Price Volatility Research Articles

Developing and implementing AI-driven models for demand forecasting in US supply chains: A comprehensive approach to enhancing predictive accuracy

Demand forecasting has long been a critical challenge in the US supply chain operations, plagued by disruptions, fluctuating demand, and price volatility. Developing and implementing AI models that can accurately predict demand is essential in response to these issues. This study aimed to investigate the feasibility of applying machine learning techniques to demand forecasting, particularly in supply chain operations. A comprehensive analysis was conducted using historical data from a logistics company in the USA, which was used to train five traditional demand forecasting methods: Linear Regression, ElasticNet, Random Forest, MLPRegressorn, and XGBoost. Additionally, feature selection, data normalization, and dimensionality reduction techniques were employed to improve the accuracy of these models. Strategic metrics were used to evaluate the model's performance: Random Mean Squared Error, Mean Absolute Error, and R-squared score. The results of this study indicate that AI models have shown significant promise in predicting target sales in supply chains with Linear Regression emerging as the most effective model with the lowest RMSE, MAE, and an R-squared score close to 1. Practical implications of implementing such AI models in the US supply chain include optimized inventory management, reduced costs, and enhanced customer satisfaction. This research contributes to the existing body of knowledge on AI applications in demand forecasting, highlights the importance of traditional methods being supplemented by machine learning techniques, and provides practical recommendations for businesses seeking to improve their supply chain operations.

On RBL-STEM Learning Activities: The Use of ANN Multi-Step Time Series Forecasting to Improve the Students Metacognition in Solving A Cryptocurency Volatility Based on the Fundamental, Technical and On-Chain Analysis

Metacognitive skills refer to an individual’s ability to be aware of, manage, and evaluate their own thinking processes, supporting independent learning strategies and understanding. However, the low level of metacognitive skills highlights the need for further research, particularly on how to improve them. This study aims to enhance students’ metacognitive skills through a Research-Based Learning (RBL) approach and STEM learning activities by employing multi-step time-series forecasting techniques based on Artificial Neural Networks (ANN). The main focus is on designing learning activities that teach ANN Multi-Step Time Series Forecasting to address cryptocurrency volatility, analyzed using three main approaches: fundamental analysis, technical analysis, and on-chain analysis. By applying ANN to predict cryptocurrency price volatility, students are expected to better understand market dynamics and improve their decision-making abilities. The results show that RBL-STEM learning activities can be structured into six clear stages to utilize ANN Multi-Step Time Series Forecasting for improving students’ metacognitive skills in solving cryptocurrency volatility issues based on these analytical approaches.

A MATHEMATICAL APPROACH TO INVESTMENT WITH CHARGE ON BALANCE AND VOLUNTARY CONTRIBUTIONS UNDER WEIBULL MORTALITY FORCE FUNCTION

One of the many challenges encountered by most pension fund administrators (PFAs) in the Defined Contribution (DC) pension plan is the determination of a sustainable and suitable investment plan for their members under mortality risk. To achieve this, there is need to develop an optimal portfolio which considers the volatility of the stock market price consisting of one risk-free asset and a risky asset which follows the Heston volatility model (HVM). Also, the portfolio considers additional voluntary contributions (AVC) by members, tax on the stock market price, charge on balance (CB), and the mortality risk of the pension scheme members (PSM) modeled by the Weibull mortality force function. Furthermore, an optimization problem is established from the extended Hamilton Jacobi Bellman (EHJB) equation using variational method. By applying the variable separation technique and mean variance utility, the optimal control strategy (OCS) and the efficient frontier are obtained. Finally, some numerical simulations are presented to study the behavior of the OCS with respect to some sensitive parameters. It was discovered that the composition of the OCS depends on the instantaneous volatility, tax on investment, AVC, risk aversion coefficient (RAC), CB and the correlation coefficient. Hence, the understanding of the behaviour these parameters are very crucial in the determination of OCS.

