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Volatility spillovers between food commodities and oil prices have been identified in the literature, yet, there has been no empirical evidence to suggest that oil price volatility improves real out-of-sample forecasts of food price volatility. In this study we provide new evidence showing that oil price volatility does not improve forecasts of agricultural price volatility. This finding is based on extensive and rigorous testing of five internationally traded agricultural commodities (soybeans, corn, sugar, rough rice and wheat) and two oil benchmarks (Brent and WTI). We employ monthly and daily oil and food price volatility data and two forecasting frameworks, namely, the HAR and MIDAS-HAR, for the period 2nd January 1990 until 31st March 2017. Results indicate that oil volatility-enhanced HAR or MIDAS-HAR models cannot systematically outperform the standard HAR model. Thus, contrary to what has been suggested by the existing literature based on in-sample analysis, we are unable to find any systematic evidence that oil price volatility improves out-of-sample forecasts of food price volatility. The results remain robust to the choice of different out-of-sample forecasting periods and three different volatility measures.

A model is proposed to investigate the effects of power generation source diversification and CO2 emission control in the presence of dispatchable fossil fuel sources and non-dispatchable carbon-free renewables. In a stochastic environment in which three random factors are considered, namely fossil fuels (gas and coal) and CO2 prices, we discuss a planning methodology for power system portfolio selection that integrates the non-dispatchable renewables available in a given energy system and optimally combines cost, risk and CO2 emissions. By combining the deep neural network probabilistic forecasting of fossil fuel path prices with a geometric Brownian motion model for describing the CO2 price dynamics, we simulate a wide range of plausible market scenarios. Results show that under CO2 price volatility, optimal portfolios shift toward cleaner energy sources, even in the absence of explicit emission targets, highlighting the implicit regulatory power of volatility. The results suggest that incorporating CO2 price volatility through market mechanisms can serve as an effective policy tool for driving decarbonization. Our model offers a flexible and reproducible approach to support policy design in energy planning under uncertainty.

PurposeThe purpose of studying the impact of crude oil and natural gas prices on the Vietnamese stock market is to understand the relationship between energy prices and the overall performance of the financial markets. As Vietnam is an energy-dependent country, fluctuations in crude oil and natural gas prices can significantly affect various industries, including manufacturing, inflation, transportation, energy production and economic growth. These sectors are often sensitive to changes in energy costs, which can lead to shifts in corporate profitability and investor sentiment. By analyzing how crude oil and natural gas prices influence the Vietnamese stock market, policymakers and investors can provide deeper insights into the economic risks and opportunities related to energy price volatility. This paper can also provide valuable information for decision-making in sectors such as economic forecasting, risk management and investment strategies.Design/methodology/approachUsing monthly data from January 2006 to March 2024, data were collected from the Vietnamese stock market and the OPEC organization for oil prices, while data on natural gas were obtained from the EIA. The data were analyzed using vector error correction (VEC) model, impulse response function, variance decomposition test and asymmetric reactions method; the study tries to ascertain the short-term and long-term dynamic relationships between the shocks of the crude oil price and natural gas prices and their effects on the movement of the stock price. In addition, the GARCH model is applied to measure the volatility of crude oil and natural gas prices.FindingsCrude oil price shocks have a statistically significant impact on most Vietnamese real stock market indices, except for the utility and consumer indices and some energy companies. Conversely, natural gas price shocks do not significantly affect on Vietnamese stock market indices, except for the energy index and some energy companies. Some “important” of both crude oil price and natural gas price shocks tend to depress the stock returns of energy companies. An increase in both crude oil and natural gas volatility can lead to heightened speculation in certain indices, particularly the energy and industrial indices, as well as in some energy companies. This heightened speculation often results in elevated of their stock returns.Originality/valueThis study provides valuable insights into the field of study examining how fluctuations in the prices of oil and gas, particularly during major crisis periods such as global financial crisis, COVID-19 pandemic and the Russo-Ukrainian War, affect financial markets.

