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This paper explores the typologies of risks in the banking sector and examines the strategies employed to mitigate them, with a focus on Azerbaijani banks. The study identifies key risk categories, including credit risk, market risk, liquidity risk, and operational risk, each of which plays a critical role in the financial health and profitability of banks. The research highlights that credit risk, driven by loan defaults, remains the most significant concern for Azerbaijani banks, while market risks, particularly from currency fluctuations, also pose major challenges. Effective risk mitigation strategies, including collateralization and diversified lending practices, are essential to maintaining stability in this emerging market. Additionally, the study emphasizes the importance of integrating modern risk management technologies and adhering to international regulatory standards. Azerbaijani banks that adopt advanced monitoring systems and proactive risk management frameworks demonstrate improved financial performance and resilience in the face of economic volatility. The findings underscore that well-implemented risk management practices are crucial for enhancing profitability and ensuring long-term sustainability in the banking sector. [ABSTRACT FROM AUTHOR]

In the face of global climate change and the collision of consumer preferences towards green and low-carbon, businesses need to accelerate the transition to sustainable development to achieve long-term growth. Companies must raise significant funds to support this transition and manage high market risk. The existing research on green innovation within supply chains often overlooks market risks, particularly those associated with incomplete information. Hence, this paper considers a two-echelon supply chain system composed of a manufacturer and a retailer. Manufacturers are willing to carry out green innovation and make a single product for sale in the consumer market with green preferences. However, innovation is risky due to the uncertainty in the sales volume of green products. In addition, the manufacturer may lack internal capital to invest in the innovation activities and may seek external financial resources, e.g., bank loans or retail prepayment financing. Hence, the manufacturer and retailer must decide which financial option to adopt. The results show that when the market risk is high, the supply chain members tend to make conservative decisions, no matter which financial modes they choose. However, with the robust optimization approach, the manufacturer and the retailer may earn a higher profit when the market risk is high. When the prepayment rate and bank loan interest rate are equal, regardless of the market risk, the manufacturer's optimal decision is to choose prepayment financing from the retailer. However, when the prepayment rate is higher than the bank loan interest rate, there is no dominant strategy for the manufacturer to choose. [ABSTRACT FROM AUTHOR]

Although small and medium-sized enterprises (SMEs) play a vital role in countries' entrepreneurship and export activities, their lack of financial assets and human resources makes them more vulnerable to difficult-to-control external risks, including financial, legal, and market risks. Since the exposure to these risks might reduce their willingness to export, SMEs' risk management capabilities included in the dynamic capabilities of Resource-based View (RBV) might enable them to reduce their exporting concerns and stimulate their entrepreneurial spirit. In this regard, this paper purposes to investigate the impacts of financial, legal, and market risk management capabilities on export intentions of SMEs. The effects of risk management capabilities on the export intention of SMEs might differ depending on the countries where SMEs operate because locally specific government bureaucracy, documentation, standards, regulations, and cultural values make companies face various circumstances that might differently affect their management of multiple risk factors and export entrepreneurship. Thus, this paper also aims to determine whether the effects of risk management capabilities on the export intention of SMEs differ depending on their country of origin. In line with the research aims, this paper employs the Binary Logistic Regression Test to analyze 1221 SMEs in various countries. The results indicate that while financial risk management does not have any significant impacts on the export intention of SMEs, the effects of legal and market risk management on export intention differ depending on SMEs' country of origin. This paper explains the reasons for those results by the institutional factors. [ABSTRACT FROM AUTHOR]

The Chinese government has made the realization of sustainable development in grassland animal husbandry an important policy objective, and achieving a reasonable input of production factors is the key to realizing that goal. Based on the assumption of "rational economic man", this study measures the economically optimal inputs and actual input bias of production factors, and constructs an econometric model focusing on analyzing the impact of operation scale on the factor input bias. The results indicate that herdsmen deviate from the economically optimal production input levels in forage, labor, and machinery, with the degree of bias decreasing as the livestock size or pasture size expands. Furthermore, it is established that market risk plays a role in mediating the impact of operation scale on the bias of variable production factors. Overall, large-scale herding households have a smaller bias in factor inputs, and should be promoted to operate on an appropriate scale, while paying attention to the prevention of market risk and the enhancement of information symmetry between herders and factor markets. [ABSTRACT FROM AUTHOR]

