Increased Price Volatility Research Articles

Can Bitcoin trigger speculative pressures on the US Dollar? A novel ARIMA-EGARCH-Wavelet Neural Networks

In recent years, Bitcoin has garnered attention as a digital currency, prompting increasing debate regarding its effects on traditional financial markets, particularly the US dollar. This study investigates the relationship between Bitcoin and the US dollar, especially in the contexts of speculative attacks, where investors attempt to devalue a currency, and short squeezes, where rapid price rises force short sellers to quickly buy back assets to avoid further losses. The study employs a novel hybrid model combining an autoregressive moving average, Generalized Autoregressive Conditional Heteroskedasticity, and Wavelet Neural Networks techniques with neural networks approaches. The results suggest that significant trading activity in Bitcoin/US dollar, particularly during speculative attacks and short squeezes, can substantially impact the US dollar/EUR market, increasing price volatility as traders adjust their strategies. These adjustments, along with risk management strategies, drive higher trading volumes and further volatility. Our findings demonstrate that our novel hybrid model combined with Quantum Recurrent Neural Networks provides the most accurate predictions, offering valuable insights to inform trading strategies in both Bitcoin/US dollar and US dollar/EUR markets. This study has important implications for policymakers and market participants, emphasising the need to understand the relationship between Bitcoin and the US dollar for financial stability and effective policy formulation. It also highlights the necessity of advanced modeling techniques to accurately predict cryptocurrency market behavior.

Loan-Level Disclosure and the Convenience Yield of Asset-Backed Securities

ABSTRACT We examine the impact of transparency on the convenience yield of AAA-rated asset-backed security (ABS) tranches. AAA tranches of ABS are commonly held by investors to manage financial liquidity and therefore enjoy a price premium beyond what is determined solely by the expected monetary payoff (i.e., convenience yield). The Securities and Exchange Commission (SEC) requires ABS issuers to provide monthly disclosures about the performance of the underlying individual loans for ABS issued after November 23, 2016 to improve transparency. We document that AAA tranches of ABS experience a significant increase in price volatility after the loan-level disclosure mandate. As a result, the use of these tranches as collateral for short-term funding and their convenience yield decrease significantly after the increased disclosure. Our collective evidence highlights an important unintended consequence of increased transparency in the ABS market; it diminishes the pledgeability of long-term safe assets. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: G14; G23; M48.

Crude oil price volatility and environmental performance

We study the relationship between crude oil price volatility and corporate environmental performance. Using an extensive dataset from 32 countries consisting of 18,464 firm-year observations, we provide strong evidence that oil price volatility significantly increases firms' environmental performance. Our main inference is robust when using alternative measures of oil price volatility and environmental performance, alternative econometric specifications and samples, and several approaches to control for endogeneity. In a set of additional analyses, we first conduct a difference-in-differences analysis that exploits the Arab Spring as an exogenous oil price volatility increase and document a stronger relationship between oil price volatility and environmental performance in the aftermath of the Arab Spring. We second identify (i) capital expenditures and (ii) alternative energy importation as two mechanisms through which oil price volatility influences environmental performance. We finally show that national culture plays a significant role in moderating the relationship between oil price volatility and environmental performance. Taken together, our empirical findings highlight the role of economic uncertainty in affecting firms’ environmental performance and provide significant contributions to management and policymakers.

Effect of the intermittency of non-conventional renewable energy sources on the volatility of the Colombian spot price

This paper explores one of the side effects and challenges that integrating non-conventional renewable energy sources poses to the Colombian electricity market, the spot price volatility which is directly related to financial risk. We propose a vector error correction model that allows an integrated and dynamic modelling of the offer-side of the spot market by considering bid prices, available energy, renewable energy production and the spot price. To validate the model, we performed statistical tests on the residuals of the model and back-testing. The results show that given a one standard deviation shock in renewable energy production from non-conventional sources, the spot price volatility increases from 12.7 % to 14.5 % in a 365-day horizon, which represents a relative increase of 14.2 %. Also, when evaluating different renewable energy integration scenarios and the official system expansion, we see that assuming 100 % fulfilment of the non-conventional sources integration plan, the volatility goes up to 31.2 % vs. 12.3 %. It is also worth noting that if the plan is fulfilled up to 25 % there is no significant increase in spot price volatility, which can be justified by the simultaneous expansion of conventional sources that compensate for the effects of non-conventional renewable sources.

