Market Risk Management Research Articles

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.

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.

Statistical model validation through white noise hypothesis testing in regression analysis and ARIMA models

In the domain of finance and economics, ensuring the validity and dependability of forecasting models is crucial. In this paper, the White Noise Hypothesis Test is elucidated as an essential model validation tool for both the regression and ARIMA models. In the case of regression analysis, there is a need for residual independence and homoscedasticity to confirm that the estimates of the parameters are not biased, that is, the chances of the parameter estimated to be true is high. In the case of an ARIMA model, residuals are checked for the white noise characteristics, and so as to ensure that all the relevant patterns associated with the time series have been detected. The paper uses simulated stock market data and risk management scenarios to demonstrate how these tests enhance model reliability, since when white noise characteristics are ascertained, the residuals are confirmed random and independent. The importance of validating models is discussed in the context of economic forecasting and policy analysis, where the validated models are indispensable for anticipating a market upheaval or to make right economic decisions. Therefore, when white noise test is used to confirm that the model meets the white noise criteria, this improves the credibility of predictions and thus influences strategic decision making in the uncertain environment.

FEATURES OF THE DESIGN AND IMPLEMENTATION OF INVESTMENT AND CONSTRUCTION PROJECTS UNDER CONDITIONS OF BALANCED DEVELOPMENT

The development and implementation of investment and construction projects on the basis of balanced development is critical for achieving sustainable growth. This article examines the key features and challenges associated with such projects, emphasizing the importance of integrating economic, environmental and social practices into construction activities. The study provides insights into best practices and strategies for effectively managing investment and construction projects to ensure long-term success and sustainability.The main goals of this article are the definition of the fundamental principles of balanced development in the context of investment and construction projects, the analysis of features that contribute to the achievement of balanced development.The article uses a comprehensive review of the literature and case studies, which allows for a holistic understanding of the multifaceted nature of investment and construction projects in conditions of balanced development. Emphasis is placed on the integration of sustainable development principles from the initial stages of design. This includes consideration of environmental impact assessment, efficient use of resources and use of sustainable materials and technologies. Also, the goal of project implementation is that the projects are economically feasible and provide long-term financial returns. This involves careful market analysis, cost-benefit analysis and risk management strategies to mitigate potential financial problems. The importance of involving all relevant stakeholders, including local communities, government bodies and investors, in the decision-making process is emphasized. Effective communication and collaboration are necessary to address diverse interests and reach consensus. Ensures compliance of projects with national and international rules and standards. This applies to environmental regulations, building regulations and labor legislation. Compliance minimizes legal risks and increases project credibility.Focused on optimizing the use of resources such as materials, labor and time, implementing lean construction methods and advanced project management practices can increase efficiency and reduce waste.The implementation of advanced technologies such as building information modeling (BIM), automation and renewable energy systems increase design accuracy, reduce costs and increase sustainability. Conformity of construction work to established quality standards, regular inspections, testing and adherence to best practices are critical to obtaining high quality results.Constant monitoring of project progress and effectiveness is emphasized. The use of key performance indicators (KPIs) and regular evaluations help to identify problems early and make the necessary adjustments.The development and implementation of investment and construction projects in conditions of balanced development requires a complex and integrated approach. By prioritizing sustainability, economic viability, stakeholder engagement and regulatory compliance, stakeholders can ensure these projects contribute positively to long-term growth and development. The paper provides a road map to navigate the complexities of such projects, ultimately contributing to a more resilient and sustainable human-made environment

A Deep Learning Quantile Regression Photovoltaic Power-Forecasting Method under a Priori Knowledge Injection