This paper presents a method to identify causal interactions between two time series. The largest eigenvalue follows a Tracy-Widom distribution, derived from a Coulomb gas model. This defines causal interactions as the pushing and pulling of the gas, measurable by the variability of the largest eigenvalue's explanatory power. The hypothesis that this setup applies to time series interactions was validated, with causality inferred from time lags. The standard deviation of the largest eigenvalue's explanatory power in lagged correlation matrices indicated the probability of causal interaction between time series. Contrasting with traditional methods that rely on forecasting or window-based parametric controls, this approach offers a novel definition of causality based on dynamic monitoring of tail events. Experimental validation with controlled trials and historical data shows that this method outperforms Granger's causality test in detecting structural changes in time series. Applications to stock returns and financial market data show the indicator's predictive capabilities regarding average stock return and realized volatility. Further validation with brokerage data confirms its effectiveness in inferring causal relationships in liquidity flows, highlighting its potential for market and liquidity risk management.

Asset pricing models are widely applied for explaining variations in stock returns. The applicability of these models is tested on different markets for assessing different stock price anomalies. In this paper, Fama and French three-factor model and Carhart Model were applied to the KSE-100 Index, over the period of 2004 to 2019. Following the FF 3 factor methodology, we create a relatively large number of portfolios based on size, value, and momentum, whereas the existence of the momentum factor was checked through the Carhart model. The results indicate that, out of 25 portfolios, 15 were able to explain the variations in stock returns, which shows 60% efficiency of the Carhart model compared to the FF 3 factor model with 56% efficiency whereby only 14 portfolios were able to explain the variations in the stock returns. The momentum factor is thus evident from the results, whereas the value factor is found to be redundant. Our findings suggest that while projecting stock prices, financial experts and analysts should not ignore the momentum factor as by doing so there may be chances of underpricing or over-pricing of stock.

Electricity retailers in an electricity market use over-the-counter (OTC) contracts, or bilateral, and spot market purchases to meet the energy demands of their users. In some markets, OTC contracts face issues with price discrimination and accessibility. This study reveals some inefficiencies of OTC contracts in Colombia that expose regulated users—approximately 70% of the national demand—to market risk. This risk is aggravated by the current tariff design. To mitigate these inefficiencies, this article proposes the incorporation of a market maker that will improve the liquidity of existing energy futures in the country. These futures are mechanisms that the retailers could implement to hedge their demand and reduce the adverse effects of market risk. The characteristics of the market maker and a quantitative analysis of its impact are developed in this paper. While the characterization of the problem with its solution is developed with Colombian data, the conceptual framework could be extended to other countries that are concerned about how energy users are being affected by increases in tariffs due to high exposure to spot market price volatility. [ABSTRACT FROM AUTHOR]

The public's confidence in insurance companies has been damaged by state-owned insurance companies' inability to pay claims. The purpose of this research is to investigate how financial risk affects business performance and how hedge accounting mitigates that influence. This study uses a quantitative approach. The analysis makes use of secondary data from 2019 to 2022 from insurance firms' annual reports. Both moderated regression analysis (MRA) and multiple linear regression analysis are used in this investigation. The findings show that return on assets (ROA) is significantly and negatively impacted by credit risk. Return on assets is unaffected by market, insurance, liquidity, and operational risks. The MRA analysis demonstrates how hedge accounting can increase the impact of risk associated with credit and insurance returns on assets. Hedge accounting, however, has no effect on how much market, operational, and liquidity risk affect return on assets. [ABSTRACT FROM AUTHOR]

The Financial Risk Meter (FRM) employs Quantile-LASSO regression to identify systemic financial risk and dependencies among tail events across financial assets. This paper establishes, both theoretically and empirically, a meaningful economic relationship between the FRM index, derived from the penalization parameter in quantile LASSO regression, and the volatility of assets' pricing kernels, the attainable maximal Sharpe ratio, and market volatility. Despite the rapid growth of the crypto market and its increasing integration with traditional financial markets, there remains a dearth of risk measures in this space. $ FRM@Crypto $ FRM @ Crypto exhibits robust predictive capabilities in anticipating future market risk, potentially filling a critical void in this market. [ABSTRACT FROM AUTHOR]