The Asymmetric Effects of Oil Price Volatility on Stock Returns: Evidence from Ho Chi Minh Stock Exchange

This study is the first to investigate the asymmetric effects of oil price volatility on stock returns for the Ho Chi Minh Stock Exchange (HOSE). We utilized weekly series of VN30-Index, WTI crude oil prices, geopolitical risks (GPR) index, and gold prices spanning from 6 February 2012 to 31 December 2023 as data sources. Using a nonlinear autoregressive distributed lag (NARDL) bounds testing approach, we found that, in the shortterm, oil price volatility has negative asymmetric effects on market returns. Specifically, in the shortterm, a 1 percent increase in oil price volatility immediately leads to a 2.6868 percent decrease in the market returns, while a similar magnitude decrease in oil price volatility is associated with a 6.3180 percent increase in the market returns. In addition, the results obtained from the NARDL model indicated that, in the longterm, the negative and positive changes of oil price volatility have significantly negative effects on the market returns. Finally, the findings derived from the error correction model (ECM) show that a 98.21 percent deviation from the equilibrium level in the previous week is converged and corrected back to the long-term equilibrium in the current week.

A comparison of cryptocurrency volatility-benchmarking new and mature asset classes

The paper analyzes the cryptocurrency ecosystem at both the aggregate and individual levels to understand the factors that impact future volatility. The study uses high-frequency panel data from 2020 to 2022 to examine the relationship between several market volatility drivers, such as daily leverage, signed volatility and jumps. Several known autoregressive model specifications are estimated over different market regimes, and results are compared to equity data as a reference benchmark of a more mature asset class. The panel estimations show that the positive market returns at the high-frequency level increase price volatility, contrary to what is expected from the classical financial literature. We attributed this effect to the price dynamics over the last year of the dataset (2022) by repeating the estimation on different time spans. Moreover, the positive signed volatility and negative daily leverage positively impact the cryptocurrencies’ future volatility, unlike what emerges from the same study on a cross-section of stocks. This result signals a structural difference in a nascent cryptocurrency market that has to mature yet. Further individual-level analysis confirms the findings of the panel analysis and highlights that these effects are statistically significant and commonly shared among many components in the selected universe.

Application of information technology (IT) in Iraq's petroleum industry

The crucial role of information technology (IT) in operating and managing industrial systems is widely recognized, and Iraq's IT capabilities hold great promise for its industrial and other economic sectors. The demand for IT services has risen considerably, particularly for oil and gas companies, which face the challenge of boosting capital asset returns while ensuring safety and sustainability. They need to manage rising demands as well as competition for access to oil reserves while driving improved asset reliability, productivity, and performance. This application show how information technology is used in the petroleum industry. It specifically examines information technology applications in the upstream, midstream, and downstream important segments of the petroleum industry during the drilling process. The increased price volatility's noteworthy effect on the profitability of the energy industry is just one of the current supply chain management challenges. Better supply chain management aids in lowering operational costs, a challenge that many businesses are under demand to address right away. The study's theoretical and practical value is in exposing the characteristics and issues with the infrastructure supporting the Iraqi oil industry and in creating a control algorithm for the evolution of its Regular visual inspections of pipelines, offshore oil rigs, and other infrastructure may be carried out swiftly and safely thanks to technology. Light devices that enable temperature checks using infrared scanning are available in modern applications. Hazards like poisonous substances can be identified by other sensors from a safe distance. Enhancing oil and gas safety with artificial intelligence technologies protects the environment and ensures worker safety

