Market Risk Research Articles

Impact of climate risk on energy market risk spillover: Evidence from dynamic heterogeneous network analysis

Using meteorological monitoring data from eight climate disasters across the globe, the Climate Policy Uncertainty (CPU) Index, and the financial data of eight energy markets between 2010 and 2020, this study employs the fixed base extreme difference entropy method to construct a Climate Risk Index (CRI) and investigates the impact of climate risk on the risk spillover among energy markets based on dynamic heterogeneous network analysis. The results confirm that the CRI fluctuated periodically and exhibited an upward trend during the sample period. In terms of risk spillover, the oil, geothermal, and fuel cell markets act as risk receivers, whereas the gas, wind, and bioenergy markets functions as risk emitters. Climate risk significantly increases the network complexity between energy markets and the risk spillover of gas, solar, water, and wind markets but decreases the risk spillover of oil, bioenergy, fuel cells, and geothermal markets. Additionally, the fossil fuel market is more sensitive to climate risk than is the renewable energy market.

Heterogeneous impact of economic and political uncertainty on green bond volatility: Evidence from the MRS-GARCH-MIDAS-Skewed T model

Green bonds attract increasing attention as a new eco-friendly investment product. We explore the heterogeneous impact of low-frequency economic and political uncertainty across high or low uncertainty states on green bond volatility in order to accurately analyze the green bond market risk. To this end, we propose a new Markov regime switching GARCH-MIDAS-Skewed T model, in which the regime switching behavior occurs on the low-frequency long-term volatility. An effective filtering estimation method is put forth by introducing the likelihood of the low-frequency sub-sample set. The evidence supports that there are significant time-varying and state-dependent impacts from uncertainty shocks on the volatility of green bonds, including monetary policy, inflation, and crude oil prices as well as global economic policy and political environment. In addition, we find the counter-cyclical behavior of green bond volatility, which increases in the period of economic recession or financial turbulence with expanding uncertainty. Improving the hedging ability of green bonds against uncertainty risks effectively contributes to low-carbon economic development.

Asymmetric spot‐futures prices adjustments in Quebec grain markets

AbstractAsymmetric price transmission has been the subject of many studies in agricultural economics, but few has been said on Quebec grain market. This study uses threshold cointegration models to examine the dynamic relationship between Quebec spot and futures prices. Estimation results with daily, weekly, and monthly data from September 1994 to May 2022 are three‐fold. First, the main point to keep from cointegration tests is that futures prices and Quebec local grain prices are integrated but estimations with daily data are consistent to highlight a nonlinear long‐run relationship. Second, results indicate overall positive asymmetric for daily and weekly prices transmissions from futures to spot market. Third, the positive asymmetric prices transmissions shed light on the oligopoly structure of Quebec grain market and inefficiencies in the pricing mechanism which favor local grain sellers. Results with different data frequencies show that aggregated monthly prices data compared to more disaggregated data such as daily and weekly prices may lead to different results. Results imply that policymakers and market stakeholders could facilitate increased grain market competition. Grain users like Quebec hog producers need to better understand additional tools for their management of price risk like futures market and real time prices movement monitoring.

Research on the risk effects of accounting information disclosure

Drawing upon accounting-based asset pricing model and insights from theories of information asymmetry and cognitive psychology, this study investigates the risk implications of accounting information disclosure from dual perspectives: firm-specific risk and market risk. Our findings reveal a positive association between earnings management and both firm-specific and market risk effects of accounting information, underscoring the presence of post-disclosure risk effects. Heterogeneity analysis further uncovers the moderating roles of factors such as accounting firm size, company tenure, and regional economic growth in firm-specific risk effects, while corporate governance structures influence market risk effects. This study enriches the theoretical literature on accounting information and risk effects.

Electricity Price Analysis for Renewable Energy Dominated Electricity Markets under Extreme Scenarios

The COVID-19 pandemic, the energy crisis, the outbreak of the Russia-Ukraine war, and the rapid development of renewable energy electricity generation in European countries brought risks to electricity market operations, which are shown as high fluctuations in electricity prices. In order to explore the price risks of electricity markets in European countries, this paper studies the annual average electricity spot price and their fluctuations in each country, and compares them based on the renewable energy percentage of the country and inter-country power exchanges. It was found that under periods with extreme energy supply/consumption situations, a high proportion of hydropower generation may aggravate the price fluctuation of the electricity market. Meanwhile, electricity systems with high wind and solar generations are highly rely on natural gas, and hence be affected by gas/oil markets. Finally, cross-border electricity transactions might trigger electricity price fluctuations under extreme circumstances.