This paper presents a method to identify causal interactions between two time series. The largest eigenvalue follows a Tracy-Widom distribution, derived from a Coulomb gas model. This defines causal interactions as the pushing and pulling of the gas, measurable by the variability of the largest eigenvalue's explanatory power. The hypothesis that this setup applies to time series interactions was validated, with causality inferred from time lags. The standard deviation of the largest eigenvalue's explanatory power in lagged correlation matrices indicated the probability of causal interaction between time series. Contrasting with traditional methods that rely on forecasting or window-based parametric controls, this approach offers a novel definition of causality based on dynamic monitoring of tail events. Experimental validation with controlled trials and historical data shows that this method outperforms Granger's causality test in detecting structural changes in time series. Applications to stock returns and financial market data show the indicator's predictive capabilities regarding average stock return and realized volatility. Further validation with brokerage data confirms its effectiveness in inferring causal relationships in liquidity flows, highlighting its potential for market and liquidity risk management. [ABSTRACT FROM AUTHOR]

This paper aims to find an answer to the questions of "whether chief executive officers (CEOs) are compensated for market risk", and "how the combined interaction of board size and CEO duality moderates this relationship" from the tenets of agency theory and managerial power theory. Even though the contracting view of agency theory posits that agents are neither to be punished nor rewarded for events that go beyond their direct control (market risk), the research findings in the corporate governance domain are contradictory. It was found that Chinese and American executives were paid for market risk, including oil prices and exchange rates, which was explained by retention risk and weaker corporate governance systems. To shed light on previous inconclusive research, this paper investigates the topic further in a new country setting, that of Switzerland, because the previous results were mostly related to Anglo-Saxon countries. Switzerland is also one of the exemplary countries for executive compensation. Furthermore, it investigates the combined (cascaded) interaction effects of the board size and CEO duality on CEO compensation and market risk from the perspective of managerial power theory, which has not been previously analyzed in the literature to date. For the direct effect, in contrast to previous findings in Anglo-Saxon countries, it has been found that CEOs were not paid for market risk in Switzerland, which confirms agency theory's contracting prediction. This finding outlines the future comparative research area in this domain. For the combined interaction effect, it has been found that board size incorporated with CEO duality is the significant cascaded moderator, and large boards with CEO duality are significantly more effective in controlling asymmetric compensation, which confirms the efficacy of large boards with CEO duality in coping with asymmetric compensation and managerial entrenchment (managerial power theory). These results have both practical and academic implications for boards of directors, Human Resources and corporate governance literature, agency theory, and managerial power theory, by providing further evidence on previous inconclusive findings on board size, CEO duality, and the role of market risk in the CEO pay structure. [ABSTRACT FROM AUTHOR]

Gujarat is known for its French fries potatoes in India. This state is number one in potato productivity. It provides an amicable business environment for producers and processors for contract farming. To understand potato production practices, 180 contract farmers were studied in different villages of northern Gujarat after doing multistage sampling from districts to Talukas and then villages. Data were analysed through frequencies, percentages, and Garrett ranking techniques. It was found that potato contract farming was more centric towards larger producers although processors screened out their preferred producers on the basis of a number of parameters, like land holding capacity, business sense, youthfulness, and positive attitude. Potato contract farming in the state was more informal than formal. The processors provided seed and in time consultancy support to the producers. Higher input cost was the major reason for breaching the contract. Any conflict in this direction was solved through a participatory approach. Ultimately, lack of labour and credit issues made contract farming challenging for the producers that need to be nullified by the processors. [ABSTRACT FROM AUTHOR]

