Market Risk Research Articles

Exploring the risk dynamics of US green energy stocks: A green time-varying beta approach

Green investments play a crucial role in fighting climate change and facilitating the shift towards a low-carbon economy in line with goals of the Paris Agreement. This paper focuses on the U.S. green energy sector, analyzing its underlying risk dynamics, especially during crisis periods. In this paper, we employ a novel green energy time-varying beta (GETVB) to assess the risk profiles of U.S. green energy stocks across different market conditions. We have chosen NASDAQ Clean Edge Green Energy Index (CELS) as the U.S. green energy market index. We use Markov-switching and discrete-threshold-regression models to examine whether the GETVB varies across regimes and is affected by market stress. In particular, we examine if the market risk of these stocks itself exhibits higher volatility during regimes of stress. Our results show that the green stocks are apparently risky, with a GETVB most likely to lie between 1.2 and 1.6. However, these stocks turn out to be resilient against market volatility with the market stress having negligible impact on GETVB. This suggests an inherent robustness of green stocks against extreme market conditions. This resilience makes the U.S. green energy stocks a potential safe destination for investors during times of significant market volatility. Based on the results, we recommend that policymakers bolster support for green investments through enhanced tax incentives and subsidies, aiming to standardize the ESG metrics for increased transparency, and aligning the international financial flows with strategies that align with meeting the Paris Agreement targets.

Corporate risk disclosure in response to heightened entry threat: Evidence from a quasi-natural experiment in China

Using the deregulation of market entry barriers in China as a quasi-natural experiment, this study examines how increased entry threats affect incumbent firms' risk disclosure. We find that firms respond by increasing their market-related risk disclosures. This effect is stronger among firms facing higher economic uncertainty, lower financial constraints, greater product market competition, and higher information transparency. Mechanism analysis shows that increased risk disclosure weakens the link between deregulation of market entry barriers and new firm entry, suggesting that such disclosures can effectively deter potential competitors. Moreover, while market reactions to market risk disclosures are neutral, they are negative for operational and financial risk disclosures. Our findings suggest that firms strategically disclose market risks to mitigate entry threats, thus enhancing their resilience and adaptability in dynamic markets.

Demystifying the dynamic relationship between news sentiment index and ESG stocks: Evidence from time-frequency wavelet analysis

Using the Time-Frequency Wavelet Analysis, this study investigates the dynamic connectedness between the News Sentiment Index (NSI) and ESG leader indices of developed countries. The empirical findings clearly show nontrivial dynamic comovements between NSI and the ESG indices and the existence of the ESG market volatility in different frequency scales. We also report some discrepancies in cross-linkage patterns among different countries. In particular, such associations for some countries (e.g., Australia and the United Kingdom) are negative, with the leading role of the ESG index against market risk due to high fluctuations in sentiments. These findings suggest that ESG stocks possess potential hedging and diversifying features and safe-haven attributes against market risk driven by negative sentiment during the outbreak of Black Swan events. Our empirical results have insightful implications for investors, portfolio managers, and policymakers.

Do regional trusts alleviate bond market risks? Evidence from the Chinese municipal corporate bond pricing

Municipal corporate bonds have attracted significant attention due to their pricing mechanisms and reflect China's distinct financial landscape. Existing research has highlighted the influence of informal institutional factors on bond pricing. This study introduces the social trust index to measure regional trust levels and assesses its correlation with municipal corporate bond issuance pricing. The empirical analysis indicates that higher regional trust levels are significantly associated with reduced issuance credit spreads for municipal corporate bonds. The relationship is moderated by local economic development and the strength of implicit government guarantees. Employing the “Yongmei” event as a case study, this research explores the dynamics of bond pricing mechanisms following a reduction in government implicit guarantees and finds that a weakening of these guarantees intensifies the influence of regional trust on credit spreads. Additionally, default events are observed to have spillover effects, impacting neighboring provinces and the overall market. This research extends the investigation of regional trust's pricing mechanisms to the municipal corporate bond market, thereby enriching the study of informal institutions' impact on financial market pricing. The findings underscore the importance of effectively harnessing market mechanisms by governments to mitigate risks in the municipal corporate bond sector.

