Market Risk Research Articles

Technology-Driven Financial Risk Management: Exploring the Benefits of Machine Learning for Non-Profit Organizations

This study explores how machine learning can optimize financial risk management for non-profit organizations by evaluating various algorithms aimed at mitigating loan default risks. The findings indicate that ensemble learning models, such as random forest and LightGBM, significantly improve prediction accuracy, thereby enabling non-profits to better manage financial risk. In the context of the 2008 subprime mortgage crisis, which underscored the volatility of financial markets, this research assesses a range of risks—credit, operational, liquidity, and market risks—while exploring both traditional machine learning and advanced ensemble techniques, with a particular focus on stacking fusion to enhance model performance. Emphasizing the importance of privacy and adaptive methods, this study advocates for interdisciplinary approaches to overcome limitations such as stress testing, data analysis rule formulation, and regulatory collaboration. The research underscores machine learning’s crucial role in financial risk control and calls on regulatory authorities to reassess existing frameworks to accommodate evolving risks. Additionally, it highlights the need for accurate data type identification and the potential for machine learning to strengthen financial risk management amid uncertainty, promoting interdisciplinary efforts that address broader issues like environmental sustainability and economic development.

Estimating Tail Risk in Ultra-High-Frequency Cryptocurrency Data

Understanding the density of possible prices in one-minute intervals provides traders, investors, and financial institutions with the data necessary for making informed decisions, managing risk, optimizing trading strategies, and enhancing the overall efficiency of the cryptocurrency market. While high accuracy is critical for researchers and investors, market nonlinearity and hidden dependencies pose challenges. In this study, the filtered historical simulation is used to generate pathways for the next hour on the one-minute step for Bitcoin and Ethereum quotes. The innovations in the simulation are standardized historical returns resampled with the method of block bootstrapping, which helps to capture any hidden dependencies in the residuals of a conditional parameterization in the mean and variance. Ordinary bootstrapping requires the feed innovations to be free of any dependencies. To deal with complex data structures and dependencies found in ultra-high-frequency data, this study employs block bootstrap to resample contiguous segments, thereby preserving the sequential dependencies and sectoral clustering within the market. These techniques enhance decision-making and risk measures in investment strategies despite the complexities inherent in financial data. This offers a new dimension in measuring the market risk of cryptocurrency prices and can help market participants price these assets, as well as improve the timing of their entry and exit trades.

Minimum capital requirement portfolios according to the new Basel framework for market risk

Minimum capital requirement portfolios according to the new Basel framework for market risk

Analyzing the Influence of Telematics-Based Pricing Strategies on Traditional Rating Factors in Auto Insurance Rate Regulation

This study examines how telematics variables such as annual percentage driven, total miles driven, and driving patterns influence the distributional behaviour of conventional rating factors when incorporated into predictive models for capturing auto insurance risk in rate regulation. To effectively manage the complexity inherent in telematics data, we advocate for the adoption of non-negative sparse principal component analysis (NSPCA) as a structured approach for data dimensionality reduction. By emphasizing sparsity and non-negativity constraints, NSPCA enhances the interpretability and predictive power of models concerning both loss severity and claim counts. This methodological innovation aims to advance statistical analyses within insurance pricing frameworks, ensuring the robustness of predictive models and providing insights crucial for rate regulation strategies specific to the auto insurance sector. Results show that, to enhance auto insurance risk pricing models, it is essential to address data dimension reduction challenges when integrating telematics data variables. Our findings underscore that integrating telematics variables into predictive models maintains the integrity of risk relativity estimates associated with traditional policy variables.

Exploring Crisis Mitigating Measures of Micro and Small Enterprises during the Coronavirus Pandemic Crisis in Indonesia

This study examines the crisis mitigation measures (CMMs) implemented by micro and small enterprises (MSEs) during the COVID-19 pandemic in Indonesia. The research has three main goals: (i) to assess the impact of COVID-19 on MSMEs; (ii) to identify the business risks encountered by MSMEs during the crisis; and (iii) to investigate the most frequently adopted CMMs by MSMEs during the pandemic. The analysis utilizes secondary data from the 2020 national survey on MSEs in the manufacturing sector and primary data from a field survey of 137 MSEs across various cities and industries in Indonesia, conducted between May and September 2020. The findings indicate that the primary business risks during the crisis were (i) market risk (decreased domestic demand); and (ii) production risk (production stoppages due to lockdown measures). Consequently, many MSEs had to temporarily cease, shut down entirely, or pivot to different industries to remain operational, which was a common CMM among the affected MSEs. The study’s limitation is its small sample size, which prevents comprehensive survival analyses that could reveal significant differences in the development trajectories of MSEs during the crisis. Despite this, the study adds valuable insights to the literature on MSEs during economic crises.