• Commodity investments promote sustainable markets. • Climate-transition strategies have bi-directional causality with sustainable markets. • Long-only commodity strategies mitigate market risk for regenerative markets. • Global sustainability leaders connect with climate-transition strategies in commodities. • Integrating climate-transition strategies fosters sustainable investment practices. By allocating investments towards commodities that align with climate-transition goals, environmentally conscious commodity investment strategies serve to promote and support sustainable markets, channeling capital towards sectors that prioritize environmental sustainability. Through the application of a quantile causality test, which examines the relationship between commodity-based strategies with a climate-transition focus and eco-friendly markets, over the period spanning from May 1, 2013, to May 25, 2023, our findings reveal a bi-directional causality relationship between different themes of sustainable markets and long-only climate-transition strategies in the commodity market across various market conditions. Furthermore, employing a quantile time-frequency connectedness approach allows us to discern that long-only climate-transition strategies in the commodity market exhibit lower long-run total connectedness with responsible and conscious markets compared to the short term. Consequently, these results suggest that transition-oriented strategies for commodities in a climate-conscious world not only mitigate market risk for regenerative markets in the long run but also indicate that different types of global sustainability leaders demonstrate a stronger connectedness with climate-transition strategies in commodities when compared to the Islamic sustainable market across a majority of quantiles and time horizons. In light of these findings, policymakers are urged to prioritize the long-term dimensions of climate-transition strategies in commodity markets by implementing new emission standards and environmental benchmarks. Additionally, the design and implementation of similar long-only climate-transition strategies in other markets would further enhance the long-term effectiveness of climate-conscious markets and foster stronger connections with responsible markets. our study underscores the significance of integrating climate-transition strategies into commodity markets and highlights their role in promoting sustainable and environmentally conscious investment practices. By directing investments towards climate-aligned commodities, policymakers and market participants can contribute to the long-term sustainability of global markets while fostering stronger connections between sustainable markets and climate-transition strategies in commodities. [ABSTRACT FROM AUTHOR]

This study analyzes 23,292 TikTok short-video stock recommendations from 2022 to explore their impact on stock market performance using the Difference-In-Differences Model. Key findings include: (1) short video recommendations provide valuable information, significantly boosting stock price discovery; (2) using a large language model (ChatGPT), it was found that the impact of clustered recommendations on stock price discovery is enhanced when considering recommendation strength. This pioneering research offers insights into the relationship between short-video stock recommendations and abnormal stock returns, benefiting researchers, investors, corporations, and social media analysts. [ABSTRACT FROM AUTHOR]

This study aims to examine the effect of investor sentiment on stock market crash risk in the Asia–Pacific region. The research employs principal components analysis (PCA) to construct an investor sentiment index, while the Method of Moments Quantile Regression (MMQR) is used to analyze monthly data of 16 Asia-Pacific stock markets. The findings show that investor sentiment positively impacts on crash risk in the middle to higher quantities. Moreover, regional sentiment significantly increases stock market crash risk, particularly at higher quantiles, while local sentiment generally reduces crash risk at the lower to middle quantiles. Besides, the magnitude and direction impact of investor sentiment on stock market crash risk is heterogeneous across market levels. Specifically, the results indicate that at higher quantiles of risk, investor sentiment increases crash risk in developed and emerging markets, while it decreases crash risk in frontier markets.

This paper proposes the time‐heterogeneous Student's t autoregressive model as an alternative to the various volatility forecast models documented in the literature. The empirical results indicate that: (i) the proposed model has better forecasting performance than other commonly used models, and (ii) the problem of reliable risk measurement arises primarily from the model risk associated with risk forecast models rather than the particular risk measure for computing risk. Based on the results, the paper makes recommendations to regulators and practitioners. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Research in Humanities is the property of Tarbiat Modares University Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Gharee for Economics & Administration Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper evaluates the relationship between investing in workforce well-being and profitability of listed companies in Mexico compared to European companies from an Environmental, Social, and Governance (ESG) investor perspective. In this case, the Refinitiv workforce score or High-Performance Work Policies (HPWP) is used as an indicator of the quality of workforce well-being by including the industry effects (economic and business sectors) and the behavioral (sentiment) factors as control variables. Specifically, this article examines the relationships between HPWP, stock price changes (measured as a percentage), profitability (ROE), and market risk (betas). We used a sample of companies from the Refinitiv Mexico and European stock indices for this purpose. In the Mexican case, the results show that a higher level of well-being promotion relates to better company profits. The opposite happens in European companies. Regarding market prices, European companies show higher prices when they have higher HPWP and Mexican companies confirm the opposite. Regarding market risk, only European basic materials with high HPWP show less risk. Finally, in almost all Mexican business sectors, the relationship between market risk and workforce well-being is negative. [ABSTRACT FROM AUTHOR]