Pricing Intellectual Capital in Asset Pricing Models: A study on Shanghai Stock Exchange

This study aims to examine the validity of intellectual capital as a priced risk factor in the asset pricing model. We have used the data of companies listed on the Shanghai Stock Exchange from 2000 to 2021. To test the models, time series as well as cross sectional analysis have been done using the Fama-MacBeth method. The results show that intellectual capital is a significant risk factor and after augmenting IC into asset pricing models improves the explanatory power to explain the variation in the stock returns. It further suggests that IC can be priced in the Chinese market, over and above what can be explained by covariance risk, size, value, investment and profitability factors.

Стохастично моделиране при маслодайните култури в България в условията на Зелената сделка

This study is designated to elaborate and propose a model for estimation of outcomes and effects at the level of production of oilseed crops in Bulgaria. The projections are manifested through scenarios based on the stochastic error, which is yielded as a difference between real and modelling results on the projected variables. It is deemed to provide a good basis for modelling of scenarios driven by specific parameters from the Green Deal of EU. The applied stochastic model gives a very good basis to elaborate and simulate further sensitive scenarios generated by the factors and variables of the Green Deal. It is thought to carried out through incorporation of the variables covering the world price of oilseeds and the use of nitrogen mineral fertilizers. The applied and evolved model for projecting the development and future outlook of the production of oilseed crops in Bulgaria provides a system of model equations, which is created as a interdependencies between different variables. This model is considered to achieve and reveal specific quantitative results, facilitating the scenario comparisons. The presented model results are made as an estimation of the stochastic error, standing for the difference between the model over the real historical data in the period of 15 years, between 2007 – 2021. It is assumed that production and market risks in the oilseeds sector will increase crucially if the full-scale implementation of the intended restrictions and bans connected to Green Deal measures are adopted. Those restrictions are seen in purported reduction in the use of plant protection chemicals and fertilizers application. Production risks are mainly highlighted in the cut of production yield, market volatility of the oilseed commodity and the affected efficiency due to cost increment. Market risks will mainly be related to securing a market stability and appropriate cost level of the production. The results from the model show that up to the time of this study work covering the historical real data up to 2021, specific signals and turbulences in the production, supply and market performance of the sector are not yet noticed and the supposed stagnation effects from the Green Deal measures are not displayed. Therefore, putting aside the expected strong impact of the Green Deal on the sector by 2030, the production and consumption of oilseeds is projected to remain strong and to gravitate in the high levels achieved in the best years reached recently, indicating that the sector is on the upward trend and the constraints and bans of the Green Deal may have a greater affect eventually in future.

RISKS OF INVESTMENT PORTFOLIO OF BANKS OF KAZAKHSTAN: PROBLEMS AND WAYS TO SOLUTION

The article examines the essence and classification of the definition of «market risk». It is noted that managing market risks in a bank is a complex and multifaceted process that requires constant analysis and monitoring of market conditions, and an important element of the market risk management system is the formation of an effective investment portfolio, which includes various assets with different levels of risk. This will allow you to control and minimize possible losses, and will also help the bank determine the optimal balance between risk and profitability, which will help increase its profits. Using modern data analysis methods, an analysis of the real average investment portfolio of second-tier domestic banks was carried out and an assessment of the generated conditional investment portfolio was given. Based on the study, problems were identified and directions for improving market risk management in the bank were given.

Strategies for Mitigating and Managing Risks in Bank Investments

This paper suggests that while studies of banking risk management often emphasize analytics and stress testing to improve risk governance, there is too little emphasis on historical case studies to generate comprehensive risk management strategies. Using numerous case studies, the paper suggests four categories of risk - market risks, credit risks, regulatory risks, and operational risks - that impact overall investment outcomes. By extensively reviewing both historic failures and historic successes, the paper highlights the importance of understanding different types of risks and adopting strategic measures to manage them effectively. Emphasis is placed on the necessity of a diversified investment portfolio to mitigate market volatility and credit exposure. Additionally, the paper provides actionable strategies for navigating regulatory complexities and operational challenges, supported by real-world case studies and practical examples. This research aims to equip investors with valuable insights and tools to optimize investment opportunities while minimizing potential risks in the banking sector.

