Market Uncertainty Research Articles

Temporal Covariance‐Involved Distributionally Robust Optimization for Photovoltaic–Hydrogen Integrated Energy System Scheduling Considering Source, Load Sides, and Market Uncertainties

Under the pressures of environmental pollution, solar energy and hydrogen have attracted widespread attention, thus promoting the development of photovoltaic–hydrogen integrated energy system. However, multiple uncertainties seriously interfere with the system's economic and stable operation. Therefore, this article proposes a temporal covariance‐involved distributionally robust optimization model for system scheduling considering multiple uncertainties from source, load sides, and market. First, the possible range of the uncertainties distribution is described by a novel moment‐based ambiguity set modified with temporal covariances. Second, the objective function is constructed as a min{sup} form to minimize the expected operation cost under the “worst‐case” distribution in the ambiguity set. Then, the constraints present a two‐stage structure. The first stage guarantees the operation feasibility under the forecasting scenario, and the second stage accounts for forecasting errors to formulate the rescheduling space for resisting uncertainties. Finally, the model is transformed into an easy‐to‐solve semidefinite programming. Simulation finds that the model 1) significantly reduces the average and standard deviation of system operation cost by 22.35% and 27.41%, respectively; 2) ensures 100% photovoltaic accommodation; and 3) maintains low conservativeness and complexity simultaneously. Above all, this study provides an effective approach for comprehensive ultilization of clean energies under multiple uncertainties.

How useful are energy-related uncertainty for oil price volatility forecasting?

Recently, numerous events, such as trade conflicts, the United Kingdom's exit from the European Union, the Russian-Ukrainian war, and the Palestinian-Israeli conflict, have led to increased risks of uncertainty in global economic, political, and energy markets. However, despite the essential role of crude oil markets in national security and economic development, existing studies have paid little attention to how country-level uncertainty affects oil price volatility. Therefore, in this paper, we investigate whether the energy-related uncertainty index (EUI) proposed by Dang et al. (2023) affects oil price volatility through the GARCH-MIDAS framework. We first analyze whether the EUI can play a role in the oil market through parameter estimation of the model and find evidence of information spillovers from the EUI to the oil market. We then use tests to examine the accuracy of the econometric models used in this paper for WTI volatility forecasting. The test results show that the Double Asymmetric GARCH-MIDAS-EUI model has excellent forecasting performance. Therefore, this work is valuable for governments and corporations in formulating energy policies and business strategies to better cope with the risk of market uncertainty.

THE INFLUENCE OF MONETARY POLICY ON THE FORMATION OF OIL PRICES

The relationship between monetary uncertainty and price volatility in the global crude oil market has attracted considerable attention in recent years. Understanding this relationship is extremely important for both policy makers and market participants and investors. The purpose of this article is to explore a modern approach to regime switching in order to shed light on the dynamic interaction between monetary uncertainty and price volatility in the crude oil market. If we consider that oil is one of the main sources of energy in the world, and the price of oil plays an important role in the global economy, then the formation of oil prices depends on many factors, including supply and demand, political stability in the production regions, geopolitical events, climate change and, of course, monetary policy. The scientific significance of the article lies in the fact that it allows for a deeper understanding of the relationship between the economic policy of central banks and the dynamics of commodity prices. The practical significance lies in the fact that understanding the impact of monetary policy on oil prices can be useful for both government agencies and businesses. In this research methodology, an empirical method of work was used, in which the influence of monetary uncertainty on the volatility of world crude oil prices was considered. We also touch upon the issue of political uncertainty on the price of oil during the pandemic. The relevance of this article lies in the fact that the price of oil is one of the key indicators for the global economy, the study of the impact of monetary policy on this market is important. The price of oil can influence monetary policy in various ways. For example, changes in the interest rates of central banks affect investors and their decisions to invest in oil companies. In addition, monetary policy also affects the exchange rate, which also has an impact on the price of oil. In conclusion, it can be said that studying the impact of monetary policy on oil prices is important for both science and practice, and may lead to the development of more effective methods of economic and business management.

Promoting distributed photovoltaic adoption: An evolutionary game model approach for stakeholder coordination.