UNDERSTANDING LQ45 STOCKS (2021-2023) WITH K-MEANS CLUSTERING

The primary aim of this study is to examine the use of K-Means clustering in analyzing LQ45 stocks from 2021 to 2023, utilizing data obtained from the Yahoo Finance platform. The analysis delves into key performance measures such as the price-to-earnings ratio (PER), earnings per share (EPS), dividends, trading volume, and historical return on investment. This technique categorizes stocks with similar characteristics, providing financial analysts, money managers, and investors with valuable insights. The objective of the clustering analysis is to gain a deeper understanding of the relationship between intrinsic stock features and the inherent price volatility of companies. This is accomplished by using historical datasets to conduct stock feature analysis. Mathematics plays a crucial role in the K-Means model by providing the foundational algorithms and statistical methods used to categorize and analyze the data. The study contributes to the field of financial market analysis by demonstrating how understanding group-to-group dynamics can affect investment decisions and offering a more precise representation of large datasets in financial contexts. These findings provide significant insights for individuals involved in financial matters in the stock market, helping to identify potential investment opportunities and reduce risk more effectively.

Historical geopolitical risk acts and world crude oil production: evidence from the international level

Purpose The purpose of this study is to empirically investigates the influence of geopolitical risk acts (GPR) on world crude oil production. GPRs are a proxy for waging wars and military actions, thus it is expected to have a clear impact on world crude oil production (WCOP). Design/methodology/approach The current study formulates a theoretical model, and its parameters are estimated using three methodologies – traditional autoregressive distributed lag (ARDL) model, Dynamic ARDL model, and Frequency domain causality test – across the period 1973–2023 to ensure robust findings. Findings The results illustrate that GPRs constrain the global crude oil supply. The final impact relies on the positive interaction term between GPRs and international economic activity from one side and the negative interaction term with crude oil prices from the other. Likewise, the findings show that geopolitical threats reduce producing crude oil. Furthermore, this research deliberates on policy implications stemming from these findings. Originality/value Examining existing literature reveals a gap in understanding the relationship between geopolitical risk and crude oil production. Most of the studies in this research strand focus on investigating the impact on oil price volatility.

The Role of Food Prices and Market Access in Contributing to Hunger in Low-Income Populations

This article investigates the role of food prices and market access in exacerbating hunger within low-income populations, emphasizing the economic dimensions of food insecurity. Drawing on case studies from Sub-Saharan Africa and Brazil, it highlights how food price volatility and inadequate infrastructure significantly contribute to hunger. High food prices, driven by global disruptions and local inefficiencies, force low-income households to reduce their food intake or opt for less nutritious options, worsening food insecurity. Limited road networks and insufficient storage facilities inflate logistical costs, further impeding food distribution and access. Key findings include Brazil’s Zero Hunger program, which successfully reduced hunger through targeted subsidies, social policies, and infrastructure investments, and the quantitative impact of improved transportation and storage facilities in Sub-Saharan Africa, which stabilized food prices and reduced costs. This research underscores the global significance of addressing hunger amid challenges like climate change and geopolitical disruptions. Policy recommendations include targeted subsidies for staple foods, public-private partnerships to enhance infrastructure, and price stabilization mechanisms. By highlighting the interconnectedness of economic policies, market access, and food security, this study provides policymakers, NGOs, and international organizations with actionable insights to mitigate hunger and build more resilient food systems for vulnerable populations.

Study of cyclic price volatility for fish products in Russia

The purpose of the work: to test the hypothesis about the existence of periodically changing price volatility over time and to determine the key characteristics of this process.Methods used: decomposition of price dynamics for the main types of fish products in Russia with the allocation and analysis of its components; to construct a model describing the trajectory of price changes, the provisions of the spider web theory were used, in particular, the Kagan model, transformed into the harmonic oscillator model.Results: it was found that prices for fish products in the Russian Federation, in addition to the general upward trend, also have cyclical volatility and are subject to shock changes from time to time; the parameters of price fluctuations were determined.Scientific novelty: A nonlinear model has been developed that reflects in aggregate form the influence of endogenous and exogenous factors on prices for fish products. The model describes cyclical price changes in the form of relaxation fluctuations.Practical significance: The study of the recessive characteristics of price volatility made it possible to identify the key parameters of possible measures of industry policy. These measures can become part of the socioeconomic policy.