Cryptocurrency: A new player or a new crisis in financial markets? —— Evolutionary analysis of association and risk spillover based on network science

In recent years, cryptocurrency has attracted widespread research attention. Against the backdrop of financial globalization, the high volatility of cryptocurrency prices may pose a threat to the stability of the entire financial system. To explore the potential association and transmission of financial risks between cryptocurrency and traditional financial assets, this paper constructs a complex network using the Granger causality method and the DY spillover method. Based on data from 24 categories of assets or index returns from different markets, networks of association and risk spillover between cryptocurrency market and traditional financial markets are established under different time periods, the study covers the period from January 2015 to November 2023. Analyzing the mutual influence between cryptocurrency and traditional financial markets from the perspective of complex networks, and evaluating the market positioning, risk characteristics, and investment potential of cryptocurrency. Research findings indicate that cryptocurrency possesses not only monetary characteristics but also functions as an investment tool, significantly impacting the stability of traditional financial markets. Furthermore, different types of cryptocurrencies vary in market functions, risk characteristics, and investment potentials. Future research could further explore the nonlinear and dynamic associations between cryptocurrency and traditional financial markets, as well as the micro mechanisms and behavioral factors within cryptocurrency markets.

LEVERAGING ARTIFICIAL INTELLIGENCE (AI) IN PUBLIC SECTOR FINANCIAL RISK MANAGEMENT: INNOVATIONS, CHALLENGES, AND FUTURE DIRECTIONS

This paper examines the transformative role of Artificial Intelligence (AI) in enhancing financial risk management practices within the public sector in different context. With the increasing complexity of financial markets and the growing demand for transparency and efficiency in public finance management, traditional methods of risk identification and mitigation are proving to be inadequate. AI and machine learning technologies offer a promising alternative, with their ability to process vast amounts of data, recognize patterns, and predict future trends with a high degree of accuracy. We delve into the application of predictive models for identifying various financial risks, including credit, market, liquidity, and operational risks, and explore how AI algorithms can optimize decision-making processes through scenario analysis, resource allocation, and policy impact analysis. The paper also addresses the challenges associated with integrating AI into public financial management systems, such as data privacy concerns, the need for skilled personnel, and the importance of developing regulatory frameworks that ensure ethical use and transparency. By presenting case studies and real-world applications, this study highlights both the successes and failures of AI implementations in the public sector, providing valuable lessons learned and best practices. As AI technologies continue to evolve, this paper underscores the need for ongoing research and adaptation to fully realize their potential in making public sector financial risk management more proactive, efficient, and resilient.

Chinese Family Farm Business Risk Assessment Using a Hierarchical Hesitant Fuzzy Linguistic Model

Chinese family farms are continuously expanding; they are also facing various business risks that lead to a shorter lifespan. This paper constructed a family farm business risk assessment model that combined a hesitant fuzzy linguistic term sets (HFLTS) model with a hesitant fuzzy weighted average (HFWA) operator. On the basis of the factor analysis, this study built a family farm indicator system that included the natural, technical, market, policy, society, and management risk. The HFLTS was used for the assessment of weights in pairwise comparison matrices, and the HFWA operator was used as an aggregation operator to calculate the business risk score of family farms. For our case study, a method comparison analysis was also performed to check the validity of the results obtained by our risk assessment model.

A hybrid model for predicting the carbon price in Beijing: a pilot low-carbon city in China

Beijing is one of the earliest pilot low-carbon cities in China. It was one of the first cities in China to establish a pilot carbon market to achieve this goal. As an emerging market, China’s carbon pricing mechanism is not yet complete. In this context, it is crucial for market managers and companies to predict carbon prices. This study uses a Prophet-EEMD-LSTM model to predict the carbon price in the Beijing carbon market, which significantly improves prediction performance. The advantage of this hybrid model is that it considers the particularities of carbon prices including trends, cyclical changes, and volatility. Considering that the carbon market has multiple complex characteristics, the carbon price is decomposed into multiple simple sequences using the Prophet and EEMD models. These simple sequences were predicted using an LSTM model. The hybrid model outperformed both econometric and single-machine learning models in terms of carbon price prediction. Based on the findings of this study, market managers and companies can take appropriate measures to prevent carbon price risks. These findings are conducive to the smooth operation of the carbon market, thereby providing sustainable support and guidance for the development of low-carbon cities.

