Order book modeling and financial stability

The impact of virtual bidding on price volatility in New York's wholesale electricity market

By performing Grey relation analysis, this study elucidates the relationship between investor sentiment and price volatility in the Taiwanese stock market. A sequential relationship is identified between investor sentiment and price volatility, and ranked according to order of importance. Analytical results show that short sales volumes may be an individual leading indicator useful in observing the effects of sentiment on price volatility, followed by open interest put/call ratios and trading volumes, and buy/sell orders. Institutional investors are related, to a lesser extent, to price volatility and sentiment. Qualified foreign institutional investors, or more rational investors, are the least influenced by price volatility, followed by securities investment trust companies and dealers. TAIEX options exert the strongest influence on sentiment during the study period, making them a valuable reference for gauging price volatility.

An understanding of changes in price volatility is of value to policy makers and exchange committee members as well as other participants in commodity futures markets. Previous research has studied volatility by measuring: 1) the flow of new information into the market, or 2) the effect that the structure of the futures market has on price volatility. In this paper, a model is developed which integrates these two themes in the literature to measure and explain price volatility in live cattle futures prices. The model is subjected to a battery of diagnostic tests so that a comparison can be made between the integrated model and models from previous research. Also, since price volatility from the underlying commodity is a major component in the determination of option premiums, a comparison is made between the integrated model and a naive model to forecast live cattle option premiums.

Uncertainty indexes are used in economics to assess the uncertainty regarding economic policy, and in finance to quantify the uncertainty associated with the price volatility of financial instruments and commodities. Existing uncertainty indexes are typically constructed using realized (historical) price volatility, implied volatility when options are available, and conditional volatility using various econometric models. In this article, the authors present an improved commodity price uncertainty index, an oil price uncertainty index that includes information from news articles and social media. They demonstrate the superior performance of their new index compared with existing indexes that do not include news and social media information. They also create an oil price volatility forecasting model based on media coverage and use it to trade an oil volatility portfolio, obtaining a return that exceeds the average monthly growth of oil price volatility. Altogether, media coverage is a powerful predictor of future price volatility in the oil market. TOPICS:Volatility measures, performance measurement Key Findings • Media information from newspapers and Twitter about oil price uncertainty is combined with the existing OVX oil ETF volatility index to create a new oil price uncertainty index (OPUX). • The OPUX model gives a significantly better prediction of 1-month forward oil price volatility, as measured by an increase in R-squared from 12.1% for OVX to 33.0% for OPUX. • A volatility trading model based on OPUX exceeds the average oil price volatility by 0.29% per month.

Dry bulk shipping market is a key element of international trade. Newbuilding vessel prices tend to fluctuate drastically, however the price volatility mechanism and major determinants were neglected, regardless of the abundant research on newbuilding price formation and price determinants. This paper applied the GARCH (1, 1) model to investigate the key determinants of newbuilding vessel price volatility and how freight rate affects it. The empirical results revealed that freight rate volatility is the most important and the positive determinant for newbuilding price volatility in capesize, panamax, handymax and handysize sectors. Shipyard capacity change, exchange rate volatility and shipbuilding cost volatility are key determinants for some vessel types, while secondhand vessel price volatility is not the key determinant for any vessel type. Our findings could help the practitioners better understanding the market dynamics and managing the market risks.

The stock price volatility has been a problem in stock markets affecting the stock returns of firms listed at Nairobi Securities Exchange (NSE). Although some scholars have focused on the issue of high share price volatility, the issue persists and still hinders the development of the market. This study main objective is to determine the effects of macroeconomic factors on volatility of stock prices of stock market in Kenya. The specific objectives are; to establish the effects of inflation on stock prices of stock market in Kenya; to establish the effects of interest rates on volatility of stock prices of stock market in Kenya; and to establish the effects of exchange rates on stock prices of stock market in Kenya. The research was anchored on four theories and two models namely the Arbitrage pricing theory, Modern portfolio theory, information cascade theory, Fisher’s theory, Efficient market hypothesis and present value model. The research applied a causal research design, which followed the positivism philosophy of research and knowledge creation. The study focused on analyzing the effects of macroeconomic factors on volatility of stock prices of the NSE 20 share index in Nairobi Securities Exchange over a period ranging from 2009 to 2021. The design allowed collection of monthly data on the study variables and time series analysis of the hypothesized relationships between independent and dependent variables and conducted both descriptive statistical analysis and inferential statistical analysis. Descriptive statistical analysis involved generation of central tendency statistics such as mean, minimum and maximum points of the data as well as standard deviations to show the spread of the data from the mean. The inferential statistical analysis utilized in this study was Error Correction Model, guided by the results of cointegration tests. From the descriptive statistical analysis, the study found that between 2009 and 2021, interest rates demonstrated an average of approximately 9.155, inflation averaged around 150.916, foreign exchange rate maintained an average of roughly 94.219, and stock price volatility averaged 0.4%. The Error Correction Model revealed that, interest rates have no significant effect on stock price volatility of NSE 20 share index (β = -0.00034, p.value = 0.805). Similarly, inflation has no significant effect on stock price volatility of NSE 20 share index (β = -0.0000394, p.value = 0.218). However, foreign exchange has negative and significant effect on stock price volatility in Kenya (β = -0.0139, p.value = 0.017). The study concluded that interest rates and inflation have no significant effect on stock price volatility in Kenyan stock market, while foreign exchange has inverse and significant effect on stock price volatility. Thus, the study recommends that market participants, including government, investors, traders, policy makers and financial institutions, should incorporate foreign exchange rate movements into their risk management strategies. Adequate measures and policies such as market circuit breakers and enhanced risk management protocols should be in place to ensure smooth market operations and prevent any disruptions due to changing foreign exchange dynamics