Accurate and reliable PV power probabilistic-forecasting results can help grid operators and market participants better understand and cope with PV energy volatility and uncertainty and improve the efficiency of energy dispatch and operation, which plays an important role in application scenarios such as power market trading, risk management, and grid scheduling. In this paper, an innovative deep learning quantile regression ultra-short-term PV power-forecasting method is proposed. This method employs a two-branch deep learning architecture to forecast the conditional quantile of PV power; one branch is a QR-based stacked conventional convolutional neural network (QR_CNN), and the other is a QR-based temporal convolutional network (QR_TCN). The stacked CNN is used to focus on learning short-term local dependencies in PV power sequences, and the TCN is used to learn long-term temporal constraints between multi-feature data. These two branches extract different features from input data with different prior knowledge. By jointly training the two branches, the model is able to learn the probability distribution of PV power and obtain discrete conditional quantile forecasts of PV power in the ultra-short term. Then, based on these conditional quantile forecasts, a kernel density estimation method is used to estimate the PV power probability density function. The proposed method innovatively employs two ways of a priori knowledge injection: constructing a differential sequence of historical power as an input feature to provide more information about the ultrashort-term dynamics of the PV power and, at the same time, dividing it, together with all the other features, into two sets of inputs that contain different a priori features according to the demand of the forecasting task; and the dual-branching model architecture is designed to deeply match the data of the two sets of input features to the corresponding branching model computational mechanisms. The two a priori knowledge injection methods provide more effective features for the model and improve the forecasting performance and understandability of the model. The performance of the proposed model in point forecasting, interval forecasting, and probabilistic forecasting is comprehensively evaluated through the case of a real PV plant. The experimental results show that the proposed model performs well on the task of ultra-short-term PV power probabilistic forecasting and outperforms other state-of-the-art deep learning models in the field combined with QR. The proposed method in this paper can provide technical support for application scenarios such as energy scheduling, market trading, and risk management on the ultra-short-term time scale of the power system.

Spillover effect of the geopolitical uncertainty on the cryptocurrency market

PurposeThis study aims to examine the influence of geopolitical uncertainty on cryptocurrency markets (CM).Design/methodology/approachUtilizing two distinct sets of daily returns data spanning from January 1, 2019, to May 4, 2023, the analysis employs the geopolitical risk (GPR) index formulated by Caldara and Iacoviello (2022), which encapsulates two pivotal events: the COVID-19 pandemic and the Russia–Ukraine conflict. The cryptocurrency market (CM) encompasses Bitcoin (BTC), Ethereum (ETH), Litecoin (LTC) and Dogecoin (DOGE). Employing the DCC-GARCH model and supplementing it with wavelet coherence analysis to discern perceptual distinctions between short- and long-term market reactions.FindingsThe main findings indicate that the GPR index clearly impacts the return of CM in the short-, mid- and long-term periods. BTC exhibited the highest volatility in response to changes in the GPR index. The cryptocurrency market offers a better diversification opportunity, and the impact of geopolitical events varies across time, with their direction and magnitude closely related to the specificity of the CM.Practical implicationsThis research is helpful for financial market investors, portfolio and risk managers, make informed decisions about including cryptocurrencies in their investment portfolios to mitigate the risks in uncertainty period.Originality/valueCryptocurrency market volatility is treated weakly during the risk period. With advanced statistical method, this study links two important events: the COVID-19 pandemic and the Russia–Ukraine conflict and selects the top four cryptocurrencies constituting 80% of the market. This study examines the impact of geopolitical risk on the cryptocurrency market and shows that this market is considered a safe haven.

Assessing the Feasibility Study of XYZ Company’s New Supermarket Branch in Menteng, Central Jakarta