Price Risk Exposure of Small Participants in Liberalized Multi-National Power Markets: A Case Study on the Belize–Mexico Interconnection

This study examined the price risk of the Belize–Mexico interconnection using ARMA-ARCH models to assess electricity pricing volatility and autoregression to determine the influence of conditional volatilities and import consumption. The findings revealed that the volatility of both markets, especially spot price, showed rare high-impact shocks and prolonged periods of volatile clusters. Volatile pricing tendencies, and forward pricing to a lesser extent, had positive effects on premiums. Premiums were largely dependent on pricing properties and system conditions of the supplier’s (Mexico) side, with negligible influence from the buyer’s (Belize) side. However, significant effects were found during certain hours with the buyer’s loading and transmission loss patterns. Practical implications revealed the possibility of occasional losses caused mostly by information delays and misalignment of risk factors and premiums, which could affect Belize’s power cost affordability and scheduling of local generation. Further research can broaden this current scope to enhance planning on local power supply by integrating current findings on the importation market behavior with the buyer’s local power expansion plan.

An industry-level analysis of a pandemic's impact on stock market risk

We examine stock market risk surrounding the height of the COVID-19 pandemic at the industry level. We find that low-beta industries experienced a significant increase in relative risk at the worst possible time, while high-beta industries saw a reduction in relative risk. Shocks to market beta were not accompanied by offsetting changes in loadings on other factors, suggesting that risk didn't merely shift from one factor to another. Risk dynamics differed substantially from those surrounding the height of the 2008 financial crisis. Our results bear important implications for investors, firms, financial intermediaries and regulators in the event of another pandemic.

Energy Price Fluctuation and Real Estate Market Risk Prevention—Empirical Evidence at the Prefecture-Level City Level in China

Abstract The impact of energy price fluctuation on the macro economy is a hot issue that is continuously concerned by the academic community, but few literature have paid attention to the impact of energy price fluctuation on housing price and the financial risks that may arise. Based on the data of cities at and above the prefecture level in China, this paper empirically examines the impact of energy price fluctuation on the price fluctuation of the domestic real estate, and analyzes its influencing mechanism and transmission pathways. The results show that changes of energy price will significantly affect the fluctuation of domestic real estate price, and it is more obvious in large cities and megacities. Energy price affect the demand for housing through changes of interest rate levels on the one hand, and the supply of the real estate market through the cost of housing construction on the other hand, which leads to housing price fluctuation. After a series of robustness tests, the results are still valid. At the same time, the effect is asymmetric, that is, higher energy price increases the fluctuation of house price, but the impact of falling energy price is not significant. Therefore, when preventing real estate market risks, energy price should be considered as a forward-looking indicator to focus on, and the regulation and control policies of the real estate market should be scientifically formulated. This paper not only provides a new perspective on the mechanism of housing price formation, but also enriches the research on the interconnection between energy and financial markets.

Extreme weather, policy uncertainty, and risk spillovers between energy, financial, and carbon markets

Extreme weather, as well as policy uncertainties associated with climate change, have fueled global energy market turbulence and cross-market risk contagion, which have had serious effects on the economy. Using time-frequency network analysis based on quantile vector autoregression (QVAR), this study reveals the risk spillovers between China's energy, financial, and carbon markets in different time and frequency domains. The effects of extreme weather and policy uncertainty on market risk spillovers are also examined. The results show that: (1) Risk spillovers vary significantly in different time and frequency domains, and extreme risks tend to occur in the tails of the shock distribution, and market risk spillovers occur mainly in the long term; (2) Extreme weather and policy uncertainty significantly affect energy, financial, and carbon markets, altering their net spillover levels. Among which the coal market is the most volatile and highly sensitive to changes in external conditions; (3) Risk spillovers exhibit distinct time-varying characteristics, with extreme weather, climate policy uncertainty, and trade policy uncertainty exacerbating market risk spillovers. These findings could serve as a reference for monitoring market risk spillovers, facilitating energy transition, and achieving dual‑carbon targets.

Hedging Carbon Price Risk on EU ETS: A Comparison of Green Bonds from the EU, US, and China

This article aims to examine the hedging effect of green bonds in the US market, the European market, and the Chinese market on carbon price risk in the European Union Emission Trading System (EU ETS) from 2021 to 2023. By using daily datasets extracted from Bloomberg and the Vector Error Correction Model (VECM), the research provides evidence of the hedging effect of green bonds in all three markets on carbon price risk in the EU ETS. The paper concludes that the hedging ratio is positive for green bonds in the EU and China, while the figure for the US market is negative. Moreover, there is a positive effect of oil prices on carbon returns in EU ETS. Meanwhile, the opposite is found for stock prices.