For some time now, Solvency II requires that insurance companies calculate minimum capital requirements to face the risk of insolvency, either in accordance with the Standard Formula or using a full or partial Internal Model. An Internal Model must be based on a market-consistent valuation of assets and liabilities at a 1-year time span, where a real-world probabilistic structure is used for the first year of projection. In this paper, we describe the major risks of a non-life insurance company, i.e. the non-life underwriting risk and market risk, and their interactions, focusing on the non-life premium risk, equity risk, and interest rate risk. This analysis is made using some well-known stochastic models in the financial-actuarial literature and practical insurance business, i.e. the Collective Risk Model for non-life premium risk, the Geometric Brownian Motion for equity risk, and a real-world version of the G2++ Model for interest rate risk, where parameters are calibrated on current and real market data. Finally, we illustrate a case study on a single-line and a multi-line insurance company in order to see how the risk drivers behave in both a stand-alone and an aggregate framework. [ABSTRACT FROM AUTHOR]

This research endeavors to assess airline executive decision-making coping with critical crises during the pandemic by scrutinizing the Market Risks and Management Discussion and Analysis (MD&A) sections within the 10-K financial reports. This study delves into the quarterly financial reports (10-Q) of Delta Air Lines (DAL) spanning from the 1st quarter (Q1) of 2020 to the second quarter (Q2) of 2023 and discovered primary challenges and market risks that were of concern to top executives. To underscore the significance of top executives' decision-making activities within a chosen airline, this research project explores significant discussions disclosed in the financial reports. The findings show "revenue", "cost", and "fleet" were three most discussed topics while "liquidity, asset, and payable accounts", "failing cost-saving strategies and poor Return on Investment (ROI)", "policy changes in ticket cancellation and frequent flyer program", "failing new technology", and "proprietary data security" emerged as the top five market risks during the COVID-19 pandemic. Executives responded to these challenges by implementing corresponding strategies aimed at ensuring sustainability, such as labor downsizing, reforms to retirement plans, flight frequency reduction, simplification of aging fleets, cancellation of aircraft orders, cost-reduction initiatives, and adjustments to routes and hubs, among other measures. Noticeably, frequent flyer loyalty program remained lucrative. [ABSTRACT FROM AUTHOR]

We propose a method for characterizing the local structure of weighted multivariate time series networks. We draw intensity and coherence of network motifs, i.e. statistically recurrent subgraphs, to characterize the system behavior via higher-order structures derived upon effective transfer entropy networks. The latter consists of a model-free methodology enabling to correct for small sample biases affecting Shannon transfer entropy, other than conducting inference on the estimated directional time series information flows. We demonstrate the usefulness of our proposed method with an application to a set of global commodity prices. Our main result shows that, despite simple triadic structures are the most intense, coherent and statistically recurrent over time, their intensity suddenly decreases after the Global Financial Crisis, in favor of most complex triadic structures, while all types of subgraphs tend to become more coherent thereafter. [ABSTRACT FROM AUTHOR]

This research aims to prove whether firm size and market risk based on CAPM affect return reversal anomalies as indicators of market overreaction during the COVID-19 pandemic. This explanatory research used a sample of stocks on the Indonesia Stock Exchange (IDX) that could be profitable during the pandemic period up to the effective period of phase one and two vaccination. It was found that return reversal anomalies occurred in the short term on the IDX, and contrarian strategies resulted in profits. Factors of firm size and market risk affected the reversal of returns in specific periods but did not affect other periods. When firm size and market risk had no effect, the return reversal anomaly occurred entirely due to the investors' overreaction in response to the pandemic without regard to the size and market risk factors of companies whose stocks were the investment target.

