Price Volatility Research Articles

The impact of ESG rating changes on fund shareholding

This study examines the impact of environmental, social and governance (ESG) rating changes on fund shareholding based on rating data from five agencies. Using a sample of Shanghai and Shenzhen A-share listed companies in China spanning from 2019 to 2021, we find robust evidence that the mean value of ESG ratings by agencies positively impact the number of fund holdings, while the standard deviation of ESG ratings by agencies have no impact on the number of fund holdings. Additionally, the mean value of ESG ratings by agencies have a greater impact on the number of fund holdings among state-owned and industrial companies, and the mean value of ESG ratings by agencies has a larger impact on the number of fund holdings in a one-year window than in a six-month window. Moreover, we find that the mean value of ESG ratings by agencies negatively impact the stock price volatility. The results show that ESG rating changes play a positive role in the decision-making of fund shareholding and in promoting the sustainable development of firms.

Assessment of short-term supply disruption impacts for the Swiss mobility, energy and ICT sectors – Application of the SPOTTER approach

In this paper, supply disruption impacts along supply chains within the Swiss mobility, energy and ICT sectors are assessed in the short-term (i.e. the next 5 years) using the recently developed SPOTTER approach with the objective to identify hotspots and technologies associated with comparably low supply risks. SPOTTER has been chosen because, compared to other approaches, it is the only one that allows for an assessment along the full supply chain and it is integrated into the Life Cycle Sustainability Assessment framework. To decrease the respective data acquisition efforts for the here-performed supply risk analysis of entire sectors, in a first step, material/product flows that are most influential for an assessment with SPOTTER are identified and considered for the calculation of related impact scores. Our study analyzes critical material and product flows between trade partner countries along relevant supply chains. It thus identifies new hotspots and adds relevant country-specific information to current knowledge. In particular, the identified hotspots refer to volatile prices of solar panels, interrupted trade of oil and gas from Russia, Nigeria and Niger as well as disrupted imports of electronic devices from China. Suggestions for mitigation strategies comprise hedging and/or diversifying the supply chains. Other identified hotspots refer to the critical supply of cobalt and natural graphite along battery supply chains, which could for example be addressed by supporting and/or incentivizing the construction of a circular economy. A third category of identified hotspots indicates risks related to the trade of hafnium and nuclear power plant equipment that could be mitigated by shifting to less risky energy technologies. Our comparisons of overall impacts between relevant technologies suggest higher supply risks for conventional cars, solar panels and Chinese laptops compared to battery electric cars, wind turbines and German laptops. In these comparisons, more details regarding disruptions along full product and fuel supply chains are considered than in current studies.

The Painful Double-Knock on Food Prices: 2008 Financial Crises and COVID-19 Pandemic

Food supply and demand chains are susceptible to global shocks. Unstable and sudden food price hikes cause serious malnutrition problems and increase the number of food-insecure people, especially in developing countries. Using the FAO Food Price Index (FFPI), this study makes one of the first attempts to utilize monthly observations of the FFPI in dynamic time series ARDL and ARX settings to identify the effects of food prices on COVID-19 infection rates and the 2008 global financial crisis. Our empirical findings confirm that the financial crisis significantly increased the FFPI, although its effects decreased as markets equilibrated between 2007 and 2009. The pandemic has had a mild impact on food prices in the short run compared to the 2008 crisis, but in the long run, the COVID-19 outbreak has a larger impact, with 1 million new COVID-19 infections associated with an increase of between 0.0464 and 0.0509 points in the FFPI. Food price volatility and hikes, even if short-term, increase poverty, malnutrition, and food insecurity, foster social unrest, and lower people’s living standards. This research implies that food prices are globally sensitive to both pandemics and financial crises, and the severity of the pandemic can drive global food prices higher, depending on the number of infections. Over the long run, the impact of the outbreak surpasses that of the financial crisis. The latter tends to have a major impact on food prices in the short run but subsequently declines as markets begin to equilibrate, reflecting asymmetries between the two phenomena in their effects on food prices. Overall, the results indicate that the financial crisis and the COVID-19 pandemic had a short-run, immediate, augmenting impact on food prices. However, while the 2008 crisis affected the supply side only, COVID-19 had impacts on both the demand and supply sides.

