspects is associated

with faster funding success, more investors, and higher total capital

raised, whereas social aspects negatively affect these outcomes. We apply

survival analysis via a Cox proportional hazards model to investigate

time-to-success and use OLS regressions to explore the number of investors

and total capital. By employing a natural language processing approach,

our research provides comprehensive insights into the role of sustainability

communication in crowdfunding. We offer practical implications for

sustainable entrepreneurs and highlight potential directions for improving

the alignment between crowdfunding platforms and sustainable objectives.

Overall, these findings underscore the nuanced effects of ESG.

This study examines the impact of political control on corporate social responsibility (CSR) in China. Grounded in institutional theory and attention-based view, an analysis of 941 publicly listed firms finds that political control enhances CSR performance, particularly through party-building reforms. Specifically, the Party committee strengthens its leadership role by amending corporate charters to include clauses affirming their authority. This effect is more pronounced in larger firms but weaker in heavily polluting industries. The findings remain robust after addressing endogeneity concerns through various methods such as difference-in-differences (DID) estimation, the Heckman two-stage model, and instrumental variable (IV) estimation. By distinguishing political control from traditional political connections, this study contributes to the literature on political governance and corporate sustainability. Additionally, it provides practical insights into China’s distinctive corporate governance model, which differs from conventional Western frameworks.

This paper conducts a historical analysis of political and charitable donations made by publicly listed firms in the UK since 1967, and it explores the factors influencing shareholder votes on political contributions. Our findings indicate that regulatory measures have tightened for political donations while becoming more relaxed for charitable donations. Despite their low but increasing levels, political donations have shown a positive correlation with firm performance. We argue that the introduction of shareholder voting on political donations was unnecessary. This measure led to a significant reduction in political donations, which subsequently became negatively correlated with performance. It also imposed unnecessary costs and drove donations from public firms to individuals and private entities, thus decreasing transparency. Additionally, we find that shareholder voting is influenced not only by political but also by charitable donations. We propose reversing of the 2014 removal of the requirement for the disclosure of charitable donations and recommend implementing shareholder approval for such donations.

Chinese state-owned enterprises (SOEs), within the framework of multi-level principal-agent relationships, are often subject to excessive insider control, unclear managerial responsibilities, and insufficient oversight of managerial decision-making, thereby leading to inefficient investments. We focus on SOEs to examine, for the first time in the existing literature, the impact of accountability on their investment efficiency. We find robust, causal evidence that enhanced accountability improves investment efficiency of SOEs. Moreover, inherent investment stimuli, such as industrial growth potential, corporate growth prospect, financing efficiency, financial reporting quality, corporate governance and managerial experience, complement accountability in boosting investment efficiency. Yet, accountability can substitute external stimuli, such as government support and external monitoring, in driving investment efficiency. By contrast, at the regional level, higher economic growth, greater financial development, more intense anti-corruption and stronger legal environment amplify the positive effect of accountability on investment efficiency. Further analysis reveals that accountability enhances SOE investment efficiency primarily via reducing under-investment and has limited effect in curbing over-investment. Our study provides new insights into the real effect of accountability on investments as well as the interplay between accountability and various investment stimuli in improving investment efficiency, and thereby offers valuable implications for internal governance within firms.

Voting outcomes can differ from underlying preferences due to selection into voting. We document substantial and varying discretionary voting participation of 26% in the US and 7% outside the US. We explain how lower participation of shareholders with popular preferences (freerider effect) relative to that of those with unpopular preferences (underdog effect) can lead to voting outcomes that diverge from the underlying preferred choice. We illustrate these strategic effects in a rational choice model in which the voting participation decision depends on the probability of being pivotal and the costs and benefits of voting. Based on the model, we structurally estimate unobservable shareholder preferences. We show that strategic selection into voting is relevant: 10% of voting outcomes in non-routine proposals in the US and 11% outside the US do not represent the majority of the shareholder base. Our model also shows that reducing the cost of voting may not lead to more representative outcomes. Instead, we document in which circumstances reforms of the cost of voting increase rather than decrease representativeness.

We forecast future 1-day realized volatility (RV) of asset returns in the Chinese stock market by exploring the importance of main volatility features documented in the literature. Beginning with the benchmark Heterogeneous Autoregressive (HAR) model, we further examine major volatility features, including the leverage effect, measurement error, volatility spillover, jumps, and the volatility decomposition between negative and positive returns. Our contribution lies in balancing model specification and estimation to capture essential volatility features and enhance the performance of both in-sample and out-of-sample tests. Ultimately, we quantify the importance of the leverage effect, showcasing its predominance over other volatility features in out-of-sample 1-day volatility forecasting through various estimation methods.