Research on Housing Market Risk Management Based on Multidimensional Evaluation

The housing market is a high-risk industry with too many factors influenced by the outside world, and if these risks are not effectively managed, it will bring serious impacts on the stability of the economy and society. This paper takes the risk management of housing market as the research object, utilizes the information entropy model and ANP combined method to combine and assign the risk indicators, and constructs the cloud model of housing market risk evaluation. And the cloud model is applied to the GJ housing case for practical evaluation, and it is found that the cloud droplets of the comprehensive risk of the GJ housing market are mainly distributed in the medium-risk interval from 31 to 54, focusing on 41, and a small number of risk cloud droplets appear in the higher-risk cloud map, which is in line with the actual evaluation results, and further verifies the feasibility and validity of the method. Finally, based on the evaluation results, the risk control strategy of the relevant housing market is proposed, which can be used as a reference in the development process of subsequent housing projects.

Adaptive Risk Parity Strategies for Managing Portfolio Risk in Volatile Markets

This paper presents a novel model for Adaptive Risk Parity Portfolio Construction, designed to dynamically adjust portfolio weights based on changing market conditions and economic regimes. Traditional portfolio strategies, such as Modern Portfolio Theory (MPT), often rely on static assumptions that can lead to suboptimal performance in volatile markets. In contrast, the adaptive risk parity approach aims to equalize risk contributions across asset classes while responding to fluctuations in economic indicators, such as GDP growth and inflation rates. By employing techniques such as Markov switching models and machine learning for regime identification, the proposed model allows for real-time adjustments to asset allocations. The back-testing results demonstrate that the adaptive risk parity portfolio outperforms static risk parity and traditional mean-variance optimized portfolios in terms of risk-adjusted returns and drawdown management. This research contributes to the field of portfolio management by providing a framework that enhances resilience and adaptability in investment strategies.

Analysis of the Combined Impact of Socio-Economic Factors on House Price Volatility in the Housing Market

As an important variable in China’s economic system, real estate price is a key point closely linked to the country’s economy and people’s livelihood. It is of great practical significance to study the impact of socio-economic factors in the housing market on China’s house prices and its transmission mechanism, so as to put forward corresponding policy recommendations for stabilizing house prices and preventing real estate market and financial risks. Taking China’s time series data from 2014 to 2023 as a sample, this paper empirically investigates the law of the relationship between socio-economic factors in the housing market and house price fluctuations by constructing a structural vector autoregressive model, and employs impulse response function and variance decomposition to analyze the relationship between the two dynamically. The study shows that the 15-period variance decomposition effect of itself and residents’ disposable income are the main influencing factors of house price fluctuation, and the degree of explanation of its fluctuation is 55.5% and 23.2% respectively. In a market economy, real estate prices fluctuate up and down around their value due to the law of value. Therefore, real estate prices cannot rise indefinitely, and the final result should be stabilized within a reasonable level.

Manajemen Risiko Usahatani Cabai Rawit: Studi Kasus Di Kawasan Gunung Merapi

Cayenne pepper (Capsicum frutescens L) is a common horticultural commodity cultivated by farmers in the Mount Merapi area. Farmers in this region face the risk of fluctuating productivity due to unpredictable volcanic eruption activities. This study aims to describe the types, sources, and levels of risk, analyze and examine risk management in bird's eye chili farming based on risk levels, risk mapping, and the total household income analysis of bird's eye chili farmers in Mount Merapi. The sample consists of 100 farmers selected through simple random sampling, with the criteria being residents who have been farming cayenne pepper for 10 years in the Mount Merapi area, growing cayenne pepper as the main commodity on their farmland, and farmers with and without additional income sources outside of farming. Data analysis includes risk analysis, total household income analysis, and descriptive quantitative analysis. In Pakem Subdistrict, the coefficient of variation values for production, price, and profit risks are 0.67, 0.08, and 1.20, respectively. In Dukun Subdistrict, these values are 0.55, 0.14, and 2.40, respectively. Lastly, in Selo Subdistrict, the values are 0.45, 0.07, and 0.84, respectively. Household income analysis is divided into two categories: farmers with additional income sources outside of farming and those without additional income sources in each district. The highest average total household income of cayenne pepper farmers is found in Selo Subdistrict. Important considerations in agricultural risk management include providing insurance for cayenne pepperi, market development, and land diversification.