Investor Sentiment and Corporate Governance: The Role of Behavioral Biases in Stock Price Volatility and Managerial Decisions

Behavioural biases influence investor sentiment and behaviour, which in turn influence stock returns, stock price volatility and corporate governance structures. This paper focuses on the role of three common behavioural biases (overconfidence, herding and loss aversion) on investor behaviour, and how these biases drive inefficiencies in financial markets. Using worldwide market data from 1970 to 2019 and focusing on key events such as the 2008 financial crisis and the dot-com bubble, we investigate how these behavioural biases cause market volatility. This analysis further reinforces the importance of strong corporate governance structures such as independent boards, executive pay structures and risk management protocols that help reduce the impact of investor sentiment. The role of speculative bubbles, market overreaction and volatility clustering is also examined further, and corporate governance mechanisms are proposed to help mitigate the effects of behavioural biases on stock prices. The findings demonstrate the importance of adopting long-term corporate goals, transparency and strategies to help better manage market sentiment and safeguard shareholder value.

Novel Custom Loss Functions and Metrics for Reinforced Forecasting of High and Low Day-Ahead Electricity Prices Using Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) and Ensemble Learning

Day-ahead electricity price forecasting (DAEPF) is vital for participants in energy markets, particularly in regions with high integration of renewable energy sources (RESs), where price volatility poses significant challenges. The accurate forecasting of high and low electricity prices is particularly essential, as market participants seek to optimize their strategies by selling electricity when prices are high and purchasing when prices are low to maximize profits and minimize costs. In Japan, the increasing integration of RES has caused day-ahead electricity prices to frequently fall to almost zero JPY/kWh during periods of high RES output, creating significant profitability challenges for electricity retailers. This paper introduces novel custom loss functions and metrics specifically designed to improve the forecasting accuracy of extreme prices (high and low prices) in DAEPF, with a focus on the Japanese wholesale electricity market, addressing the unique challenges posed by the volatility of RES. To implement this, we integrate these custom loss functions into a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model, augmented by an ensemble learning approach and multimodal features. The proposed custom loss functions and metrics were rigorously validated, demonstrating their effectiveness in accurately predicting high and low electricity prices, thereby indicating their practical application in enhancing the economic strategies of market participants.

Debt Risk Analysis of Automotive Enterprises

In order to maintain their financial stability, businesses must recognize and manage the financial risks that are necessarily involved in their operations and output. Businesses may better comprehend their financial status and undertake efficient risk early warning by examining financial indicators. Academic research on the detection and mitigation of financial risks is extensive, and in an effort to increase the accuracy of assessments, an increasing number of academics are using multi-indicator systems for thorough analysis. Furthermore, non-statistical techniques including hierarchical analysis, B-S option pricing models, and artificial neural networks have shown extremely effective at handling complicated data and non-linear connections. As the economy transitions from high to medium-high growth, traditional and new energy vehicles in the automobile sector confront significant possibilities and problems. Traditional automakers aggressively converting to new energy cars include SAIC Motor, BAIC Group, and GAC Group. Companies that produce new energy vehicles, such XPeng, BYD, Tesla, Li Auto, and NIO, are becoming more competitive by developing new technologies and tapping into new markets. To enhance market competitiveness and sustainability, businesses must address issues like excessive inventory and price volatility that have been brought about by the new energy vehicle market’s explosive expansion through efficient financial risk management. The short- and long-term debt repayment capacities of traditional and new energy vehicle (NEV) enterprises are the main subjects of this study’s analysis of their financial statements. According to the findings, traditional automakers have more stable financial circumstances whereas NEV enterprises exhibit stronger short-term solvency and lower long-term financial risk in specific years with superior liquidity and lower debt ratios. The study attempts to offer resources for the automobile industry’s deleveraging, sustainable development, and inventory reduction.