Multifractal Behavior of Cryptocurrencies During Periods of Economic Uncertainty

Background: In recent years, investors' interest in cryptocurrencies has increased due to their notable price volatility and rapid price increases. These investors view cryptocurrencies as suitable financial assets for portfolio rebalancing strategies. Purpose: The main objective of this study is to examine the multifractality of the cryptocurrencies Bitcoin (BTC), Lisk (LSK), Quantum (QUA), Litecoin (LTC), Ripple (XRP), Augur (REP), Darkcoin (DASH), EOS, IOTA (MIOTA). Methods: The Detrended Fluctuation Analysis (DFA) econophysics model supports the methodology. Results: The results suggest that during the 2020 pandemic period, the digital currencies LSK, QUA, MIOTA, XRP, REP, BTC, ETH, LTC and DASH showed very significant persistence, indicating that price formation is not random. However, validating that cryptocurrency prices are predictable based on historical time series was impossible. On the other hand, the digital currency EOS proved to be in equilibrium; in other words, price formation follows the random walk pattern, suggesting that prices are not autocorrelated over time. During the 2022 geopolitical conflict, long-term memory patterns shifted significantly towards short-term memories, i.e. anti-persistence. The digital currencies ETH, MIOTA, EOS, LTC, REP, LSK and DASH showed anti-persistence slopes, indicating that prices were less influenced by past events and more by recent events. On the other hand, the cryptocurrencies BTC (0.50), QUA (0.50), and XRP (0.50) demonstrate that prices contain a significant random component and that the residuals are independent and identically distributed (i.i.d.), supporting the idea that white noise might be present. Conclusion: From a risk management perspective, these findings are highly relevant to investors, traders and market participants.

Supplier encroachment and pricing scheme choice in a supply chain with two-sided uncertainties

ABSTRACT This paper examines supplier encroachment in a scenario where the supplier procures an input in spot and processes it into a final product, while the retailer chooses between contingent and committed pricing schemes for the contract price. We find that when the correlation between the input spot price and final product demand is relatively small, it is optimal for the retailer to adopt the contingent pricing scheme regardless of the supplier's encroachment decision. This is because this scheme enables the supplier to share the input price risk, resulting in a lower contract price. Furthermore, the likelihood of the retailer adopting the contingent pricing scheme increases as demand variability decreases or spot price volatility increases. However, supplier encroachment reduces the retailer's inclination to use the contingent pricing scheme. We further demonstrate that the supplier should encroach when the correlation is sufficiently small, because encroachment alleviates double marginalisation in the retail channel and brings the additional responsiveness in the direct channel. By contrast, a large correlation compels the supplier to encroach even with a sufficiently small market size. In this case, encroachment improves the responsiveness of both retail and direct selling quantities and enables the quantities to match the demand better.

Research on the Mechanism of Corn Price Formation in China Based on the PLS-SEM Model.

After the cancellation of the temporary corn storage system in 2016, the price of corn in China returned to market regulation, resulting in increased price volatility. This study focuses on monthly data from April 2016 to March 2023 in China. The partial least squares structural equation modeling (PLS-SEM) is employed to analyze the impact of corn supply and demand factors, substitute prices, monetary supply, international corn prices, and international energy prices on the volatility of corn prices in China. Results indicate that supply and demand factors are the most direct influencers of corn prices, with demand factors having the most significant impact. Monetary supply and substitute prices affect corn prices through the demand side. External factors mainly consist of international energy and corn prices. The impact of international energy on Chinese corn prices is achieved through international corn prices, which directly influence the prices in China. It is recommended to stabilize corn market prices by regulating corn supply and demand, to improve the monitoring and early warning mechanisms for international energy and corn prices, and to implement measures for prudent regulation of monetary supply.

Optimizing Investment Strategies: A Case Study on JPMorgan Chase & Co. Stock Options Using the Black-Scholes Model and What-If Analysis in Excel

