In transitional countries, political manipulation of local courts is a common phenomenon, but there is little understanding of its impact on corporate behavior. We utilize a quasi-natural experiment with judicial independence reform, which removes control over personnel and finances of local courts from local governments, to examine how judicial independence influences corporate tax avoidance. We find that firms engage in less tax avoidance following the judicial independence reform. Channel tests indicate that improved judicial independence can undermine political protection and increase litigation risk, leading to lower corporate tax avoidance. Furthermore, the tax avoidance reduction effect of the reform is stronger for firms that are larger in size, engage more in bribery, and are located in cities with higher fiscal dependence and slower economic growth. Overall, this paper suggests that improvements in judicial independence curb corporate tax planning activities.

This study breaks new ground in investigating the volatility transmission between biodiversity stock indices and those from heavy industry sectors such as mining, chemicals, and energy. We estimate volatility transmission indices using data from March 2010 to April 2024, incorporating three biodiversity indices and five indices from heavy manufacturing and energy companies. By employing a dynamic Time-Varying Parameter Vector Autoregressive (TVP-VAR) model, the research identifies which indices serve as exporters or importers of volatility on a global scale, shedding light on the relationship between environmentally focused companies and traditionally pollution-intensive industries. The results indicate that biodiversity measures are significant sources of volatility, particularly impacting the mining and energy sectors. Additionally, the study explores portfolio diversification and asset allocation strategies, assessing the effectiveness of biodiversity assets in hedging. The findings provide important and policy-relevant insights. For ESG-oriented investors, integrating the systemic impact of biodiversity assets into risk models is essential for informed decision-making. Policymakers can enhance the stability and integration of these markets by promoting standardized biodiversity disclosures, incentivizing biodiversity-linked instruments, and implementing regulatory tools that address nature-related financial risks. Collectively, these efforts foster a more resilient financial system and help align biodiversity finance with global sustainability objectives.

We examine how support from the government for accredited hidden champions (accredited HCs) impacts corporate ESG engagement. Accredited HC is a firm that exhibits world-market leadership in its niche market. Based on a sample of Chinese listed firms over 2011–2020 and using a difference-in-differences (DID) method, we show that HCs accredited by the government, on average, exhibit better ESG performance than non-accredited firms. We consider two possible explanations for these findings: (1) a certification effect through which government support reveals an accredited HC firm is of better quality, which leads to better access to bank loans and customer loyalty; and (2) a monitoring effect through which government support brings more external attention and monitoring, such as more analysts following, to the accredited HC firm. Our empirical results support these two effects.

Sovereign default events are usually accompanied by a severe currency crisis and hence, increased sovereign credit risk typically prompts a flight of capital, stimulating greater fluctuations in the foreign exchange (FX) market. Further, if sovereign risk is a priced factor in currency returns, then variability in sovereign risk should generate contemporaneous FX volatility. This study investigates the contemporaneous relationship between sovereign credit risk and FX realized volatility, based on a sample of 30 currencies from January 2008 to December 2022. We examine the impact of sovereign credit risk from two different perspectives, i.e. the change and the volatility of sovereign CDS spreads. We find that FX realized volatility is positively associated with i) the change in sovereign risk and ii) the realized volatility of sovereign risk. We also split sovereign risk into a global component and a local component. Our results show that the developed countries tend to exhibit a greater impact from global sovereign risk, whereas emerging countries are more influenced by local sovereign risk.

We explore the extension of Markowitz's Mean–Variance model into a multi-period model. Such extension allows the assignment of different weights for each time period, allowing the focus on certain periods for obtaining an adequate model of optimal portfolio selection. We show that such a model gives rise to a multivariate constrained optimization problem that involves a function of a system of linear functionals and a quadratic function. We derive the explicit solution for such a model in its most general form, providing us a way to use such a model in practice while avoiding complexities that naturally come from the solution of such an involved multivariate convex problem. We then discuss some of its fundamental features and explore a numerical illustration that shows how one can use the model, in practice, based on a given historical data.

This study investigates how banks’ liability structure affects the bank lending channel of monetary policy transmission. Using a two-period model, we argue that deposit-funded banks are more inclined to provide credit than wholesale-funded banks due to a lower liquidity risk, resulting in more efficient transmission of monetary policy. To identify this causality, our difference-in-differences estimate exploits the introduction of Macro Prudential Assessment (MPA) into China’s bank regulatory system in 2016 as an exogenous shock, in which tougher regulation on wholesale funding was imposed in small banks. We find that the reform consolidates the negative sensitivity of bank loans to policy interest rate, and this effect is stronger for banks with higher risk, greater market power and lower capital adequacy. Further, using the bank-firm loan-level data, we find that a reduction in wholesale funding amplifies the real effects of monetary policy, particularly for private firms with more serious financial constraints. Our findings confirm the active role of banks’ liability structure in the effectiveness of monetary policy transmission.