Oil price shocks and human capital channels in Nigeria

Nigeria faces persistent challenges of high unemployment and poverty, worsened by its over-reliance on the oil sector. While many studies have investigated the relationship between oil price volatility and human capital development, few have examined how this relationship impacts both basic and advanced human capital channels in developing economies; this study addresses that gap by using a Structural Vector Auto Regression (SVAR) approach to investigate how oil price shocks influence human capital channels. Co-integration tests reveal significant long-term relationships among the variables. For the basic channel, oil price shocks are negatively associated with secondary school enrollment but positively linked to primary school enrollment. In the advanced channel, oil price has a positive effect on government expenditure on tertiary education but negatively affects tertiary institution enrollment. The impulse response function confirms that oil price fluctuations significantly affect both basic and advanced channels of human capital. The findings show the detrimental impact of oil price shocks on Nigeria's human capital development, which may negatively affect sustainable development. The study emphasises the urgent need for economic diversification to reduce the economy's vulnerability to oil shocks and promote long-term growth and human capital development.

The Link Effect of ESG Score, Stock Price Volatility, and Tax Payment: Doing Well while Doing Good

This study aims to identify how environmental, social and governance (ESG) performance influences stock price volatility, explicitly focusing on the moderating role of tax engagement. ESG performance is measured by an ESG Score calculated from the weighting of three dimensions: environmental, social and governance. Stock price volatility is measured by the degree of price variations over 12 months based on the last 52 weeks’ prices. A sample of Indonesia-listed firms is used, with 770 observations from 2023. The results show that the ESG Score negatively impacts stock price volatility, which is more significant in the social dimension than in the environmental and governance dimensions. In addition, the tax payment variable moderates the relationship and increases the effect of the ESG Score on stock price volatility. These findings suggest that ESG practices and tax transparency are ethical elements and critical components for financial stability, promoting the high-quality development of listed firms. This study is significant for firms, regulators, policymakers and investors. Overall, it underrates the importance of firms adopting ESG activities and engaging in tax management to mitigate risks and maintain viability in the contemporary business environment. This study provides new empirical evidence regarding the factors driving corporate stock price volatility. In addition, it offers pertinent policy recommendations for businesses and governments regarding the significance of ESG investments.

An Empirical Analysis of Price Volatility of Turmeric in India

The objective of the present study aimed at examining the price volatility of turmeric in major markets of the state. The secondary data on monthly modal prices for the period January 2011 to December 2023 were collected from Duggirala and Kadapa (Andhra Pradesh), Nizamabad and Warangal (Telangana), Sangli (Maharashtra) and Erode (Tamil Nadu) by using purposive sampling method. The findings of ARCH-GARCH analysis revealed that the price series of Duggirala, Nizamabad and Erode markets showed the presence of price fluctuations as indicated by the sum of Alpha and Beta co-efficient which were nearer to one whereas in the remaining markets, the volatility shocks were not quite persistent.

Price transmission relationships in Taiwan seafood supply chain: Tilapia (Oreochromis niloticus) and milkfish (Chanos chanos)

Tilapia and milkfish are aquaculture species associated with the highest production volume in Taiwan. With both fish species as the research focus, this study explored their price transmission relationships along the seafood supply chain. Our study results revealed that tilapia and milkfish prices had a long-run equilibrium relationship at different stages of the seafood supply chain and presented inconsistent price transmissions in the short run. There are domestic and foreign market channels for tilapia, enabling producers and wholesalers to transmit prices to retailers. By contrast, milkfish are mostly supplied to domestic markets, there is not perfect price transmission in the milkfish supply chain besides between farmers and wholesalers. An accurate price forecasting model can effectively indicate future price volatility and predict seafood prices, especially following government policy changes or in the face of external impacts. Such model can also serve as a crucial reference for future research of tilapia and milkfish industry, producers, and aquaculture farmers. Finally, seafood prices are a key determinant of fisheries and supply chain sustainability. Therefore, the stability of seafood prices, fish market wholesale pricing, and retail pricing are issues meriting continuous attention for a sustainable seafood supply chain.