Privacy’s Sky-High Battle: The Use of Unmanned Aircraft Systems for Law Enforcement in the European Union

Benefiting from the rapid advancements in Unmanned Aircraft Systems (UAS) technology with enhanced tracking and data collection capabilities, law enforcement authorities re-discovered air as a dimension where state power can be exercised in a more affordable, accessible, and compact way. On the other hand, during law enforcement operations, UAS can collect various types of data that can be personal or sensitive, threatening the right to privacy and data protection of the data subjects. Risks include challenges related to data security, bulk data collection, the diminished transparency and fairness resulting from the inconspicuous nature of UAS, as well as ethical concerns intertwined with privacy and data protection. Upon examination of the legal framework including the General Data Protection Regulation the Law Enforcement Directive, various Aviation rules, and the new proposal for the Artificial Intelligence Act, it becomes apparent that the EU legal framework’s adequacy in safeguarding privacy and data protection against law enforcement use of UAS is context-dependent, varying across use cases. The current framework lacks clarity, leading to arbitrary application and limited protection for data subjects. Enforcement of safeguards is insufficient, and the Aviation Regulations, applicable to law enforcement UAS, require member states' opt-in, which has not occurred as of the authors’ knowledge. The Artificial Intelligence Act addresses UAS operations but focuses on market risks rather than obligations imposed on law enforcement authorities. Consequently, the existing framework is rendered inadequate for medium to high-risk law enforcement operations, leaving individuals vulnerable and insufficiently protected against intrusive UAS surveillance. Rectifying this involves addressing the enforcement gap and making the necessary amendments to relevant regulatory aspects. Additionally, the implementation of specific technical measures and steps to foster effective cooperation among stakeholders in UAS deployment for law enforcement is imperative.

Stock market extreme risk prediction based on machine learning: Evidence from the American market

Extreme risk in stock markets poses significant challenges, necessitating greater attention in related research. This study presents an effective machine-learning model for forecasting extreme risks in the American stock market. Specifically, to address the issues of imbalanced data distribution and concept drift, we introduced class weight and time weight parameters to enhance the AdaBoost algorithm. Moreover, we improved the active learning framework by transitioning from manual to algorithmic annotation. Experiments on the S&P 500 index from 2005 to 2022 revealed that our optimal model significantly enhanced the classification performance, particularly for risk instances. Additionally, we validated the efficacy of customized sample weight values, the significance of the density-weight strategy, and the robustness of the overall framework under different risk definition criteria and feature lag periods. Our research is significant for the adoption of appropriate macroeconomic policies to mitigate downside risks and provides a valuable tool for achieving financial stability.

Auctions to reveal consumers’ willingness to pay for low-carbon hydrogen projects: combining lessons from renewables and natural-gas industries

Abstract Low-carbon hydrogen is expected to play a key role in realizing net-zero and sustainable development plans. Nonetheless, there is a gap between the cost of producing low-carbon hydrogen and its potential users’ willingness to pay for such hydrogen. To implement support for the development of the industry, we propose using low-carbon hydrogen long-term agreements allocated through auction mechanisms. The objectives are 2-fold: (i) matching supply and demand volumes considering the time horizon and geographical delivery point specification and (ii) allocating the subsidy. This perspective article innovates by proposing a reference price indexed to liquid to natural-gas prices, which is the main product that low-carbon hydrogen aims to substitute. The premium and the production cost are defined through a double-sided auction. This aims to minimize the public policy funds required to incentivize the low-carbon hydrogen market while facilitating long-term agreements and mitigating price risks that may hinder investment.

Optimal Investment for Defined-Contribution Pension Plans with the Return of Premium Clause under Partial Information

The optimal investment problem for defined contribution (DC) pension plans with partial information is the subject of this paper. The purpose of the return of premium clauses is to safeguard the rights of DC pension plan participants who pass away during accumulation. We assume that the market price of risk consists of observable and unobservable factors that follow the Ornstein-Uhlenbeck processes, and the pension fund managers estimate the unobservable component from known information through Bayesian learning. Considering maximizing the expected utility of fund wealth at the terminal time, optimal investment strategies and the corresponding value function are determined using the dynamical programming principle approach and the filtering technique. Additionally, fund managers forsake learning, which results in the presentation of suboptimal strategies and utility losses for comparative analysis. Lastly, numerical analyses are included to demonstrate the impact of model parameters on the optimal strategy.

Exploring Prospects for Innovation in India's Financial System via Green Financing

Green financing (GF) allows countries to foster economic growth while promoting environmentally sustainable practices through innovative advancements in the financial sector. This concept warrants examination in countries like India. The paper evaluates the potential of GF in India. Findings reveal a predominant focus on renewable energy within India's green financing landscape, followed by the construction and transportation sectors. However, the growth of green bonds, a key financial instrument, is hindered by their low credit rating. To foster the development of GF, a collaborative effort among government bodies, financial institutions, policymakers, private sector entities, and non-governmental organizations is imperative for market expansion and risk mitigation.

Liquidity Unveiled: Crafting an Index to Decode the Sovereign Bond Market Risk

Liquidity Unveiled: Crafting an Index to Decode the Sovereign Bond Market Risk