Cryptocurrency, a novel digital asset within the blockchain technology ecosystem, has recently garnered significant attention in the investment world. Despite its growing popularity, the inherent volatility and instability of cryptocurrency investments necessitate a thorough risk evaluation. This study utilizes the Autoregressive Moving Average (ARMA) model combined with the Generalized Autoregressive Conditionally Heteroscedastic (GARCH) model to analyze the volatility of three major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), and Binance Coin (BNB)—over a period from January 1, 2017, to October 29, 2022. The dataset comprises daily closing prices, offering a comprehensive view of the market's fluctuations. Our analysis revealed that the value-at-risk (VaR) curves for these cryptocurrencies demonstrate significant volatility, encompassing a broad spectrum of returns. The overall risk profile is relatively high, with ETH exhibiting the highest risk, followed by BTC and BNB. The ARMA-GARCH-VaR model has proven effective in quantifying and assessing the market risks associated with cryptocurrencies, providing valuable insights for investors and policymakers in navigating the complex landscape of digital assets.

Purpose: The purpose of this study is to analyse the effect of COVID-19 on the risk factors affecting the South African bond market. The global economy has resulted in a couple of total shutdowns in 2020 to minimise the spread of the COVID-19 virus. This has resulted in significant adjustments in monetary and fiscal policies to address the impact on the fiscus. South Africa also adopted a couple of adjustments which saw a drastic spike in the nominal debt issued to fund the increased budget shortfall. This came immediately after South Africa's exit from the World Government Bond Index after being rated sub-investment by all three major rating agencies. Design/Methodology/Approach: The study takes inferences from experiences from some leading emerging markets with the same attributes as South Africa. The study further looks at graphical presentation and descriptive statistics for data analysis. Findings: It was found that, even though South Africa is still well below the risk measures for debt management, the quantum of debt has increased significantly, thus putting pressure on the fiscus in absolute terms. Paper Type: Research paper.

Asset pricing models are widely applied for explaining variations in stock returns. The applicability of these models is tested on different markets for assessing different stock price anomalies. In this paper, Fama and French three factor model and Carhart Model were applied on the KSE-100 Index, over the period of 2004 to 2019. Following the FF 3 factor methodology, we create a relatively large number of portfolios based on size, value and momentum, whereas the existence of momentum factor was checked through Carhart model. The results indicates that, out of 25 portfolios, 15 were able to explain the variations in stock returns, which shows 60% efficiency of the Carhart model compared to the FF 3 factor model with 56% efficiency whereby only 14 portfolios were able to explain the variations in the stock returns. The momentum factor is thus evident from the results, whereas the value factor is found to be redundant. Our findings suggest that while projecting stock prices, financial experts and analysts should not ignore the momentum factor as by doing so there may be chances of underpricing or over-pricing of stock returns. [ABSTRACT FROM AUTHOR]

We identify factors influencing farmers' decision‐making on various production contracts and are explicitly concerned with whether managing market risk or profit orientation promotes contract farming (CF). After controlling for potential endogeneity, the IV‐Tobit regression results indicate that farmers' risk behavior and profit orientation are vital factors driving CF participation decisions. However, we observed that the impact of profit orientation is relatively more substantial than the risk management motive, suggesting that earning a higher profit, rather than managing market risks, is the primary objective of CF adoption. In addition, other factors such as farm size, mean contract price, education, age, and extension services play a significant role in CF participation. The major policy implications, based on results, call for enhancing the CF network and encouraging farmers to commercialize agriculture as it facilitates access to the market and higher profits. Further, agribusiness firms should share more market risks with farmers to invite risk‐averse smallholders into the fold of commercial farming. [ABSTRACT FROM AUTHOR]

Market risk is one of the risks in Islamic banking that cannot be avoided. If Islamic banking cannot manage market risk properly, it will have an impact on bank profitability or even lead to other risks. Therefore, Islamic banking must create a market risk management strategy to achieve sustainable business growth. This research made Bank Mega Syariah the locus of research. The research method used is qualitative research using the main data source, namely Bank Mega Syariah's 2023 annual report as well as the December 2023 risk exposure and capital report. The results of the research show that Bank Mega Syariah manages market risk based on its fund distribution portfolio, the majority of which is in the form of financing and partly in the form of sukuk securities as a liquidity reserve, does not carry out over the counter trading activities for proprietary trading so market risk exposure is relatively low. The focus is on changing behavior and maintaining customer trust when market benchmark interest rates increase, adjusted to the majority of financing which has a fixed margin. [ABSTRACT FROM AUTHOR]