XYZ Company plans to expand into Jakarta’s supermarket market, leveraging Indonesia’s growth in the food and beverage industries. With a population of 273 million in 2024, urbanization, a rising middle class, and evolving consumer behaviours drive sector growth. XYZ Company, with over 40 successful stores in Bali and Lombok, aims to enter Jakarta’s competitive retail market, dominated by players like Hero Supermarket and Carrefour. This feasibility study uses a mixed-methods approach, including interviews, surveys, financial reports, and market analyses. Financial metrics such as Net Present Value (NPV), Internal Rate of Return (IRR), and payback period indicate the project’s viability, with costs estimated at Rp. 22,858,000,000.00. Marketing analysis, using SWOT, TOWS, and Porter’s Five Forces, reveals opportunities for differentiation through premium product offerings and food courts, enhancing customer experience and foot traffic. Significant opportunities for XYZ Company to differentiate include leveraging the growing middle class and diverse consumer preferences. Integrating food courts within supermarkets, a strategy used by competitors like Transmart Carrefour and Grand Lucky Supermarket, can significantly enhance customer experience. Focusing on premium products and readyto-eat meals can attract a broader customer base. Strategic recommendations involve selecting high-traffic locations, expanding the online presence, and adopting sustainable practices to control costs and appeal to environmentally conscious consumers. The study emphasizes adapting to market trends and maintaining financial prudence for long-term profitability. In conclusion, the feasibility study offers a comprehensive analysis of XYZ Company’s proposed expansion into Jakarta. By focusing on strategic market entry, financial planning, and risk management, XYZ Company can successfully establish its presence in Jakarta’s competitive retail market, ensuring sustainable growth and profitability.

The Dynamic Cointegration Relationship between International Crude Oil, Natural Gas, and Coal Price

In this study, we conducted an in-depth analysis of the dynamic cointegration relationship between international crude oil, natural gas, and coal price indices from 2009 to 2023, revealing the changes and differences in the cointegration relationship between these three prices during different periods. Utilizing statistical analysis and economic modeling, we found significant cointegration among these energy prices during the initial decade-long observation period, indicating their close interaction in the global energy market influenced by supply and demand fundamentals, macroeconomic conditions, and geopolitical landscapes. However, since 2020, this long-standing stable cointegration relationship has been severely disrupted due to the global spread of the COVID-19 pandemic and escalating geopolitical tensions, leading to a notable increase in volatility and uncertainty in the energy market. Further analysis highlights that, in recent years, with the strengthening of global climate governance and the advancement of the low-carbon transition trend, fossil fuel markets, particularly high-carbon-emitting crude oil and coal markets, have undergone significant adjustments. Meanwhile, the role of natural gas as a transitional clean energy source has become increasingly prominent. The findings of this study have significant implications for energy policy formulation, market risk management, and strategic planning in the energy industry, while providing directions for future research on resilience and adaptability in the transition process of energy systems.

An Exploration of the Development of Computerized Data Mining Techniques and Their Application

The process of data mining involves extracting valuable information and knowledge from vast amounts of data, encompassing various fields such as statistics, machine learning, and database theory. It transcends mere data processing tools, employing intelligent methods for data analysis and stands as a pivotal technology in the era of big data. Its applications span across numerous domains including consumer behavior analysis, market marketing strategies, risk management, medical diagnosis, and fraud detection, with notable prominence in the realm of commerce. In e-commerce platforms, data mining techniques adeptly analyze user purchase histories and browsing records to precisely identify user preferences, recommend personalized products, thus enhancing user satisfaction and sales revenue. In devising marketing strategies, analyzing market data and consumer feedback optimizes product promotion strategies, bolstering market competitiveness. In the medical domain, data mining exhibits robust vitality. By analyzing patient medical records, genetic data, drug response data, predictions of disease trends, optimization of treatment plans, and even early disease warnings are made possible, thereby improving disease cure rates. In the realm of financial security, data mining plays a pivotal role by analyzing transaction data and user behavior to timely detect abnormal transactions and identify fraudulent activities, thereby safeguarding the security and stability of the financial system. In the Internet of Things (IoT) domain, data mining techniques analyze massive sensor data, enabling real-time monitoring and predictive maintenance of devices, thus enhancing operational efficiency and longevity. Data mining extends beyond the aforementioned domains, finding significant applications in social governance, scientific research, intelligent manufacturing, and more. Not only does data mining enhance the scientific rigor and precision of decision-making, but it also empowers various industries to improve efficiency, realize intelligent transformations, thereby further advancing societal progress.