Can banks, aiming to maximize equity value, be induced through market discipline to adopt the socially optimal risk management? I seek an answer to this question with a model where banks perform risk management via asset diversification by exchanging a portion of their assets with the other bank and where market discipline is provided by bank creditors monitoring the bank by setting the cost of debt according to its diversification decision. While the market discipline induces the equity-maximizing bank to diversify in contrast to no diversification in the absence of market discipline, diversification is always excessive relative to the socially optimal level since diversification increases systemic risk while decreasing idiosyncratic risk. An increase in the financial distress costs from systemic risks makes market discipline less effective in inducing the equity-maximizing bank to choose diversification closer to the socially optimal level. On the other hand, the market discipline becomes more effective when financial distress costs from individual risks rise or when bank leverage ratios fall.

This study investigates the time-varying salience of risk aversion in mainstream green bond and equity markets. Further, we examine the mean and volatility transmissions between a new indicator of climate risk, which is the media climate change concern (MCCC) index, and risk aversion of green markets from both static and dynamic perspectives. We find that risk aversion of green markets follows a non-monotonic random walk with high oscillations. More interestingly, the MCCC index dominates over green market risk aversion in the long-run channels of mean and volatility spillovers, playing a role of information transmitter. Such an information leadership is resilient against time being and turns more pronounced during the COVID-19 pandemic. Our findings unveil the timing of anomalies in risk aversion of the specific green markets, providing a tool through which investor behavior in those markets is monitored. Further, the results provide more insights for market regulators and policy makers as to the dynamic driving influences public concerns on climate change exert on risk preference of green investors, thereby enhancing their understanding of investor trading behavior in the green markets.

This paper examines the impact of Bitcoin futures introduction on the crash risk of spot Bitcoin prices. Using both time-series regression with a time dummy and a difference-in-differences (DID) framework, we find that crash risk, proxied by the negative conditional skewness (NCSKEW) and down-to-up volatility (DUVOL) of 5-minute intraday Bitcoin returns, declines significantly after the launch of Bitcoin futures. Robustness checks confirm that the findings are robust to changes in control variables, control cryptocurrencies, the sampling frequency for high-frequency returns, and an extended post-introduction period. Furthermore, we explore the moderating roles of market liquidity and investor attention. The crash-mitigating effect of Bitcoin futures is significantly more pronounced in periods of low liquidity and limited investor attention, suggesting that futures markets play a stronger role in enhancing information efficiency under such conditions. Highlights This paper examines whether Bitcoin futures introduction increases or decreases Bitcoin price crash risk. The price crash risk of Bitcoin, measured by NCSKEW and DUVOL from high-frequency intraday data, decreases significantly after futures introduction. The main findings are robust to changes in control variables, control cryptocurrencies, the sampling frequency for high-frequency returns, and an extended post-introduction period. The crash-mitigating effect is more pronounced in periods of low liquidity and limited investor attention.

Numerous global agreements and protocols urge industries and businesses to address the challenges of global warming and climate change. Emerging concepts, such as environmental, social, and governance (ESG) scores, have been introduced to measure and encourage corporate responsibility in tackling these issues. This study investigates whether the predictive role of ESG and its pillars on corporate financial performance (CFP) in the fast-moving consumer gods (FMCG) industry varies between mandatory and non-mandatory ESG disclosure using machine learning (ML) techniques. In this context, the study analyzes data from over 174 FMCG firms in Western Europe and North America from 2013 to 2020, corresponding to the second commitment period of the Kyoto Protocol (SCKP). Methodologically, the proposed ML framework, which combines principal component analysis (PCA) with a multi-output gradient boosting model (GBM), demonstrates superior predictive performance compared to traditional models. The findings reveal that ESG improves the predictability of CFP more under mandatory disclosure regimes in Europe than under voluntary disclosure in North America, highlighting the value of regulated ESG transparency. In terms of financial metrics, return on assets (ROA) and operating income (OI) consistently benefit from ESG integration, while return on equity (ROE) appears more leverage-sensitive, reflecting differences in capital structures across regions. Regarding SHAP values, higher ESG and ENV generally enhance model predictions across CFP, while SOC and GOV demonstrate more variable and context-dependent impacts. Overall, the results provide evidence that mandatory ESG disclosure, combined with advanced ML models, strengthens the link between sustainability practices and corporate performance.