In this article, the market risk associated with the financial markets of New York and Colombia is evaluated in three periods belonging to the 2019-2020-time window, characterized by shocking economic and social conditions such as the oil price war between Saudi Arabia and Russia and the global pandemic by COVID-19. Risk measurement is carried out using the value at risk (VaR) and Median Shortfall (MS), applying a statistical methodology that considers the use of parametric and non-parametric resampling techniques (Bootstrapping). Data from five indices (Standard and Poor's 500, Dow Jones, COLCAP, VIX and Brent) were taken in order to evaluate the effects caused by variables such as the price of oil and the conditions generated by the COVID-19 pandemic on the dates of study, as the main result it is obtained that in general there is a very high volatility in the periods affected by the two aforementioned phenomena when they occurred simultaneously, and that in addition to large falls in the reference indices, there is also evidence of large recoveries that contribute positively to the trend in prices. [ABSTRACT FROM AUTHOR]

This paper presents a new Expected Shortfall (ES) model based on the Quantum Harmonic Oscillator (QHO). It is used to estimate market risk in banks and other financial institutions according to Basel III standard. Predictions of the model agree with the empirical data which displays deviations from normality. Using backtesting, it is shown that the model can be reliably used to assess market risk. [ABSTRACT FROM AUTHOR]

Financial market instability and losses driven by changes in stock prices, currencies, interest rates, and other factors are the primary causes of economic risk. One of the risk types with the highest priority for every business is financial risk. The consumer electronics manufacturing sector's focus on rising technology is driving important growth and includes manufacturers of smartwatches, stylish home products, and smart speakers. Risks can arise from the inability to meet functional requirements and business expectations throughout the life cycle, from original formation to final disposal, while supplying competitive electronic products. All of this highlights the necessity and potential of thorough study in the field of financial risk in economic growth. With the help of owners and managers of top electronic manufacturing industries in India, this study's goal is to examine and evaluate several aspects of financial risk in economic benefits. The main factors of the financial risk covered under the study include liquidity risk, market risk, credit risk, and operational risk. Financial risks also arise from a combination of macroeconomic factors, including changing interest rates on the market and the potential for default by sizable businesses or industries. Financial stability is of the utmost importance to a commercial enterprise to maintain its position and status in the commercial environment. All of this demonstrates the value and need for rigorous research in the area of financial risk affecting the performance of the organization. This study intends to analyze several components of financial risk in consumer electronic goods manufacturers in India. Various aspects discussed in the study revolving around financial risk management are an important factor and demand the maximum attention of the organization. [ABSTRACT FROM AUTHOR]

Amidst the global push for decarbonization, green hydrogen has gained recognition as a versatile and clean energy carrier, prompting the financial sector to introduce specialized investment instruments like Green Hydrogen Exchange-Traded Funds (ETFs). Despite the nascent nature of research on green hydrogen portfolio performance, this study examines two key green hydrogen ETFs (i.e., HJEN and HDRO) from April 2021–May 2023, aiming at conducting a multifaceted exploration of their performance, isolating and measuring their sensitivity to the primary market factor, and assessing the capabilities of systematic trading strategies to preserve capital and minimize losses during market downturns. The results spotlight lower returns and higher risks in green hydrogen investments compared to conventional equity (proxied by ETFs offering exposure to developed markets—EFA and emerging markets—EEM) and green energy portfolios (proxied by the ETF ICLN). To comprehensively evaluate performance, an array of risk-adjusted metrics, including Std Sharpe, ES Sharpe, VaR Sharpe, Information ratio, Sortino ratio, Treynor ratio, and various downside risk metrics (historical VaR, modified VaR, Expected Shortfall, loss deviation, downside deviation, and maximum drawdown) are employed, offering a nuanced understanding of the investment landscape. Moreover, single-factor models highlight significant systematic market risk, reflected in notably high beta coefficients, negative alphas, and active premia, underscoring the sensitivity of green hydrogen investments to market fluctuations. Despite these challenges, a silver lining emerges as the study demonstrates the efficacy of implementing straightforward Dual Moving Average Crossover (DMAC) trading strategies. These strategies significantly enhance the risk-return profile of green hydrogen portfolios, offering investors a pathway to align financial and social objectives within their equity portfolios. This research is motivated by the need to provide market players, policymakers, and stakeholders with valuable insights into the benefits and risks associated with green hydrogen investment, considering its potential to reshape the global energy landscape. [ABSTRACT FROM AUTHOR]

