We study whether commodity risk is efficiently priced in the cross-section of US equities. We first provide novel evidence on firm-level risk exposure to up to 42 individual commodities. We then construct a mispricing measure as the realized return less the commodity-risk-implied return, and find that it strongly predicts next-month return reversal beyond the traditional short-term reversal. This commodity-risk-mispricing reversal is substantially stronger among small, less analyst- or media-covered stocks, is weakly associated with investor sentiment, and is less prevalent among firms that engage in commodity hedging. In particular, underpricing among these hedgers often remains persistent and even intensifies after the 2004 financialization of commodity markets. This implies that despite the overall decline in mispricing, financialization has likely introduced more costs than benefits for corporate risk management.

Firm characteristics help estimate conditional factor loadings. Larger libraries contain new and overlapping information. We use the cross-sectional distribution of firm characteristics as a ruler to evaluate IPCA loading spaces. With factor counts fixed, expanding from 36 to 153 characteristics can improve in-sample fit while representing momentum and size positions less fully. Reallocating shared exposures changes attribution without changing joint fitted returns. With coefficients fixed, exposure references alter holdings, trading, and subsequent risk at the same characteristic target. Statistical factor models should be evaluated not only by return fit, but also by the exposures they represent and the investment decisions they support.

This study examines the role of financial derivatives in managing operational and financial risks within China's chemical manufacturing sector. While prior research has primarily focused on financial determinants of hedging decisions, we highlight the significant influence of operational metrics—particularly inventory levels and turnover rates—in shaping firms’ engagement in derivatives markets. Drawing from a sample of 289 publicly listed chemical firms from 2016 to 2022, we employ probit regression and K-means clustering to explore how operational and financial factors jointly determine derivatives adoption. Our empirical results reveal that operational metrics have a non-negligible impact on hedging decisions. Specifically, inventory and turnover rates emerge as primary determinants of firms' initiatives, while pre-tax operating profit remains significant from a financial perspective. The moderation analysis of cash flow reveals that financially constrained firms prioritize derivatives for operational risk mitigation, while resource-abundant firms employ them selectively for strategic optimization. Furthermore, our robustness tests, which control for geographical distinctions and the COVID-19 effect, confirm that firm-specific operational characteristics consistently dominate firms' hedging decisions despite regional heterogeneity. Finally, clustering analysis underscores the interplay between operational efficiency and capital robustness, showing that firms exhibiting superior operational efficiency and capital robustness demonstrate higher engagement in derivatives hedging. These findings contribute to the corporate risk management literature by expounding on the primacy of operational considerations in derivatives usage, particularly in asset-intensive industries. The study also provides practical implications for manufacturing firms navigating volatile market conditions, emphasizing that integrating operational and financial strategies is crucial for effective risk management.

In the realm of financial stability, accurate credit default discrimination models are crucial for policy-making and risk management. This paper introduces a robust model that enhances credit default discrimination through a sophisticated integration of a filter-wrapper feature selection strategy, instance selection, and an updated version of majority voting. We present a novel approach that combines individual and ensemble classifiers, rigorously tested on datasets from Chinese listed companies and the German credit market. The results highlight significant improvements over traditional models, offering policymakers and financial institutions a more reliable tool for assessing credit risks. The paper not only demonstrates the effectiveness of our model through extensive comparisons but also discusses its implications for regulatory practices and the potential for adoption in broader financial applications.

We construct a new geospatial measure using high-resolution river vector data from National Geomatics Center of China (NGCC) to study how urban riparian exposure shapes local government debt financing costs. Our base-line results show that cities with higher riparian exposures have significantly lower credit spreads, with a one-standard-deviation increase in riparian exposure reducing credit spreads by approximately 12 basis points. By comparing cities crossed by natural rivers with those intersected by artificial canals, we disentangle the dual role of riparian zones as sources of natural capital benefits (e.g., enhanced transportation capacity) versus climate risks (e.g., flood vulnerability). We find that climate change has amplified the impact of natural disasters, such as floods and droughts, particularly in riparian zones, thus weakening the cost-reducing effect of riparian exposure on bond financing. In contrast, improved water infrastructure and flood-control facilities strengthen the cost-reduction effect. Our findings contribute to the literature on natural capital and government financing, offering valuable implications for public finance and risk management.

This paper develops a network to analyze inter-industry risk spillovers during high and low volatility periods. Our findings indicate that China's Industrials and Consumer Discretionary exhibit the greatest levels of spillovers in both high and low volatility states. Notably, our results demonstrate the "event-driven" character of structural changes to the network during periods of pronounced risk events. At the same time, the economic and financial network exhibits clear "small world" characteristics. Additionally, in the high volatility stage, the inter-industry risk contagion network becomes more complex, featuring greater connectivity and direct contagion paths. Furthermore, concerning the spillover connection between finance and the real sector, the real economy serves as a net exporter of risk. The study's findings can assist government agencies in preventing risk contagion between the financial market and the real economy. The empirical evidence and policy lessons provide valuable insights for effective risk management.

Since the global financial crisis of 2007–2009, preventing financial crises has become one of the most important objectives of regulators and banks. Although previous studies have identified the phenomenon of risk contagion in the banking system, the underlying mechanisms of risk contagion are still unclear. This study delves into the multi-stage contagion mechanism of liquidity risk based on interbank lending linkages and clearing rules and introduces a new index to quantify bank liquidity risk. We find that the contagion of liquidity risk is primarily determined by the network structure of risk exposures between banks in default and is not significantly influenced by the lending relationships of banks that remain solvent. The empirical results suggest that banks with high risk should be prioritized for cash injections to improve system liquidity. These findings offer new insights into financial risk contagion and practical recommendations for regulatory authorities formulating intervention strategies and for banks conducting risk management.

This paper addresses the dynamic tail risk in price-limited financial markets. We propose a novel censored autoregressive conditional Fr´echet model with a fiexible evolution scheme for the time-varying parameters, which allows deciphering the impact of historical information on tail risk from the viewpoint of different risk preferences. The proposed model can well accommodate many important empirical characteristics, such as thick-tailness, extreme risk clustering, and price limits. The empirical analysis of the Chinese stock market reveals the effectiveness of our model in interpreting and predicting time-varying tail behaviors in price-limited equity markets, providing a new tool for financial risk management.

Firms, which trade in today’s open economy often involved multi-currency transactions, will have their stock returns influenced by traded transaction currencies variations. Frequently, these firms also use derivatives for either active (hedging and speculative) or passive (hedging) currency risk management. It is therefore nontrivial to analyse empirically for these firms the relationship between stock return, currency risk exposure, and the motive of their derivatives use. This paper aims to test the relationships, via a two-factor market return model, which is based on the Arbitrage Pricing Theory (Ross, 1976). Descriptive and Inferential statistical tests are implemented on published accounting data (cross sectional and time series) for 69 Australian listed firms excluding non-financial institutions. Statistical test results reveal that there is a weak positive relationship between stock return and currency risk exposure level. The test results also suggest a negative relationship between the currency risk level and the motive (either hedging, speculative or both) of derivatives use. These findings are consistent with the modern finance theory.