Distributed photovoltaic (DPV) is a promising solution to climate change. However, the widespread adoption of DPV faces challenges, such as high upfront costs, regulatory barriers, and market uncertainty. Addressing these barriers requires coordinating the interests of stakeholders in the promotion of DPV. Therefore, this paper constructs a three-party evolutionary game model in a social network with the government, investment companies and residents as the main subjects and examines the influence of different subjects' behavioral strategies on the promotion of DPV under the social learning mechanism. The results show that: (1) In the game equilibrium, both the government and residents hold a positive attitude towards the promotion of DPV; (2) Companies will obtain most of the subsidies through market power and information differences, resulting in the increase of government subsidies that do not always benefit residents; (3) The increase of energy consumption and pollution prevention costs can promote companies' investment in DPV; (4) The increase of environmental protection taxes to a certain extent helps companies to take responsibility for promoting DPV, reducing the pressure on the government to promote it and increasing residents' income. This study provides insights into the sustainable development of DPV.

Strategic Planning of Reconfigurable Industrial Systems and Value Chains: A life cycle conceptual model

Strategic planning of industrial systems has become increasingly challenging for decision makers at different production levels, due to everal aspects, such as global competition, market uncertainty and volatility, and changing regulations. These evolving aspects often lead to unplanned and high-effort reconfigurations of systems. Additionally, high-paced innovation in industrial technologies and the diffusion of software, data analytics, and big data technologies in industrial systems, while potentially supporting ever smarter, safer, and greener decisions, also challenge decision makers, as often multi-disciplinary knowledge is required to successfully plan and operate increasingly complex systems. Existing literature provides little guidance on how strategic planning of industrial systems is expected to evolve in order to fully exploit advanced industrial technologies to responsively and cost effectively respond to global challenges. To fill this gap, this paper reviews existing literature on industrial systems’ planning and reconfigurability and derivesmulti-sectorial requirements. Based on the findings, a life cycle conceptualmodel for strategic planning of reconfigurable industrial systems and value chains is provided. Moreover, the concepts of hierarchical interfaces, functional interfaces, and reconfigurability bottleneck are introduced and directions for future research are accordingly outlined.

Исследование влияния индексов неопределенности на волатильность Bitcoin с помощью ARDL модели

The article uses the ARDL model to study the impact of the VIX index and the indices of economic and market uncertainty based on data from the social network X (formerly Twitter, blocked in the Russian Federation) on the volatility of Bitcoin. To estimate the unobservable cryptocurrency volatility, the author uses a nonparametric estimator derived from 5-minute data on Bitcoin closing prices, considering adjustments if there are gaps within the day. The paper considers the data for the period from 02.01.2018 to 30.12.2022, divided into two half-periods: pre-COVID-19 period (from 02.01.2018 to 28.02.2020) and post-COVID-19 period (from 01.03.2020 to 31.12.2022). According to the results obtained by estimating the ARDL model at different half-periods, in the long term, there is a significant negative VIX index impact on the realized volatility of Bitcoin. This paper observed a short-term (instantaneous) significant positive effect on the realized volatility of Bitcoin for the index of market uncertainty TMU_ENG in the pre-COVID-19 period. The identified significant effects make it possible to use the VIX and TMU_ENG indices to improve the forecast quality in models for the volatility of the Bitcoin cryptocurrency.

Robust Reinforcement Learning for Decision Making Under Uncertainty in Electricity Markets

Robust Reinforcement Learning for Decision Making Under Uncertainty in Electricity Markets

Stock price volume leap principle based on investor sentiment contagion model

The rise and fall of the Chinese stock market has been the subject of many studies since its inception. However, the recurring uncertainty of the broad market and individual stocks has overturned many people’s perceptions of the financial market. This paper explores the principles of the rise and fall of individual stocks under certain conditions, starting from Samuelson’s phenomenon of backward bending of the price supply curve for oil and labour, and proposes a methodology for stock market investment based on this principle. The results of the study show that the rise of stock prices is often characterized by a rapid jump. An important factor related to this is the interest rate of private lending. The rise and fall of private lending rates affect the expected return on stock market investment funds and has an important impact on stock market investment. Therefore, in the absence of a qualitative change in the stock’s texture, the medium-term rate of increase is the key to judging the stock price’s later trend. The results of this study provide a new direction to think about when judging the short- and medium-term stock price movements.