A Study on Perception of Mutual Fund Investment

This study explores the performance, risks, and opportunities associated with mutual fund investments, particularly in the Indian market. It evaluates different mutual fund types, analyzing their historical returns and risk metrics to provide investors with data-driven insights. The research examines the factors influencing mutual fund performance, including market volatility, asset management strategies, and economic conditions. Additionally, the study assesses investor behaviour in response to mutual fund performance, emphasizing the role of financial literacy and risk tolerance. The findings suggest that mutual funds offer a viable investment option for wealth creation, with equity-based funds outperforming others over a long-term horizon. However, market risks remain significant, requiring careful management and strategic investment planning

Risk Management and Software Solutions in Financial Institutions: Move Towards Effective Implementation

Technological advancements and intensified regulatory pressures have led to a new level of the evolution of risk management in financial institutions. The entities have exposure to different categories of risks, such as credit risks, market risks, operational risks, and cybersecurity risks. The growing intensity of these factors makes proper risk management crucial for the stabilization and success of such enterprises. This report analyzes the best practices and software applications adopted by financial institutions for effective risk management. Improved risk identification, assessment, mitigation, and reporting within the organization would be through the integration of advanced analytics, AI, ML, and cloud computing. Based on this, the current paper assesses the part that these technologies play in contributing to compliance, cost savings, and better decision-making skills.

A Study of Hong Kong's Insurance Industry Based on the New Development Pattern Model

Against the backdrop of global economic changes and China's economic restructuring, the potential of Hong Kong's insurance industry as an investment channel is gaining increasing attention. This thesis aims to explore the opportunities and challenges of Hong Kong's insurance industry under the new development pattern. Through a combination of quantitative and qualitative analyses, it examines the historical data and future trends of Hong Kong's insurance market and conducts a study based on first-hand information obtained through data surveys and expert interviews, exploring the specific impacts of the new development pattern on the insurance industry, and incorporating the development of the Greater Bay Area into the research framework. Hong Kong's insurance industry harbours a wealth of investment opportunities in terms of expanding market size, product diversification, and policy support, but it also faces challenges such as intensifying market competition and international uncertainty. The study recommends that investors should focus on long-term trends to optimise their investment strategies and address market risks.

Design, simulation and feasibility of the innovative agricultural warehouse receipt system through dynamic programming and agent-based models.

To address marketing challenges in the agricultural sector, provide financial support for small-scale farmers over marketing seasons, manage price risks of agricultural products, and enhance the functioning of agricultural mercantile exchanges, it is attainable to implement an efficient and compliant warehouse receipt system (WRS) that aligns with the legal, institutional, social, and economic-financial conditions of a country. The aim of this study is to design, simulate, and assess the feasibility of an innovative WRS in the agricultural sector. To achieve this, a WRS was designed and evaluated for maize crop in Iran. The research methodology of this study is divided into three main parts: design, simulation, and feasibility assessment of the WRS. The design process incorporated the FAO-provided (2009) warehouse receipt system development, considering the experiences of various countries and the institutional and financial regulations specific to Iran. Additionally, a dynamic programming model was used to simulate the system, and an agent-based model was utilized for system feasibility assessment. The study results demonstrated that it is possible to design an innovative and efficient WRS by involving five key actors, including farmers, buyers (maize traders), banks, mercantile exchange market, and the warehouse (governmental institution), and establishing clear communications among them. Based on simulation results using dynamic programming model, it was evident that four parameters, namely the annualized loan interest rate, the valuation coefficient for loan collateral, price volatility of the product over the marketing season, and the warehouse cost-to-product value ratio, significantly impact the adoption of the WRS by farmers. In conclusion, the findings from the agent-based model revealed that setting the annualized loan interest rate at 8%, a collateral valuation coefficient of 85%, price fluctuations over the non-harvest time at 60%, and a warehouse cost-to-product value ratio of 2% can result in the participation of nearly 100% of farmers in the proposed WRS.