Spatial Price Integration and Spillover Linkages Between Regional Grain Markets in Nigeria

ABSTRACTThis paper aims to characterize the spatial set‐up of Nigeria's grain market integration from January 2002 to December 2013 across 19 Nigerian regions. Using market price data of maize, sorghum, and millet, two hypotheses are tested: (i) the law of one price across market pairs, and (ii) the leader market hypothesis in any network of market pairs. The results suggest that integration fails to hold in many cases for over 60% of market pairs, while perfect integration is found to be considerably stronger for organized local markets such as Abuja and Lagos than for remote markets. There is evidence that Northeastern markets subject to the Boko Haram insurgency are weakly integrated with the other markets. Only three Northwest rural markets exhibit leading status in volatility prediction in different markets. The findings suggest that maize and millet markets located in Abuja and Lagos are the net receivers of price volatilities, and they are more exposed to price shocks originating from other markets. The pricing policy should mainly concentrate on leading markets as it will be efficiently transmitted to the followers.

World crude oil price volatility impacts on domestic fuel-imports and carbon emissions: short and long-run evidence using ARDL

World crude oil price volatility impacts on domestic fuel-imports and carbon emissions: short and long-run evidence using ARDL

Impact of Indices on Stock Price Volatility of BRICS Countries During Crises: Comparative Study

This study aims to identify the common indices having an impact on the SPV of BRICS countries during crises. To address this, the monthly data retrieved from the database of the Global Economic Monitor (GEM), World Bank, IMF International Financial Statistics data, and OECD in the period of January 2000 to December 2023 are analyzed in two phases. In the first phase, DM classification techniques are applied to the data to identify the best common classification technique in order to use this technique in the second phase to compare the results with Multiple Linear Regression (MLR) results. In the second phase, to account for the global financial crisis and COVID-19 crisis, the sample period is divided into two sub-periods. For those sub-periods, MLR and the best classification technique that was found in the first phase are utilized to find the common indices that have an impact on the stock price volatility during individual and both crises. The findings indicate that the Random Tree method commonly classified the data among the seven classification techniques. Regarding MLR results, no common indices were identified during the global financial crisis or the COVID-19 crisis. However, based on Random Tree classifications, the CPI price percent, National Currency, and CPI index for all items were common during the global financial crisis, whereas only the CPI price percent was common during the COVID-19 crisis. While some common indices were observed in individual crises for specific countries, no indices were consistently found across both crises. This variation is attributed to the unique nature of each crisis and the diverse economic and socio-political structures of different countries. These findings provide valuable insights for financial institutions and investors to refine financial and policy decisions based on the specific characteristics of each crisis and the indices affecting each country.

Forecasting the Volatility of OPEC Oil Prices using Fractionally Integrated GARCH models

Forecasting the Volatility of OPEC Oil Prices using Fractionally Integrated GARCH models

Modern methods of strategic management in the oil and gas industry

The necessity to develop new methods of strategic management in the oil and gas industry is due to many factors, among which one can note the increase in the cost of material and technical components for equipment, high volatility of prices for oil and gas industry resources, lack of stability of financial markets, increased competition for the development of new fields, which requires the search for a new vector of successful development of oil and gas companies engaged in production, transportation and sale of resources.The article provides a comparative analysis of strategic management methods in the oil and gas industry and identifies the main trends in strategic management in accordance with new processes in the global and national economy.