Effects of Economic Uncertainty on the Asset Quality of Banking Sector: Evidence from a Developing Country

ABSTRACT: Extant literature investigates on the determinants of NPL (non-performing loan) many a time, however, little is known on the relationship between economic uncertainty and NPL for any developing country. Historically, Bangladesh is suffering from high level of NPL in the banking sector. In addition to that, Bangladesh observes high level of macroeconomic uncertainty as characterized by high and volatile Gross Domestic Products and inflation, continuous exchange rate devaluations, and shallow financial markets. Motivated by this, we investigate the nexus between economic uncertainty and NPL for Bangladesh. Based on the data availability, we use the annual dataset covering 1990–2018. Furthermore, we use autoregressive distributed lag (ARDL) model considering its benefit accommodating both I(0) and I(1) variables. The empirical results show that there is positive relationship between economic uncertainty and NPL in the long run. The results are robust to the alternative measures of economic uncertainty and specifications. Consistent with the expectation, financial development carries a negative sign on the asset quality albeit the effect of economic uncertainty is actually pronounced (both the size and significance changes) in the augmented models. Key policy implication of this study is that government should formulate well anchored, rule-based policies to reduce inflation and interest related uncertainties. With a rule-based policy (for example, inflation targeting), people believe that central bank can achieve its targets. Government should adopt floating exchange rate regime that will adjust the external shocks well via exchange rates. In addition to this, government can monitor, and regulate stock market so that uncertainty in stock market is reduced. Finally, monetary and fiscal policies should be communicated well to the people. Thus, it is possible to reduce asymmetric information among the people, which may reduce economic uncertainty, and thus reduce NPL.

Дефіцит робочої сили в Україні: причини та шляхи подолання

The publication examines and analyzes the main factors of deterioration of the situation in the labor market of Ukraine and focuses on the problem of labor force shortage in conditions of imbalance and uncertainty in the labor market. The subject of the research is the tendency of transition from the excess supply of labor force along with high unemployment, which has formed in the labor market of Ukraine, to the high level of unsatisfied demand for vacancies, which numerous experts assess as a shortage of labor force. The aim of the article is to determine the nature of the problem, causes, degree of acuteness, prospects for development and measures to overcome. The scientific novelty lies in the substantiation of the shortage of labor force as a local phenomenon that has the character of a tendency that is constantly present in the Ukrainian labor market in various quantitative values, individual professional niches and territories. From the methodological point of view, the demand for labor force is analyzed in correlation with the dynamics of tension in the labor market, and in empirical terms, the basis for its study was the information selected according to special criteria from various organizations that are constantly engaged in the recruitment of personnel in the labor market or comparative analyses of labor demand and supply. A comprehensive study of the labor market of Ukraine is carried out and numerous evidences are provided that the labor force shortage, which has been formed for several years under the influence of objective and subjective reasons and exacerbated during the wartime, is threatening, while the country has considerable untapped labor reserves. A set of signs of the real presence of a shortage of labor force has been identified and substantiated. The possibilities and actual ways to overcome the imbalances between the demand for labor force and its supply are shown. In the context of identifying the causes of the labor force shortage, the sources of possible coverage of the personnel shortage have been identified. It is concluded that the key factors in covering the personnel shortage are the reserve sources of labor, considered by the authors according to three aspects: factors of formation, professional structure, and efficiency of use.

The Determinants and Effect of the Incentive Intensity: Empirical Evidence from Japan

This study examines various determinants of incentive intensity and the moderating effect of risk aversion on the relationship between incentive intensity and organisational performance. Prior studies have reported mixed results concerning the determinants of incentive intensity, and few studies have examined the moderation effect. We analysed empirical data from a cross-sectional survey of 600 Japanese organisations that manage foreign subsidiaries. The principal and agency structure can be seen in both participants (HQ and a foreign subsidiary) with the agency problem. The first determinant is environmental uncertainty, which is investigated in two types of uncertainties: market and general business environmental uncertainties. The findings suggest that general business environmental uncertainty is negatively associated with incentive intensity, but the negative effect of market environmental uncertainty on it depends on the prospect of incremental profits. The rest of the determinants are derived from the incentive intensity principle, including some features of management accounting systems. In this study, the effects of the determinants are supported as expected in principle. In particular, the incentive intensity is influenced by the prospect of incremental profits, an agent’s risk preference, and their responsiveness to incentives. As for the moderation effect, the positive effect of the incentive intensity on the performance is decreased by an agent’s risk aversion. Our empirical results explain mixed evidence in previous studies and are consistent with the agency theory.