This paper examines the dynamic and directional connectedness among currency option-implied volatilities and how it is shaped by geopolitical risk. Using a novel dataset of over the counter (OTC) option-implied volatilities for 20 developed and emerging market currencies between July 2004 and January 2023, we construct forward-looking currency volatility networks that reflect investor expectations of uncertainty over a 30-day horizon.

To analyse the evolving interdependence in global currency markets, we estimate a Time-Varying Parameter Vector Autoregression (TVP-VAR) model using the Quasi-Bayesian Local Likelihood (QBLL) approach. This methodology enables the construction of dynamic, directed, and horizon-specific adjacency matrices, capturing both aggregate and net directional connectedness. We also apply rolling window variance decompositions to monitor real-time transmission patterns of volatility shocks across currencies.

Our analysis incorporates multiple versions of the Geopolitical Risk (GPR) index, including overall risk (GPR), threats (GPRT), acts (GPRA), and country-specific measures (GPRC), as exogenous drivers in both time-series and panel regressions. The empirical results demonstrate that increases in geopolitical risk, particularly GPR and GPRA, lead to significant rises in total financial connectedness, with effects more pronounced at the daily frequency. Among control variables, the VIX index consistently exhibits a positive and significant relationship with connectedness, while exchange rates display a negative and stabilizing effect. Stock indices and crude oil prices have limited explanatory power in most specifications.

Panel regression analysis confirms that country-specific geopolitical risk (GPRC) has a positive and statistically significant effect on Aggregate Directional Connectedness (AGM) and TO connectedness, indicating that geopolitical shocks amplify both overall and outward spillovers in financial markets. However, the impact of GPRC on FROM connectedness is statistically insignificant.

Overall, this study emphasizes that geopolitical risk is a significant and asymmetric driver of systemic connectedness in currency volatility networks. The findings offer practical insights for policymakers, investors, and regulators by highlighting the role of GPR indices and macroeconomic conditions as early-warning indicators for financial contagion and interconnectedness dynamics.

We develop a signalling game model of investment to examine the implications of ambiguity aversion on corporate equilibrium strategies, investment dynamics, and financing decisions within incomplete markets marked by asymmetric information. Our analysis reveals that volatility ambiguity aversion exerts a comparable yet more pronounced influence than asymmetric information, leading to heightened financing costs, decreased investment probabilities and prompting corporates to adopt non-participation investment decisions. Notably, volatility ambiguity aversion exhibits an amplifier effect, magnifying financing costs, adverse selection costs, and distortion in investment choices under asymmetric information. This heightened ambiguity aversion escalates the likelihood of inefficient separating and pooling equilibria, ultimately resulting in a discernible welfare loss. The findings underscore the substantial impact of ambiguity aversion on strategic decision-making and equilibrium outcomes in the context of investment within environments characterized by information asymmetry and incomplete markets.

The logarithmic prices of financial assets are conventionally assumed to follow a drift-diffusion

process. While the drift term is typically ignored in infill asymptotic theory and its applications, the presence of nonzero drift is an undeniable reality. Our finite-sample theory, along

with extensive simulations, reveals the non-negligible impact of drift on the estimation precision of good and bad volatility. We also demonstrate that this poor estimation of good and

bad volatility leads to significant bias in signed jump estimation. As a solution, we propose

an alternative construction of good volatility, bad volatility, and signed jumps, which shows

a marked improvement in estimation accuracy in the presence of non-negligible drift. When

applying the modified estimators to forecast stock market volatility, we do not find evidence of

an asymmetric impact of good and bad volatility, nor a significant role for signed jumps. We

show that the asymmetric effects of good and bad volatility, as well as the role of signed jumps

reported in existing literature, may be largely attributed to biases in their estimators caused by

nonzero drift.

How is volatility transmitted between options and futures contracts, and can this information transmission be used to generate profitable trading strategies? We examine the bidirectional relationship in volatility between commodity options and futures markets for key commodities to learn about how each market influences the other. To this end, we estimate volatility forecasting models using random forests and we calculate connectedness and spillover measures. We find that futures volatility has a strong but short-lived impact on option volatility, while option volatility has a longer lasting effect on futures volatility, confirming a bidirectional volatility transmission. We further document important net spillovers from options to futures. Moreover, predictive analysis shows that option markets generally lead futures markets in terms of providing information that is relevant for trading strategies. We obtain more accurate futures volatility predictions and trading strategies generate superior economic gains.