This research focuses on applying the Black-Scholes Model to evaluate European options on JPMorgan Chase & Co. stocks. This model has been a critical foundation in evaluating financial instruments, especially options, since its development in 1973 by Fisher Black, Myron Scholes, and Robert Merton. The study utilizes secondary data from some sources to obtain current information regarding stock prices, strike prices, expiration time, volatility, and relevant risk-free interest rates for option valuation as of December 19, 2023. Through this approach, our aim is to gain a better understanding of how the Black-Scholes Model is used as a framework in determining option prices for these stocks. The research methodology involves What-If analysis, exploring variations in key variables such as current stock price, strike price, expiration time, stock price volatility, and risk-free interest rates to assess how these changes affect the prices of both call and put options. Additionally, the study presents graphs representing stock prices, strike prices, interest rates, time, and volatility to visually support the research findings. The analysis results reveal that the prices of both call and put options are highly responsive to changing market conditions. An increase in the current stock price tends to raise the call option price while reducing the put option price. Conversely, an increase in the strike price has the opposite effect. Moreover, variations in the risk-free interest rates influence the option prices, with rising rates increasing the call option price and decreasing the put option price. Furthermore, as the expiration time approaches or stock price volatility increases, both call and put option prices tend to rise. These findings provide a comprehensive understanding of the dynamics of JPMorgan Chase & Co.'s stock option pricing, offering a foundation for investors to make informed and adaptable investment decisions amid constantly evolving financial markets. Sensitivity to changes in key variables is an essential aspect to consider in designing effective investment strategies in the face of ever-changing financial markets.

Drug reimbursement reforms could increase price volatility, warns negotiator

Drug reimbursement reforms could increase price volatility, warns negotiator

A Study on the Development of Chinese Agriculture under the US-China Tariff War and Countermeasures

Since the trade friction between China and the US in 2018, the US has maintained a surplus in agricultural trade between China and the US, and China will also impose agricultural tariffs in response to the US sanctions. the list of tariff increases announced by China and the US in 2018 covers all types of agricultural products traded between China and the US, meaning that agricultural trade becomes an important part of the game between the two countries. Based on the list and the analysis of previous literature, this paper focuses on the development of Chinese agriculture and tries to provide countermeasures. The study finds that the US-China tariff war has both positive and negative impacts on China's agricultural development, bringing benefits to some enterprises and pushing back the development of new foreign trade markets. It has caused losses to relevant stakeholders, increased price volatility of agricultural products, and is detrimental to the development of agricultural foreign trade and the restructuring of agriculture. In the future, the development of Chinese agriculture can be promoted through the promotion of market stability, the continuous optimization of the agricultural structure and the improvement of modernization.

Strategic Implicit Balancing With Energy Storage Systems via Stochastic Model Predictive Control

Battery Energy Storage Systems (BESS) may exploit the increasing price volatility in imbalance settlement mechanisms via inter-temporal arbitrage. However, participating in these markets requires a careful trade-off between expected profits, accounting for the impact of BESS actions on prevailing imbalance prices, the financial risks and the incurred battery degradation costs. This paper introduces a novel forecast-informed Model Predictive Control (MPC) methodology in which a strategic and potentially risk-averse BESS performs implicit balancing by taking out-of-balance positions in near-real time. Thereby it anticipates expected imbalance prices in a European-style balancing market, and takes into account state of charge-dependent battery degradation costs. To this end, an attention-based recurrent neural network forecasting technique is leveraged to predict the System Imbalance. The proposed methodology is tested on a real-life case study of the Belgian balancing market. Expected profits of a 2 MW/2 MWh BESS (21,784 MW/month) are shown to exceed those of different benchmarks available in the literature, including the profit associated with participating in the day-ahead energy market with perfect price foresight (7,082 /MW/month). From a system perspective, these implicit balancing actions performed by the BESS owner reduce the system imbalance in 75% of all cases, thus improving the cost-efficiency of power systems.

Impact of social Media and Google on stock Markets During a Pandemic: The Case of an Airline