ABSTRACTAquaculture production is increasingly important in seafood production and is currently the fastest growing food production technology worldwide. To continue the industry growth, price risk management is an essential part in creating successful projects and sustainable business operations. This research uses trade data on fish and invertebrate prices, and evaluates the relative price uncertainty from farmed and captured products. Moreover, we consider a set of species groups (fish, invertebrates), species’ families (e.g., Salmonidae, white fish and crustaceans), and individual species (e.g., salmon, seabream and prawns), and evaluate their price volatility over time using Value-at-Risk (VaR). The results indicate that the introduction of aquaculture production to a species’ total supply will reduce price volatility. Consequently, incentives and regulations should support research and development of new aquaculture species.

If the volatility of stock market prices is to be understood in terms of the efficient markets hypothesis, then there should be evidence that true investment value changes through time sufficiently to justify the price changes. Three indicators of change in true investment value of the aggregate stock market in the United States from 1871 to 1986 are considered: changes in dividends, in real interest rates, and in a direct measure of intertemporal marginal rates of substitution. Although there are some ambiguities in interpreting the evidence, dividend changes appear to contribute very little toward justifying the observed historical volatility of stock prices. The other indicators contribute some, but still most of the volatility of stock market prices appears unexplained.

This paper examines the causality between open market repurchase announcements and price volatility in Indian Stock Market. To assess the stock price reactions to open market repurchase public announcements, a random sample of two companies (Infosys and IIFL) have been selected from all companies that declared open market repurchase between June 2020 and March 2022. The analysis is conducted using the GARCH model, which is based on the diagnostic features of time series data, and the outputs of the repurchase announcement analysis of both sample companies are negative. There appears to be a slight decrease in price volatility, however, the dummy coefficient is considered to be statistically insignificant in case of Infosys and significant in case of IIFL, indicating that impact on price volatility leads to asserted outcomes. This can be explained by the fact that, in India, annual and interim dividend payments have been the preferred forms of dividend payment for many years, and open market share repurchases were not accepted as cash payments by investors. In India, the sentiments and dynamics of investor community seem to be changing fairly recently and the preconceived notion of share buybacks in terms of acceptance as cash payments appears to be polemic.

In 2022–2023, price volatility in the grain market of the Russian Federation increased, which led to a significant decrease in the profitability of grain production and increased uncertainty in the industry. The objective of the study is to assess the impact of key market factors that determine the dynamics of prices for grain and leguminous crops in the Siberian Federal District (SFD) and to clarify the medium-term prospects for price growth for grain products. The research methods include time series analysis, correlation and regression analysis. In the Siberian Federal District, during the period under review, after peak values of RUB 17–18 thousand per 1 ton of grain and leguminous crops, prices fell below RUB 9 thousand per 1 ton, but since mid-2023, there has been an increase with stabilization at the level of RUB 12–13,5 thousand. The greatest correlation is observed between prices with wheat futures and the FAO global food inflation index. The influence of the grain stock factor should be considered insignificant (the correlation for 2022–2023 with prices was -0,42). There was virtually no correlation with the dollar exchange rate in 2023. Univariate and multivariate regression analysis between prices and the market factors considered did not reveal any stable regression relationships in the Siberian Federal District. The linear correlation coefficient (index) did not rise above 0.66 in any of the models, and nonlinear correlation was virtually absent. The best statistical characteristics are demonstrated by the model of dependence of prices in the Siberian Federal District on the cost of wheat futures and grain reserves in the Russian Federation, but it can only describe 43 % of the price variance. Internal factors that determine the variability of prices for grain and leguminous crops in the regions of the Siberian Federal District prevail: export duties on grain; purchase prices in intervention funds; domestic demand for grain; expectations of grain producers; competition in interregional markets.