Hybrid in-situ and ex-situ hydrolysis of catalytic epoxidation neem oil via a peracid mechanism

The depletion of oil reserves and their price and availability volatility raise researchers’ concerns about renewable resources for epoxidized material. This study aims to produce in situ and ex-situ hydrolyzed dihydroxy stearic acid via the epoxidation of neem oil. Epoxidized neem oil was synthesized using in situ-generated performic acid. The Taguchi method was employed to optimize hydrolysis, aiming for maximum production of dihydroxystearic acid. The Taguchi method’s signal-to-noise (S/N) ratio analysis identified optimal conditions for producing dihydroxy stearic acid with a maximum hydroxyl value of 129.4 mg KOH/g: (1) water/neem oil molar ratio of 2:1, (2) water addition time of 90 min, and (3) reaction stop time of 120 min. ANOVA revealed the significant order of parameters as reaction stop time > water addition time > water/neem oil molar ratio. Lastly, a mathematical model was developed using MATLAB, applying the fourth-order Runge–Kutta method and simulated annealing optimization to determine the best-fitting kinetic model. This research aids in transforming neem oil into a value-added product, reduces petroleum dependence, and provides key insights into reaction kinetics for industrial applications.

Price limits, investor asset allocation, and price volatility: Evidence from China’s registration-based IPO reform

Price limits, investor asset allocation, and price volatility: Evidence from China’s registration-based IPO reform

OIL PRICE FLUCTUATIONS AND MACROECONOMIC INDICATORS: AN ARDL AND NARDL APPROACH

This study investigates the effects of oil price fluctuations on Nigeria’s macroeconomic indicators from 2015 to 2023, focusing on inflation and economic growth. Utilizing Autoregressive Distributed Lag (ARDL) and Nonlinear ARDL (NARDL) models, we analyze the short-term and long-term impacts of variables such as Premium Motor Spirit (PMS) prices, exchange rates, money supply, interest rates, and economic uncertainties. Results reveal significant short-term effects of oil price increases on inflation and GDP, with asymmetric influences observed in exchange rate movements. The long-term analysis confirms cointegration relationships but underscores slow adjustment speeds for inflation and GDP equilibrium. Recommendations include strengthening monetary policy, stabilizing the exchange rate, and adopting strategies to mitigate oil price volatility’s adverse effects.

The tail risk premium in the oil market

This paper studies tail risk and its option-implied risk compensation in the crude oil market. We identify economically large premia for upside and downside tail risks that significantly forecast crude oil futures returns. These premia are also reflected in the convenience yield for physical oil, which amplifies the predictive power for spot returns. Oil tail risk premia are not spanned by aggregate uncertainty measures, suggesting that shifts in market-specific risk attitudes contribute to commodity price volatility and return predictability.

Shocked: Electricity Price Volatility Spillovers in Europe

Shocked: Electricity Price Volatility Spillovers in Europe

Exploring the relationship between dividend policy and stock price volatility: a sectoral analysis of Indian companies

Exploring the relationship between dividend policy and stock price volatility: a sectoral analysis of Indian companies

Neural network framework for predicting groundnut price volatility in india

Neural network framework for predicting groundnut price volatility in india

Effect of global oil shocks on the sectoral returns

PurposeThis study examines the effects of global oil shocks on sectoral returns during the Pakistani market’s bearish, normal and bullish behavior by controlling VIX and SP500.Design/methodology/approachWe analyze daily sectoral return data from the Pakistan Stock Exchange, covering the period from January 2, 2012, to February 17, 2022. Oil shocks are classified into demand, risk and supply shocks using Ready (2018) methodology, distinguishing their unique characteristics and importance. We utilize quantile regression to evaluate how these oil shocks affect sectoral returns, considering market dynamics. Additionally, we employ the Granger causality test to validate our findings.FindingsThe quantile regression results indicate that the effects of oil shocks on each sector are heterogeneous and significant during both bullish and bearish market phases. Specifically, supply shocks demonstrate a more substantial influence than other shocks, consistent with Kilian’s (2009) findings. Furthermore, the Granger causality test outcomes, conducted after removing control variables, reinforce our findings.Practical implicationsThis study offers evidence that aids investors in understanding how shocks affect sectoral returns. Additionally, it provides insights for policymakers to develop policies that respond effectively to market fluctuations caused by these shocks.Originality/valueThis study holds value for researchers investigating the causes of shocks and their implications for the stock market. Moreover, the insights from this study, focusing on the Pakistani market, could provide a fundamental reference for other oil-dependent nations aiming to mitigate the impacts of global oil price volatility.