Predicting the equity premium with a high‐threshold risk level and the price of risk

Predicting the equity premium with a high‐threshold risk level and the price of risk

Clarity in Crisis: How UK Firms Communicated Risks during COVID-19

This study explores the influence of risk disclosure levels and types on the readability of annual reports of non-financial firms in the UK during the COVID-19 outbreak. It further investigates how the disclosure of COVID-19-related information moderates the relationship between risk disclosure and readability. The study uses a content analysis approach and CFIE software to measure the level of risk disclosure and readability in the annual reports of non-financial firms listed on the FTSE all-share from 2019 to 2021. The results show a positive and significant effect of risk disclosure level on readability, which is stronger for firms that disclosed COVID-19 information. Different types of risk disclosure have varying effects on readability, with COVID-19 risk, credit risk, and strategic risk positively affecting readability, while operational risk negatively affects it. The study contributes to the literature on information asymmetry and institutional theory by demonstrating how risk disclosure and readability are influenced by external factors like the COVID-19 outbreak and internal factors such as firm characteristics and types of risks. It introduces a new risk definition and category specific to the COVID-19 pandemic and develops new measurements for risk disclosure, including credit, liquidity, market, operational, business, strategic, and COVID-19 risks. The study provides valuable insights for managers, investors, regulators, and standard setters on the relationship between risk disclosure and readability in annual reports. It highlights the importance of disclosing COVID-19-related information to enhance the readability and understandability of financial communication. The paper contributes to the literature and practice on risk disclosure, readability, and financial communication during crises.

Trade can buffer climate-induced risks and volatilities in crop supply

Abstract Climate change is intensifying the frequency and severity of extreme events, posing challenges to food security. Corn, a staple crop for billions, is particularly vulnerable to heat stress, a primary driver of yield variability. While many studies have examined climate impact on average corn yields, little attention has been given to the climate impact on production volatility. This study investigates the future volatility and risks associated with global corn supply under climate change, evaluating the potential benefits of two key adaptation strategies: irrigation and market integration. A statistical model is employed to estimate corn yield response to heat stress and utilize NEX-GDDP-CMIP6 climate data to project future production volatility and risks of substantial yield losses. Three metrics are introduced to quantify these risks: Sigma (σ), the standard deviation of year-on-year yield change, which reflects overall yield volatility; Rho (ρ), the risk of substantial loss, defined as the probability of yield falling below a critical threshold; and Beta (β), a relative risk coefficient that captures the volatility of a region's corn production compared to the globally integrated market. The analysis reveals a concerning trend of increasing year-on-year yield volatility (σ) across most regions and climate models. This volatility increase is significant for key corn-producing regions like Brazil and the United States. While irrigated corn production exhibits a smaller rise in volatility, suggesting irrigation as a potential buffer against climate change impacts, it is not a sustainable option as it can cause groundwater depletion. On the other hand, global market integration reduces overall volatility and market risks significantly with less sustainability concerns. These findings highlight the importance of a multidimensional approach to adaptation in the food sector. While irrigation can benefit individual farmers, promoting global market integration offers a broader solution for fostering resilience and sustainability across the entire food system.

RISIKO USAHATANI TOMAT (LYCOPERSICUM ESCULENTUMMILL) DI KECAMATAN LEMBAH GUMANTI KABUPATEN SOLOK (Risks of Tomato (Lycopersicum Esculentum) Farming in Lembah Gumanti District Solok Regency)

This study aims to determine analyze the characteristics of tomato farmers, analyze the income of tomato farmers and analyze the risk level of tomato farming in Lembah Gumanti District, Solok Regency. The method used in this study is quantitative descriptive The analysis used is income, variety, standard deviation, coefficient of variation, and lower limit. The respondents who were sampled were 40 farmers with a population of 423 farmers and were selected randomly (proportional random sampling). The results showed that the characteristics of tomato farmers have a productive age of 25 to 48 years, a low level of education, farmers who already have long farming experience. The amount of cash costs incurred by farmers is IDR 6,456,733.33 / MT and the calculated costs are IDR. 4,919,309.63/MT with revenue of Rp 23,608,331.25/MT so that cash income is Rp 17,209,386.82/MT and total income is Rp 12,290,077.19/MT. Tomato farming has a production risk level with a KV value of 10.39, price risk with a KV value of 18.56, and income risk with a KV value of 13.36 with a lower income limit of Rp 10,450,763.47 where this figure will indicate the amount of loss that will be experienced by tomato farmer

Forecasting crude oil prices with global ocean temperatures

This paper explores the role of ocean temperature (OT) in forecasting crude oil prices. Global OT shows statistically and economically significant predictive power, with corresponding in-sample and out-of-sample R2s of 1.81 % and 1.74 %, respectively. Both southern and northern hemisphere OTs can predict future oil prices, and the former is more predictive. Compared to the Atlantic, OTs in the Indian and Pacific Oceans display considerable predictive gains. In addition, we find that OTs have stronger forecasting power for crude oil prices in the recent period, with the in-sample R2 of global OT for the recent decade's sample reaching as high as 2.74 %. Correspondingly, we find that OTs' predictive power in the full sample can be explained through two channels: market risk and investor attention. In the recent period, in addition to these two channels, OTs have also influenced crude oil prices through the oil fundamental channel.

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.