The outbreak of the pandemic has dealt a tangible blow to the global economy, in particular by causing the collapse of stock markets. Most countries have taken measures to contain the coronavirus related to the restriction of human mobility. One of the main victims of such actions were airlines. In order to examine the direct and indirect effects of the pandemic, we investigated the long- and short-term effects on airline stock price volatility of the spread of coronavirus, social media attention to it, the vaccines against coronavirus and restrictive measures in relation to the pandemic. The ARDL model with MG and PMG estimates was applied after the preliminary stability tests for airlines in developed and developing countries. We analyzed the period of the greatest anti-COVID restrictions from 23 March 2020 to 23 March 2021. We reached the following conclusions. Firstly, the increase in the number of cases and deaths from COVID-19 was accompanied by a short-term increase in the volatility of airline stock prices. Secondly, Twitter’s increased focus on COVID-19-related restrictive measures and vaccines against it was accompanied by a short-term increase in airline stock price volatility. Thirdly, the increasing attention at Google on airline restrictions has been accompanied by the long-term effects of rising stock volatility. Our results demonstrate that with the spread of the Internet and social media, the impact of the pandemic on stock markets occurs not only through direct effects on the determinants of Solow’s economic growth model, but also through indirect effects of social media and the Internet on investor behavior through the formation of fear and hysteria in them.

Optimization under uncertainty of a hybrid waste tire and natural gas feedstock flexible polygeneration system using a decomposition algorithm

Market uncertainties motivate the development of flexible polygeneration systems that are able to adjust operating conditions to favor production of the most profitable product portfolio. However, this operational flexibility comes at the cost of higher capital expenditure. A scenario-based two-stage stochastic nonconvex Mixed-Integer Nonlinear Programming (MINLP) approach lends itself naturally to optimizing these trade-offs. This work studies the optimal design and operation under uncertainty of a hybrid feedstock flexible polygeneration system producing electricity, methanol, dimethyl ether, olefins or liquefied (synthetic) natural gas. A recently developed C++ based software framework (named GOSSIP) is used for modeling the optimization problem as well as its efficient solution using the Nonconvex Generalized Benders Decomposition (NGBD) algorithm. Two different cases are studied: The first uses estimates of the means and variances of the uncertain parameters from historical data, whereas the second assesses the impact of increased uncertain parameter volatility. The value of implementing flexible designs characterized by the value of the stochastic solution (VSS) is in the range of 260–405 M$ for a scale of approximately 893 MW of thermal input. Increased price volatility around the same mean results in higher expected net present value and VSS as operational flexibility allows for asymmetric exploitation of price peaks.

Impacts of the COVID-19 Pandemic on the Production Costs and Competitiveness of the Brazilian Chicken Meat Chain

Sanitary requirements, geopolitical crises, and other factors that increase price volatility have an impact on the organization of markets and changes in investment policies and business strategies. The COVID-19 pandemic interrupted the trade of chicken meat, due to the drastic reduction in the circulation of goods, interrupted the supply of production chains, changed consumption habits, and made it difficult to reorganize business due to the slow resumption of operations by suppliers of inputs and in distribution logistics. The magnitude of these impacts has not been studied despite the high relevance of this economic dimension and the managerial implications for sector governance and trade management. The purpose of this study was to evaluate the economic impact of the COVID-19 pandemic on the production costs and competitiveness of the Brazilian chicken meat production chain. The methodology consisted of the detailed collection of information and data on private and social prices carried out using the Policy Analysis Matrix (PAM) method. The competitiveness coefficients and policy effects in the Brazilian broiler production chain before (2015) and during (2022) the COVID-19 pandemic were quantified and compared. Generally, the significant increases in the production costs of chicken meat (30.49%) caused a decrease in total factor productivity (−19.54%), a reduction in gross revenue, and lower tax collection. The pandemic has reduced the profitability of the chicken production chain in Brazil by 32.31%, reduced the competitiveness of exports, and worsened other economic indicators of the production chain. To the best of our knowledge, no other study has investigated the impacts of the COVID-19 pandemic on the competitiveness of the Brazilian chicken meat production chain. The PAM method allows for prices paid and received to be updated in real terms in projects representative of Brazil, the world leader in exports. This information is important for both national and international stakeholders. Additionally, this model is applicable to other meats traded in the international market, as it provides greater precision in business management and can estimate the impacts of risks on the availability or quality of food and health crises with robust results.