Carbon emissions trading price forecasts by multi-perspective fusion

<p>The precise prediction of carbon emissions trading prices is the foundation for the stable and sustainable development of the carbon financial market. In recent years, influenced by a combination of factors such as the pandemic, trading regulations, and policies, carbon prices have exhibited strong random volatility and clear non-stationary characteristics. Traditional single-perspective prediction methods based on conventional statistical models are increasingly inadequate due to the homogenization of features and are struggling to adapt to China's regional carbon emissions trading market. Therefore, this paper proposes a multi-perspective fusion-based prediction method tailored to the Chinese market. It leverages carbon emissions trading information from key cities as relevant features to predict the price changes in individual cities. Inspired by the development of artificial intelligence, this paper implements various time series models based on deep neural networks. The effectiveness of the multi-perspective approach is validated through multiple metrics. It provides scientific decision-making tools for domestic carbon emissions trading investors, making a significant contribution to strengthening carbon market risk management and promoting the establishment and rational development of a unified carbon market in China.</p>

Utilization Schemes of the Pre-Settlement Risk Limits

The purpose of the article is to investigate the selected method employed to manage the counterparty credit risk, namely the application of various risk limits. The aim is to recognize utilization schemes of the pre-settlement risk limits in the Polish OTC derivatives market in the relationship between a financial institution and a non-financial counterparty. They are used not only to cover the credit exposure but also to support and enhance the entire market risk management process and day-to-day operations in the financial institutions. Methodology. The research method comprises the analysis of recommendations of the Polish Financial Supervision Authority as well as reports, documents and market risk management principles of selected financial institutions (WSE listed banks). Results of the research. The study indicates two utilization schemes of the pre-settlement limit setup applicable both for daily and credit-related transactions. The first one assumes that the risk requirements remain unchanged during the contract lifetime, the second one considers variable risk requirements over time. Practical implications are discussed (in relation to a notional trade size, risk exposure and margining policy).

The Role of Machine Learning in Enhancing Risk Management Strategies in Financial Institutions

Purpose: The aim of the study was to examine the role of machine learning in enhancing risk management strategies in financial institutions. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study revealed that integration of machine learning into risk management strategies within financial institutions has demonstrated significant potential for enhancing decision-making processes and mitigating various risks. The study have consistently shown that machine learning algorithms outperform traditional statistical methods in areas such as credit risk assessment, fraud detection, market risk management, and loan portfolio optimization. These advancements have led to improved accuracy, efficiency, and timeliness in risk assessment, enabling financial institutions to make more informed decisions while reducing losses and enhancing overall performance. Unique Contribution to Theory, Practice and Policy: Modern Portfolio Theory (MPT), Efficient Market Hypothesis (EMH) & Agency Theory may be used to anchor future studies on role of machine learning in enhancing risk management strategies in financial institutions. Invest in building robust data infrastructure and governance frameworks to support the implementation of machine learning models in risk management practices. High-quality data is crucial for training accurate and reliable machine learning algorithms. Establish regulatory guidelines and standards for the responsible use of machine learning in risk management within the financial industry. These guidelines should address issues such as model transparency, fairness, and accountability to ensure ethical and responsible practices.

MARKET EFFICIENCY AND STABILITY IN THE ERA OF HIGH-FREQUENCY TRADING: A COMPREHENSIVE REVIEW

This comprehensive review analyzes the impact of high-frequency trading (HFT) on market efficiency and stability, synthesizing insights from 50 peer-reviewed articles, industry reports, and regulatory documents. High-frequency trading, which leverages sophisticated algorithms and high-speed data networks, has significantly transformed financial markets. The review confirms that HFT enhances market efficiency by providing liquidity and facilitating rapid price discovery, contributing to tighter bid-ask spreads and lower transaction costs. However, it also highlights several challenges, including market fragmentation, increased volatility, and potential for market manipulation. The review examines how HFT can exacerbate market instability and systemic risks, as demonstrated by incidents like the 2010 Flash Crash. It underscores the importance of robust risk management practices and regulatory measures to mitigate these risks and enhance market resilience. While current regulatory frameworks have had some success, continuous adaptation is necessary to keep pace with rapid technological advancements. Additionally, the review points to the potential of AI and machine learning in improving market surveillance and risk management. Ultimately, the findings suggest that a balanced approach to regulation and innovation is crucial to maximizing the benefits of HFT while ensuring market integrity and stability.