Evaluating Volatility Using an ANFIS Model for Financial Time Series Prediction

This paper develops and implements an Autoregressive Integrated Moving Average model with an Adaptive Neuro-Fuzzy Inference System (ARIMA-ANFIS) for BTCUSD price prediction and risk assessment. The goal of these forecasts is to identify patterns from past data and achieve an understanding of the future behavior of the price and its volatility. The proposed ARIMA-ANFIS model is compared with a benchmark ARIMA-GARCH model. To evaluated the adequacy of the models in terms of risk assessment, we compare the confidence intervals of the price and accuracy measures for the testing sample. Additionally, we implement the diebold and Mariano test to compare the accuracy of the two volatility forecasts. The results revealed that each volatility model focuses on different aspects of the data dynamics. The ANFIS model, while effective in certain scenarios, may expose one to unexpected risks due to its underestimation of volatility during turbulent periods. On the other hand, the GARCH(1,1) model, by producing higher volatility estimates, may lead to excessive caution, potentially reducing returns.

Mapping Financial Connections: Market Integration in Emerging Economies through Graph Theory

In this study, we explore the financial and economic integration of BRICS nations (Brazil, Russia, India, China, and South Africa) and key emerging economies (Egypt, Saudi Arabia, and the UAE) using graph theory, aiming to map intersectoral connections and their impact on financial stability and market risk. The research addresses a critical gap in the literature; while political and economic linkages between nations have been widely studied, the specific connectivity between sectors within these economies remains underexplored. Our methodology utilizes eigenvector centrality and Euclidean distance to construct a comprehensive network of 106 publicly listed firms from 2013 to 2022, across sectors such as energy, telecommunications, retail, and technology. The primary hypothesis is that sectors with higher centrality scores—indicative of their interconnectedness within the broader financial network—demonstrate greater resilience to market volatility and contribute disproportionately to sectoral profitability. The analysis yielded several key insights. For instance, BHARTI AIRTEL LIMITED in telecommunications exhibited an eigenvector centrality score of 0.9615, positioning it as a critical node in maintaining sectoral stability, while AMBEV SA in the retail sector, with a centrality score of 0.9938, emerged as a pivotal player influencing both profitability and risk. Sectors led by companies with high centrality showed a 20% increase in risk-adjusted returns compared to less connected entities, supporting the hypothesis that central firms act as stabilizers in fluctuating market conditions. The findings underscore the practical implications for policymakers and investors alike. Understanding the structure of these networks allows for more informed decision making in terms of investment strategies and macroeconomic policy. By identifying the central entities within these economic systems, both policymakers and investors can target their efforts more effectively, either to support growth initiatives or to mitigate systemic risks. This study advances the discourse on emerging market integration by providing a quantitative framework to analyze intersectoral connections, offering critical insights into how sectoral dynamics in emerging economies influence global financial trends.

Near or far? An investigation into the relationship between spatial proximity to CSRC district offices and stock crash risk

ABSTRACT Our analysis reveals that companies with headquarters in further proximity to China Securities Regulatory Commission (CSRC) district offices are more susceptible to stock crash risk. Furthermore, this relationship is notably stronger for firms whose CEOs lack political connections, are non-state-owned, and are not audited by the Big Four audit firms. Our findings imply that regulatory remoteness plays a crucial role in shaping stock market dynamics and risk.