The paper has emphasized on the downside potential of the stock market faced by G-7 countries in the times of COVID-19 relative to other economic crises. The results of VaR models, ES, and correlation suggests that most of the nations in G-7 group experienced highest risk during COVID-19 relative to other regimes and also increased inter-linkage of different markets within the group is visible during this period. The work can definitely be a reference to the investors for taking investment decisions as well as the governments and regulators for framing policies to keep the market stable by clinging to the policies of those markets which has managed to stay stable even at turbulent times. Moreover, the group as a whole can also rethink of policy measures together to beat the crisis. [ABSTRACT FROM AUTHOR]

Machine learning has permeated almost all areas in which inferences are drawn from financial data. Nevertheless, in financial market risk measurement most machine learning techniques struggle with some inherent difficulties: Financial time series are very noisy, not stationary and mostly considerably short. This paper contains an easy to implement sequential learning algorithm that overcomes some of these disadvantages. It is based on a Kalman filtering mechanism for quite general stochastic processes and provides a first step in the direction of separating parameter dynamics from the ubiquitous noise component. The core idea here is to use some stylised facts inherent to financial markets time series such as time varying measures of volatility. The new approach is tested using real market data in two different settings. First, a hypothetical portfolio containing credit spread and equity risk is analysed over a time frame containing the outbreak of the global pandemic in 2020 and the beginning of the Russian attack on Ukraine in 2022. Another analysis is focused on US$/EUR exchange rate during a time span containing the global financial crisis of 2008 and the subsequent European sovereign crisis. In all test calculations the proposed sequential learning algorithm performs better than the historical simulation approach used by many firms in the banking industry to meet regulatory capital requirements. Due to its simplicity this method has a high degree of explainability and interpretability which will decrease the inherent model risk. The paper concludes with a discussion of model risk for machine learning in financial institutions. Compared to classical model risk frameworks, the emphasis must be put on the more prominent role of data. The simple approach described in this paper shows that machine learning in financial market risk does not have to get lost in noise. [ABSTRACT FROM AUTHOR]

We use granular information on securities holdings from 2008 to 2021 to estimate the market risk of Italian bank securities portfolios. The market risk is measured by the value-at-risk and the expected shortfall. The main advantages of our approach are the following: (1) profits and losses are computed through simple operations and without the need of complex calibration algorithms; (2) we are able to incorporate all market data available in Refinitiv; and (3) the risk measures can be estimated for all banks located in Italy, irrespective if the bank has validated internal models for market risk or not. Finally, we conduct an econometric analysis to identify the main drivers of market risk and to perform a forecasting exercise. [ABSTRACT FROM AUTHOR]

Electricity retailers in an electricity market use over-the-counter (OTC) contracts, or bilateral, and spot market purchases to meet the energy demands of their users. In some markets, OTC contracts face issues with price discrimination and accessibility. This study reveals some inefficiencies of OTC contracts in Colombia that expose regulated users—approximately 70% of the national demand—to market risk. This risk is aggravated by the current tariff design. To mitigate these inefficiencies, this article proposes the incorporation of a market maker that will improve the liquidity of existing energy futures in the country. These futures are mechanisms that the retailers could implement to hedge their demand and reduce the adverse effects of market risk. The characteristics of the market maker and a quantitative analysis of its impact are developed in this paper. While the characterization of the problem with its solution is developed with Colombian data, the conceptual framework could be extended to other countries that are concerned about how energy users are being affected by increases in tariffs due to high exposure to spot market price volatility.