Multi-Task Forecasting of the Realized Volatilities of Agricultural Commodity Prices

Motivated by the comovement of realized volatilities (RVs) of agricultural commodity prices, we study whether multi-task forecasting algorithms improve the accuracy of out-of-sample forecasts of 15 agricultural commodities during the sample period from July 2015 to April 2023. We consider alternative multi-task stacking algorithms and variants of the multivariate Lasso estimator. We find evidence of in-sample predictability but scarce evidence that multi-task forecasting improves out-of-sample forecasts relative to a classic univariate heterogeneous autoregressive (HAR)-RV model. This lack of systematic evidence of out-of-sample forecasting gains is corroborated by extensive robustness checks, including an in-depth study of the quantiles of the distributions of the RVs and subsample periods that account for increases in the total spillovers among the RVs. We also study an extended model that features the RVs of energy commodities and precious metals, but our conclusions remain unaffected. Besides offering important lessons for future research, our results are interesting for financial market participants, who rely on accurate forecasts of RVs when solving portfolio optimization and derivatives pricing problems, and policymakers, who need accurate forecasts of RVs when designing policies to mitigate the potential adverse effects of a rise in the RVs of agricultural commodity prices and the concomitant economic and political uncertainty.

Optimal Reactive Power Dispatch and Demand Response in Electricity Market Using Multi-Objective Grasshopper Optimization Algorithm

Optimal Reactive Power Dispatch (ORPD) is a power system optimization tool that modifies system control variables such as bus voltage and transformer tap settings, and it compensates devices’ Volt Ampere Reactive (VAR) output. It is used to decrease real power loss, enhance the voltage profile, and promote stability. Furthermore, several issues have been faced in electricity markets, such as price volatility, transmission line congestion, and an increase in the cost of electricity during peak hours. Programs such as demand response (DR) provide system operators with more control over how small customers participate in lowering peak-hour energy prices and demand. This paper presents an extensive study on ORPD methodologies and DR programs for lowering voltage deviation, limiting cost, and minimizing power losses to create effective and economical operations systems. The main objectives of this work are to minimize costs and losses in the system and reduce voltage variation. The Grasshopper Optimization Algorithm (GOA) and Dragonfly Algorithm (DA) have been implemented successfully to solve this problem. The proposed technique has been evaluated by using the IEEE-30 bus system. The results obtained by the implementation of demand response systems show a considerable reduction in costs and load demands that benefit consumers through DR considerations. The results obtained from the GOA and DA are compared with those generated by other researchers and published in the literature to ascertain the algorithm’s efficiency.

Retraction Note: An integration of oil price volatility, green energy consumption, and economic performance: assessing the mediating role of trade.

Retraction Note: An integration of oil price volatility, green energy consumption, and economic performance: assessing the mediating role of trade.

Climate change and political stability in African states: the role of agricultural food price volatility

Climate change and political stability in African states: the role of agricultural food price volatility

Nonlinear impact of climate risks on renewable energy stocks in China: A moderating effects study

This paper assesses the physical climate risks renewable energy stocks face in China. Employing a functional coefficient model, the study investigates the nonlinear impacts and moderating effects of climate risk on the returns and volatility of these stocks. The key findings are as follows: (1) The physical climate risk levels for renewable energy stocks exhibit a normal distribution pattern. (2) Physical climate risk has a U-shaped nonlinear effect on both stock returns and volatility, where higher levels of risk enhance returns but also increase volatility. (3) Climate transition risk has a U-shaped influence on stock returns and an inverted U-shaped influence on stock volatility. Specifically, high climate transition risk not only elevates returns but also stabilizes the renewable energy stock market. Moreover, heightened climate transition risk amplifies the negative effects of oil price volatility on stock returns and increases overall stock volatility.

Unraveling the time-varying asymmetric impact of fossil fuel price volatility on high cleantech investments

Unraveling the time-varying asymmetric impact of fossil fuel price volatility on high cleantech investments