Impact of Sharia‐Based Screening on Equity Risk: International Evidence

ABSTRACTThis paper investigates the impact of mainstream Sharia‐based screening methodologies on equity risk. The study is based on the daily returns of 23 pairs of stock indices belonging to different index providers, namely Dow Jones, FTSE, and S&P. For risk measurement, the study uses five Value at Risk (VaR) methods. Moreover, the conditional VaR (CVaR) is used to analyze the tail behavior of return distributions. The results show that Sharia‐based screening is a source of significant differences in risk. There is strong evidence that Islamic stock indices (ISIs) tend to be less risky than their conventional peers. Also, the screening process allows for keeping extreme negative returns at a lower level relative to unscreened indices. The analysis during the Dotcom and Subprime crises shows that, globally, the results are time‐invariant. In sum, Islamic screening is tantamount to prudential rules that lead to low‐risk portfolios; it is, in its essence, a risk‐reduction strategy. Findings suggest that Sharia‐compliant stocks would be of great attractiveness to risk‐averse investors. They will find their claim in this asset class, as its riskiness is relatively low. For banks, a lower VaR of Islamic investing implies fewer capital requirements for market risk when holding portfolios that include Sharia‐compliant stocks. Fewer capital requirements will enhance the banks' ability to invest and make profits, which will, in turn, result in reducing the cost of capital.

Measuring Stock Market Risk with Correlation

Measuring Stock Market Risk with Correlation

The Role of Public Information in Capital Markets with Investors of Unknown Risk Attitudes

We consider a single-period, pure-exchange setting where investors have mean-variance preferences. Investors are uncertain about the risk attitudes of other capital market participants and, thus, about the market's risk premium. Assuming that there are no strategic interactions among investors, we identify conditions under which investors choose to abstain from trading in the capital market due to the risk associated with an uncertain risk premium. In addition, we find that investors may prefer the disclosure of public information prior to trading, as this information reduces the risk premium and thus relaxes the condition for market participation. Therefore, the presence of investors with unknown risk attitudes suggests a beneficial role for public (accounting) information.

Challenges and Strategies Faced by Enterprise Financial Management under the Background of Tax Automation

This study explores the challenges and opportunities brought by tax automation to corporate financial management in America. In terms of challenges, the article points out the complexity and variability of tax regulations, the stability and security requirements of tax information systems, and the need to enhance professional competence in tax accounting. In terms of opportunities, tax automation has significantly improved financial work efficiency, reduced compliance risks, promoted the rationality of tax planning, and enhanced the market competitiveness and risk resistance of enterprises. To address these challenges and fully utilize opportunities, the article proposes a series of measures, including deepening the study of tax regulations, automating salary and tax processes, dynamically monitoring financial and tax policies, optimizing salary and tax planning, and utilizing big data technology for in-depth financial analysis to promote the optimization and upgrading of enterprise financial management.

Risk and Return Analysis of Stocks of Nepalese Commercial Banks

This research article analyzes the impact of risk and return of Nepalese commercial bank stocks on portfolio returns to assist investors in making informed investment decisions. It aims to examine the effects of individual bank stock returns, risks, market return, and market risk (beta) on overall portfolio performance. The study utilizes a quantitative approach, employing descriptive statistics, comparative (trend) analysis, correlation analysis, and hypothesis testing. Secondary data was collected from 18 commercial banks in Nepal, including post-merger banks, covering the fiscal years from 2015/16 to 2021/22. Statistical tools such as SPSS and EViews were used for analysis. The findings indicate that investors seeking higher returns might consider Prabhu Bank, which offers high mean returns but also comes with higher volatility. In contrast, risk-averse investors may prefer banks like NMB and Nabil, which provide lower mean returns but more stable performance. Notably, this research contributes to the understanding of the risk-return relationship in the context of 18 Nepalese commercial banks post-merger, providing new insights for investors. This study highlights the importance of analyzing individual bank stocks and their associated risks for portfolio construction. By understanding the dynamics of risk and return, investors can optimize their investment strategies and enhance portfolio performance. Overall, this research fills a gap in existing literature by focusing on the Nepalese commercial banking sector and its implications for investor decision-making. The insights gained from this study can guide investors in navigating the complexities of the stock market, ultimately leading to more informed and strategic investment choices. Future research could expand on this work by exploring additional factors influencing stock performance and investor behavior in the Nepalese context.