Construction of Financial Risk Analysis and Early Warning Model for Information Technology Enterprises Based on Complex Networks and LightGBM

Against the backdrop of the rapid rise of the information technology industry and the era of big data, enterprises are facing increasingly complex and diversified financial challenges. These challenges cover the acceleration of technological updates, the increase in financial pressure, as well as the increasingly fierce market competition and uncertainty in financial markets. These factors collectively drive a deeper and more refined demand for financial risk management. In this context, building a model that can accurately predict financial risks has become crucial. By integrating complex network theory and lightGBM algorithm, it is possible to gain a more comprehensive insight into the interrelationships and risk factors between enterprises, thereby improving the accuracy and timeliness of financial risk prediction. This model not only helps enterprises detect potential financial risks in a timely manner, but also provides effective warning and response strategies, thereby enhancing the risk control ability and operational stability of the enterprise. This study is of great significance for risk management in information technology enterprises and the entire industry, and also provides useful ideas and methods for further optimizing future financial risk prediction models.

Financial Asset Allocation and Portfolio Optimization Strategies Based on Stochastic Optimization Methods

Abstract Financial market uncertainty necessitates sophisticated asset allocation and portfolio optimization strategies for enhanced returns and risk mitigation. This study explores stochastic optimization methods to overcome the limitations of traditional models in complex market environments. By employing expected utility theory and the Lagrange multiplier method, we develop an optimization model that facilitates fair equity pricing and enables dynamic allocation of financial assets, outperforming traditional approaches in simulated markets. Specifically, our method yields mean allocations for state-owned and non-state-owned enterprises at 17.4112 and 16.9297, respectively, demonstrating its superiority. Analysis of bank asset portfolios reveals significant improvements in asset structure and yields, underscoring the effectiveness of stochastic optimization in financial asset management, offering substantial benefits in efficiency and investment returns.

US Credit Market Uncertainty Spillovers and Global Business Cycles

US Credit Market Uncertainty Spillovers and Global Business Cycles

Forecasting day-ahead expected shortfall on the EUR/USD exchange rate: The (I)relevance of implied volatility

The existing literature provides mixed results on the usefulness of implied volatility for managing risky assets, while evidence for expected shortfall predictions is almost nonexistent. Given its forward-looking nature, implied volatility might be more valuable than backward-looking measures of realized price fluctuations. Conversely, the volatility risk premium embedded in implied volatility leads to overestimating the observed price variation. This paper explores the benefits of augmenting econometric models used in forecasting the expected shortfall, a risk measured endorsed in the Basel III Accord, with information on implied volatility obtained from EUR/USD option contracts. The day-ahead forecasts are obtained from several classes of econometric models: historical simulation, EGARCH, quantile regression-based HAR, joint VaR and ES model, and combination forecasts. We verify whether the resulting expected shortfall forecasts are well-specified and test the models’ accuracy. Our results provide evidence that the information provided by forward-looking implied volatility is more valuable than that in backward-looking realized measures. These results hold across multiple model specifications, are stable over time, hold under alternative loss functions, and are more pronounced during periods of higher market uncertainty when risk modeling matters most.

Uncertainty-Aware Portfolio Management With Risk-Sensitive Multiagent Network.

As deep neural networks (DNNs) have gained considerable attention in recent years, there have been several cases applying DNNs to portfolio management (PM). Although some researchers have experimentally demonstrated its ability to make a profit, it is still insufficient to use in real situations because existing studies have failed to answer how risky investment decisions are. Furthermore, even though the objective of PM is to maximize returns within a risk tolerance, they overlook the predictive uncertainty of DNNs in the process of risk management. To overcome these limitations, we propose a novel framework called risk-sensitive multiagent network (RSMAN), which includes risk-sensitive agents (RSAs) and a risk adaptive portfolio generator (RAPG). Standard DNNs do not understand the risks of their decision, whereas RSA can take risk-sensitive decisions by estimating market uncertainty and parameter uncertainty. Acting as a trader, this agent is trained via reinforcement learning from dynamic trading simulations to estimate the distribution of reward and via unsupervised learning to assess parameter uncertainty without labeled data. We also present an RAPG that can generate a portfolio fitting the user's risk appetite without retraining by exploiting the estimated information from the RSAs. We tested our framework on the U.S. and Korean real financial markets to demonstrate the practicality of the RSMAN.