This paper investigates the systemic risk spillovers from the oil and gold markets to China financial market. Specifically, this paper uses wavelet analysis methods combined with time-varying copula models to calculate the CoVaR to explore the risk tail correlation and the strength of systemic risk spillover effects among oil, gold and China financial market under time-frequency conditions. The empirical results are as follows: First, there is a systemic risk spillover from the oil and gold markets to China financial market with significant time-varying characteristics. Second, changes in oil prices exacerbate risk spillovers from China financial markets, while price volatility in the gold market has a moderating effect on its risk spillovers, with system-wide risk spillovers increasing significantly in times of major economic crisis events. Third, oil market has a greater intensity of systemic risk spillover to China financial market than the gold market. External economic events significantly affect the intensity of risk spillovers from oil to China financial market, and abnormal volatility shocks in internal stock markets significantly affect the intensity of risk spillovers from gold to China financial market more significantly than the impact of external events. Furthermore, in the frequency domain, the oil and gold markets have some lagging effect on the transmission of systemic risks to the China financial market.

In electricity markets, bilateral contracts (BC) are used to hedge against price volatility in the spot market. Pricing these contracts requires scheduling from either the buyer or the seller aiming to achieve the highest profit possible. Since this problem includes different players, a Generation Company (GC) and an Electricity Supplier Company (ESC) are considered. The approaches to solve this problem include the Nash Bargaining Solution (NBS) equilibrium and the Raiffa–Kalai–Smorodinsky (RKS) bargaining solution. The innovation of this work is the implementation of an algorithm based on the RKS equilibrium to find a compromise strategy when determining the concessions to be made by the parties. The results are promising and show that the RKS approach can obtain better results compared to the Nash equilibrium method applied to a case study.

Accurately predicting cryptocurrency prices is still a difficult task because of the extremely volatile nature of the market. This study introduces a new methodology combining web scraping, data analysis, and machine learning to further improve prediction accuracy. A live cryptocurrency monitors gathers data from various sources such as trading volumes, price volatility, and sentiment in market to create a rich data set. Feature engineering is used to convert raw data into useful inputs for machine learning algorithms to further enhance prediction functions. Utilizing Python libraries including Beautiful Soup, Pandas, Scikit-learn, and deep learning libraries, the correct predictive model is designed and strictly tested for precision, performance, data quality, usability, scalability, and cost. The proposed hybrid model is a combination of traditional statistical methods with deep learning models to overcome the constraints of conventional forecasting methodologies. The output reflects the performance of the model in identifying the trends in the market and rendering data-driven insights to traders and investors. Future studies can employ different data sources, including social media sentiment analysis, financial news articles, and web-based cryptocurrency forums, to enhance predictability. Further advancement in time series forecasting through deep learning models, including transformer models, may also enhance the precision of long-term forecasting. A deeper insight into how external forces, including government intervention, macroeconomic trends, and emerging blockchain technologies, would complement our understanding of cryptocurrency market dynamics. This study contributes to complementing predictive analytics in financial markets by providing useful insights to investors, researchers, and policymakers.

There has been considerable price volatility in the crude oil market since 1861. At times the yearly variations, as reported by the BP Statistical Review, have been minor but on occasion they have been quite significant. The small and large variations have been matched successfully with a Variable Shape Distribution (VSD) model. Remarkably, the comparison between actual price variations and those calculated by the VSD leads to a coefficient of determination (R2) larger than 0.98. Given this validation, we integrate the results with a Global Energy Market (GEM) model developed in 2007 that presented an oil consumption forecast to 2030. Thus far, the forecast has been in line with actual oil consumption to 2012. Contrary to statements made by various oil commentators, the integration of results suggests that the possibilities of seeing sudden, large and permanent increases in future oil prices are very low. Given the huge quantities of conventional and unconventional oil available at or below current market prices, society should be able to substitute between alternative sources long before depletion causes oil to become unduly expensive. Further, a case can be made that with the vast global oil resource base and the significant technological advances being implemented by the industry, oil prices could decrease in the future.

Volatility driven market in a generalized Lotka–Voltera formalism