Analysis of Periodic and Seasonal Characteristics in Time Series: Taking the Dow Jones Industrial Average as an Example

The objective of this research is to conduct a comprehensive analysis of the Dow Jones Industrial Average (DJIA) with a focus on identifying cyclical and seasonal patterns observed in long-term time series data. In order to accurately predict future market trends, this research employs a combination of fundamental and advanced forecasting techniques, including mean, naive, snaive, drift, Holt, Holt-Winters, and ARIMA models. Furthermore, the research explores the impact of return ratios on market investment opportunities. Customized portfolio optimization strategies are developed based on the risk profiles of different investors, along with quantitative trading strategies that effectively manage market risks using the Capital Asset Pricing Model (CAPM). The research shows that the price of Dow Jones Industrial Index has a gradual upward trend, and its return rate has a certain periodicity and seasonality. The aim of this research is to provide valuable insights into the fundamental framework of the American stock market, enabling economists, investors, and policymakers to make informed decisions.

Coal price shocks, investor sentiment, and stock market returns

The development of energy finance and the financialization of bulk commodities has deepened the financial attributes of coal, resulting in coal price shocks not only affecting the macroeconomy through the production sector, but also further affecting the financial market. This study investigates the dynamic impacts of coal price shocks on stock market returns accounting for the macroeconomic path by using a time-varying parameter vector autoregressive with stochastic volatility (TVP-SV-VAR) model, as well as examines the role of investor sentiment in the transmission of coal price shocks to the stock market. The results show that coal price shocks have a significant negative impact on Chinese stock market returns, but the impact degree is time-varying, particularly larger during economic downturns. Moreover, individual investor sentiment provides an important channel in the transmission of coal price shocks to the stock market. Insights gleaned from the findings in this study are useful for coal industry regulation policy formulation, market investment decision-making and energy market risk management.

Risk Management Strategies in Facing Global Financial Market Volatility

Global financial market volatility refers to the unpredictable fluctuations in the prices of financial assets such as stocks, bonds, currencies, and commodities. This volatility can stem from various factors such as economic indicators, geopolitical events, policy changes, technological advancements, and investor sentiment. This research aims to analyze effective risk management strategies in facing global financial market volatility. Market volatility often triggers anxiety, uncertainty, and irrational investment decisions. In this situation, it is important for investors to adopt the right strategies to minimize risks and maximize long-term investment outcomes. In this study, data is collected from various sources related to global financial market volatility and risk management strategies adopted by investors during periods of uncertainty. Qualitative and quantitative analyses of this data are conducted to identify patterns of investor behavior and the effectiveness of applied risk management strategies. The research findings indicate that maintaining a long-term perspective, retaining portfolio diversification, and taking an active approach to risk management are effective strategies in dealing with market volatility. Open communication with financial professionals is also proven to be essential in helping investors stay calm and make rational decisions.

Do macroprudential policies reduce risk spillovers between energy markets?: Evidence from time-frequency domain and mixed-frequency methods