RELATIONSHIP BETWEEN ENERGY AND FOOD PRICES IN THE EURO AREA

Over the past five years, global economic developments have been accompanied by a number of challenges. After a period of economic development, the global economy is facing a significant challenge caused by the COVID-19 crisis. There have been factory closures, border closures and supply chain disruptions. All this is reflected in the EU and the euro area. This was followed by a period of recovery and accelerating inflation. The war on the territory of Ukraine is also having a significant impact on inflationary processes. This further jeopardises the supply of energy resources and their prices begin to rise at a very high rate. The shock of rising energy prices has also been reflected in sharp increases in food prices. In the euro area, there have been periods when food prices have risen by more than 17% on an annual basis and energy products by almost 60% in some months. All these developments highlight the need to examine the links between energy and food prices. This is necessary in order to be able to take quick decisions in the event of new volatility in energy prices. The aim of the study is to establish a causal relationship between the dynamics of energy and food prices and the time lag over which food prices react to increases in energy prices. A correlation analysis is carried out and a causal relationship between the two indicators is established by regression analysis at different time lags. The hypothesis tested is that, over the last five years, there has been a strong month-to-month correlation between energy prices and food prices with a lag of six months. For the purpose of regression analysis and hypothesis testing, monthly data for food inflation and inflation for the group Electricity, gas and other fuels have been used. The data are taken from the structure of the Harmonised Index of Consumer Prices published by Eurostat. The detailed regression analysis shows that there is a statistically significant linear relationship between the price indices of the two product groups with a lag of six months. The Granger causality test confirms the results of the dynamic, correlation and regression analysis. The results of the test show not only the existence of a causal relationship, but also the ability of energy price changes to predict food price dynamics. All this leads to the conclusion that in periods of significant changes in energy prices, food prices can be expected to follow similar dynamics within six months. This relationship could be used both for forecasting and for taking specific economic measures.

Technological Innovation and Agricultural Productivity in Nigeria Amidst Oil Transition: ARDL Analysis

In contemporary discourse, Nigeria’s reliance on its oil sector is proving insufficient for sustained economic growth. The volatility of oil prices, geopolitical tensions, technological advancements, and environmental sustainability concerns have exposed the vulnerabilities of an oil-dependent economy, emphasizing the need for diversification and a renewed focus on agriculture. This study investigates the relationship between technological innovation and agricultural productivity in Nigeria, contrasting it with the oil sector. Using the ARDL estimation technique, our findings reveal a significant negative influence of immediate lagged agricultural productivity (AGTFP(−1)), indicating technological constraints. Technological innovation, proxied by TFP, shows a substantial impact on agricultural productivity, with a negative long-term effect (−90.71) but a positive, though insignificant, impact on agricultural output (0.0034). The comparative analysis underscores that the agricultural sector tends to benefit more from technological innovation than the oil sector. This highlights the critical need to prioritize technological advancements in agriculture to drive sustainable growth and economic resilience in Nigeria.

Beyond GARCH: Intraday Insights Into the Exchange Rate and Stock PriceVolatility Dynamics in Borsa Istanbul Sectors

This study investigated the impact of exchange rate volatility on sectoral stockvolatility by employing the intraday volatility measure directly calculated fromthe original data, using daily data from 27 Borsa Istanbul sectors between April 29, 2003, and April 25, 2023. In the literature, GARCH models are commonly usedtostudy the volatility spillovers between exchange rates and stock prices, typicallyusing aggregate data. However, the GARCH family models provide inefficient andbiased estimates if they are misspecified. Moreover, using aggregate-level data maylead to biased and misleading conclusions. The research used intraday volatilitymeasures to overcome the shortcomings of GARCH models. The ordinary least squares (OLS), GARCH (1,1) methods, and Garman and Klass (1980) volatilityestimator are used. The empirical results showed that the estimates fromeachmethod vary significantly, and these disparities in the results might be due tomisspecification in GARCH (1,1) models. The intraday volatility model estimationresults showed that although stock price volatilities in all sectors are positively andsignificantly affected by exchange rate volatility, their magnitudes varysignificantly. Taken together, this implies the presence of vast heterogeneities inthe responses of sectoral stock price volatilities to exchange rate volatility. The results encourage policymakers to pay special attention to these heterogeneities toprevent capital flights and underinvestment. Additionally, the findings assist investors in making more effective decisions by helping themadapt their investment strategies to factor in exchange rate fluctuations and mitigate the impact of unexpected events in the exchange rate market

Retraction Note: Does oil price volatility influences carbon emission trends and financial concerns of oil industry?

Retraction Note: Does oil price volatility influences carbon emission trends and financial concerns of oil industry?