Oil Price-Exchange Rate Interdependence: Relevance of Theory of Exchange Rate Overshooting in Nigeria

This article investigated the interdependence between crude oil variability and foreign exchange rate changes in Nigeria. Oil price variability is decomposed into fluctuation on Brent Oil, West Texas Intermediate, OPEC Basket Reference Crude Oils and Bonny Light Crude Oil Price Variability. All the data that used in this research come from secondary sources. Annual time series data for Nigeria are used from 1993 to 2022. The research design for the study is ex post facto research analysis of annual multivariate time series data. The study examined the historical data in order to understand the current state of foreign exchange rate in Nigeria and causal connections crude oil price variability using ARDL Bound test, Johansen approach and Error-Correction Mechanism (ECM) models. In the long run, the coefficient of oil price volatility shows a positive and significant relation with the f o r e i g n e x c h a n g e r a t e at 10 percent level of significance. This implies that a 10 percent, increase in oil price volatility induces foreign exchange rate by 0.61 percent. The positive coefficient of oil price volatility tends to reveal that Dornbusch (1976) Overshooting hypothesis of foreign exchange rate holds for Nigeria. This finding is in conformity with previous studies like (Driskill & McCafferty, 1982); Turnovsky & Bhandari, 1982; Driskill & McCafferty, 1982). Furthermore, the coefficient of BOP and OPEC show a negative and insignificant relationship with the foreign exchange rate in the long run. This implies that BOP and OPEC hav e no impact on foreign exchange rate. This is in contrast with economic theory particularly Dutch diseases proposition and in conformity with foreign exchange rate overshooting theory. The negative nexus can be blamed on the inability of government to manage the foreign exchange supply and demand effectively in the country. Diversifying away from oil to other non-oil activities that would generate foreign exchange sh

Sustainability-related disclosure rules and financial market indicators: Searching for interconnections in developed and developing countries

In today’s fast-paced business environment, integrating sustainability into financial decision-making has been a key driver of change. As stakeholders increasingly demand greater corporate transparency and accountability, regulatory bodies have stepped in to ensure that sustainability reporting is standardized and robust. This paper aims to establish the relationship between the sustainability-related disclosure rules and the dynamic indicators of the financial market. The object of the study is 74 countries of the world, which are grouped into developed and developing countries. The time period is 2021, for the stock market capitalization indicators – 2020, as the most recent years with available data. The research methods are normality tests (Shapiro-Wilk and Shapiro-Francia test), comparison methods (Student’s t-test and Mann-Whitney U test, regression analysis with dummy variables), linear and non-linear correlation and regression analysis (logarithmic, polynomial). The results obtained confirmed that the sustainability-related disclosure rules are higher in developed countries than in developing ones. At the same time, in developed countries, the growth of such requirements affects the increase in stock price volatility, stock market capitalization, foreign direct and portfolio investments. For developing countries, there is also an increase in the stock market capitalization, portfolio investments and the volume of stock trading. Recognizing these trends can benefit both financial market regulators and participants to encourage the formation of a transparent and efficient financial market, thereby mitigating the problems associated with information asymmetry.

Crypto Analysis and Growth

Abstract: Cryptocurrencies have emerged as a significant asset class with unprecedented growth and market volatility. This paper presents an analysis of the growth patterns and market dynamics of specific cryptocurrencies, examining their historical performance, weekly top gainers and losers, and trading volumes. The study leverages comprehensive data sets and employs statistical analysis techniques to evaluate the growth trajectories of selected cryptocurrencies. By analyzing historical price data and market indicators, we assess the overall growth patterns, identify market trends, and highlight the most prominent gainers and losers within weekly intervals. This analysis provides valuable insights for investors, traders, and researchers seeking to understand the market dynamics and make informed decisions. Furthermore, the study investigates the trading volumes of cryptocurrencies, as volume plays a crucial role in assessing market liquidity and investor participation. We explore the relationship between trading volumes and price movements, examining whether higher volumes correlate with increased price volatility or stability. These insights contribute to a deeper understanding of market behavior and provide a foundation for developing trading strategies and risk management techniques. The findings of this research shed light on the performance and market dynamics of specific cryptocurrencies, enhancing our understanding of their growth patterns and investor sentiment. Analysis serves as a valuable resource for market participants, enabling them to identify potential investment opportunities, evaluate market trends, and manage risk effectively.