Preventing volatility spillovers between energy markets is essential for maintaining financial stability. Macroprudential policies play an invaluable tool in financial risk monitoring and help to reduce the incidence of systemic risk. However, the effectiveness of macroprudential policies on risk spreads in the energy system is still unclear. This article applies the TVP-VAR (time-varying parameter vector autoregression) spillover methods to capture the risk connectedness between energy markets in the time and frequency domains, and constructs an asymmetric GARCH-MIDAS-MPP model to investigate the influence of macroprudential policies on volatility overflows of the energy system. The results suggest that there are strong risk links between global energy markets and long-term risk resonances play a dominant role. Time-varying risk overflows occur throughout the energy sector, and market turbulence amplifies these risk communications. Whether on a time or frequency horizon, the oil market represents the largest emitter of fluctuation transmissions, while the fuel oil market stands out as the biggest recipient of risk diffusion. We do not observe that macroprudential policies can significantly reduce the average and long-run risk premiums of the energy system. Conversely, our results reveal that macroprudential policies intensify short- and medium-term risk spreads between global energy markets. This paper provides some insight into energy market risk contagion, which is conducive to the improvement of macroprudential policies for energy market risk management.

Impacts of a Seven Factor Model on Gil Stock Price – A Case in Vietnam

Fluctuation of stock price in ex-import and textile system in developing countries such as Vietnam will reflect the business health of economic system. Based on the above data analysis from our regression model, although low inflation during 2015-2016 is a good signal for GIL stock price, we would suggest the government, Ministry of Finance and State Bank of Vietnam consider to control inflation more rationally, i.e not increasing much and suitable with each economic development stage. Governmental bodies and bank system also need to apply macro policies to stimulate economic growth, however not reducing lending rate too much, together with credit, operational and market risk management, corporate governance and controlling bad debt. This research finding and recommended policy also can be used as reference in policy for ex-import and textile system in many developing countries.

Effect of Risks Management on the Financial Performance of Listed Deposit Money Banks in Nigeria

This study examined the effect of risk management on the financial performance of listed DMBs in Nigeria. Adopting correlation research design, the target population was all the listed DMBs in Nigeria as at 31st December, 2022 and were nineteen (19) in number. Out of the 19 banks, 16 were purposively selected as sample based on the criteria that, the bank must have been listed before 31st December 2017 and also have complete annual report and account over the period of five years from 2018-2022. The study used descriptive statistics and panel regression analysis to analyze the data collected, and the results of the analyses revealed that, credit risk management, market risk management and capital adequacy risk management have positive and significant effect on the financial performance of the listed DMBs in Nigeria over the period of the study. However, liquidity risk management was found to have negative but insignificant effect on the financial performance of the banks. Hence, the study concluded that; effective risk management have positive and significant effect on the financial performance of listed DMBs in Nigeria. Based on these findings, this study recommends that; the management of listed DMBs in Nigeria should improve and strengthen their liquidity management strategies, while keeping adequate watch on credit risk, market risk, and capital adequacy risk to further improve their financial performance. Therefore, further studies could look into the effect of effective risk management process (risk identification, assessment, monitoring, and controlling process) on the financial performance of DMBs. Also, future studies could consider the impact of the adoption of IFRS on risk management and financial performance of listed DMBs in Nigeria.

Enhancing cryptocurrency market volatility forecasting with daily dynamic tuning strategy

This study proposes a novel parameter tuning strategy, daily dynamic tuning, and applies it to forecast volatility in the cryptocurrency market. Comparative analysis with HAR-RV and machine learning models based on fixed parameters demonstrates the superior predictive performance of the dynamic tuning-based model. These findings are corroborated in robustness tests. Using interpretable tools for machine learning model explanations, this study delves further into the driving factors behind the predictive performance of the dynamic tuning-based model. The results indicate that hash rate and the effective federal funds rate play significant roles among various forecasting factors. Further categorization of the forecasting factors reveals that blockchain indicators and macroeconomic indicators are more useful for predictions, while market indicators are relatively less informative. Moreover, a time-varying analysis of variable importance scores suggests a significant increase in the importance score of blockchain indicators from October 2022 onwards, coinciding with the EU passing landmark legislation on digital assets in October 2022. Finally, an economic value analysis indicates that the dynamic tuning-based model can achieve higher economic returns in practice compared to traditional fixed parameter models. These findings are significant for enhancing cryptocurrency market risk prediction and management and exploring the potential applications of machine learning models in volatility forecasting.