Unifying Skill-Based Programming and Programming by Demonstration through Ontologies

Smart manufacturing requires easily reconfigurable robotic systems to increase the flexibility in presence of market uncertainties by reducing the set-up times for new tasks. One enabler of fast reconfigurability is given by intuitive robot programming methods. On the one hand, offline skill-based programming (OSP) allows the definition of new tasks by sequencing pre-defined, parameterizable building blocks termed as skills in a graphical user interface. On the other hand, programming by demonstration (PbD) is a well known technique that uses kinesthetic teaching for intuitive robot programming, where this work presents an approach to automatically recognize skills from the human demonstration and parameterize them using the recorded data. The approach further unifies both programming modes of OSP and PbD with the help of an ontological knowledge base and empowers the end user to choose the preferred mode for each phase of the task. In the experiments, we evaluate two scenarios with different sequences of programming modes being selected by the user to define a task. In each scenario, skills are recognized by a data-driven classifier and automatically parameterized from the recorded data. The fully defined tasks consist of both manually added and automatically recognized skills and are executed in the context of a realistic industrial assembly environment.

Presenting the Interactive Control System Model in Knowledge-Based Companies

The aim of this study is to present a model of the interactive control system in knowledge-based companies. In the current research, three Type II knowledge-based manufacturing companies active in the field of advanced machinery and equipment production in East Azerbaijan Province were studied. Accordingly, this research follows a case study strategy, with data collection conducted from a defined statistical population. Additionally, the research employs a developmental research strategy. For the analysis of data obtained from questionnaires, the coding procedure, in line with statistical requirements, was conducted using the SPSS.V24 software, and the PLS.V3 structural equation software was used for both descriptive and inferential statistics, applying linear and multiple regression. The statistical population of this research includes managers of Type I and Type II knowledge-based manufacturing companies and faculty members from the University of Tehran's Department of Business Management, particularly those specializing in policy-making. The sampling method used in this study was snowball sampling. The results of the research indicate that regulatory uncertainties significantly impact the interactive control system of projects (0.896), technological uncertainties impact the interactive control system of projects (0.140), human and social uncertainties impact the interactive control system of projects (0.225), economic and market uncertainties impact the interactive control system of profit planning (0.476), environmental uncertainties impact the interactive control system of brand profitability (0.643), and strategic uncertainties impact the interactive control system of intelligence (0.821).

Twitter Market Uncertainty and Risk-Neutral Tails

Twitter Market Uncertainty and Risk-Neutral Tails

Impact of Movement Control Order on Risk-Adjusted Performance of the Malaysian Real Estate Investment Trusts (M-REITs)

The first Movement Control Order (MCO) implemented by the Malaysian government from 18th March 2020 to 3rd May 2020 was unprecedented. Since Malaysia was only mildly affected by previous epidemics or pandemics unlike the COVID-19, studies of the pandemic impact on the performance of Malaysian firms and M-REITs in specific were limited. To close this gap, this study compares Sharpe ratio, Treynor ratio and Jensen Alpha ratio of 18 M-REITs for sub-periods before, during and after the MCO. Paired sample t-test results for all these three risk-adjusted performance measures consistently show that M-REITs significantly performed better during the MCO compared to before the MCO as well as after the MCO compared to before the MCO. The results suggest that the business model and portfolio of real estate managed by M-REITs are resilient against both systematic and unsystematic risk factors, and imply that the intrinsic value of M-REITs is not significantly affected by the market uncertainty caused by movement restriction. The findings also bolster investors’ confidence to include M-REITs as part of their diversified investment portfolio to achieve sustainable return performance in the post-Covid period. To remain resilient and sustainable, the management of M-REITs should diversify the portfolio of properties and real estate they managed since the movement restriction had varying repercussions on different types of real estate. Rooms for further diversification are justified by a larger percentage of M-REITs having positive Treynor ratios after the MCO sub-period if compared to positive Sharpe ratios. A well-diversified portfolio managed by an M-REIT can reduce unsystematic risk, which is part of the total risk measured by standard deviation, eventually moving towards a positive Sharpe ratio.