This paper investigates the effects of CO2 price volatility on optimal power system portfolios and on CO2 emissions assessment. In a stochastic setting in which three sources of uncertainty are considered, namely fossil fuels (gas and coal) and CO2 prices, we discuss a unifying scheme for quantifying the impact of integrated environmental and renewable energy policies on the power system. We will show that the effects produced by a given environmental policy scheme strongly depend on the configuration of the power system, i.e., on the composition of the generating sources in the power system portfolio. In the empirical analysis performed on U.S. technical and cost data, we found that a non-volatile carbon tax scheme can produce significant effects on the power system portfolio selection problem in the presence of a carbon-free dispatchable source, like nuclear power, but it may have a negligible impact if the (non-renewable) dispatchable part of the power system portfolio is fully composed by fossil fuel, gas and coal, sources. On the other side, generating CO2 price volatility market-oriented mechanisms can produce relevant effects on both power system configurations. Although the empirical analysis is performed on U.S. data, the proposed methodology is general and can be used as a quantitative support by policy makers in their attempts to reconcile environmental and economic issues.

Today, few firms could survive for very long without their computer systems. IT has permeated every corner of firms. Firms have reached the current state in their use of IT because IT has provided myriad opportunities for firms to improve performance and, firms have availed themselves of these opportunities. Some have argued, however, that the opportunities for firms to improve their performance through new uses of IT have been declining. Are the opportunities to use IT to improve firm performance diminishing? We sought to answer this question. In this study, we develop a theory and explain the logic behind our empirical analysis; an analysis that employs a different type of event study. Using the volatility of firms' stock prices to news signaling a change in economic conditions, we compare the stock price behavior of firms in the IT industry to firms in the utility and transportation and freight industries. Our analysis of the IT industry as a whole indicates that the opportunities for firms to use IT to improve their performance are not diminishing. However, there are sectors within the IT industry that no longer provide value-enhancing opportunities for firms. We also find that IT products that provided opportunities for firms to create value at one point in time, later become necessities for staying in business. Our results support the key assumption in our work.

The effects of shocks on oil prices will always attract the interest of researchers and policy makers as long as such countries are oil revenue dependent. This is the case in the Central African Economic and Monetary Community (CEMAC) where 70% of the regional countries are net oil income earners. In this study, an in-depth investigation was carried out on the short and long run dynamics between monetary policy, oil price volatility and economic growth in the oil producing CEMAC countries. The target countries are Cameroon, Chad, the Democratic Republic of Congo, Equatorial Guinea, Gabon and the Republic of Congo, and the data for the study covered 1980-2018, a period of 38 years. The study employed the panel autoregressive distributed lag model for the short- and long-run dynamics, while a structural vector autoregressive (SVAR) model was employed for shocks and spillover effects. The results identified oil price volatility, GDP growth rate and exchange rate as highly influential variables in the long run, while exchange rate and GDP growth rate only have significant short run influences on monetary policy rates in the region. The countries of the region need to intensify efforts towards the diversification of individual economic base, reduce the importation of foreign goods and formulate monetary policies that will strengthen their currencies and boost the growth potential in the communities.

With the ever-increasing penetration level of renewable energy generation in a power system, more uncertainties are introduced and hence risk management in the electricity market associated is becoming a more difficult issue for a market participant in the context of optimising his/her portfolio. Among a lot of risk factors in the competitive electricity market environment, the highly volatile electricity price contributes most to the financial risk of the power portfolio, especially in a short-term risk management scenario such as the spot market and real-time balancing market. Some research work has shown that the fluctuations of electricity prices exhibit multifractal characteristics, but less work has been done on the price volatility risk evaluation based on the multifractal theory. This study hence examines the feasibility of applying the multifractal theory to analyse the electricity price fluctuation, and applies the multifractal theory for evaluating the financial risk caused by electricity price volatility. A modified return interval approach considering the parameters of multifractal characteristics is employed to estimate the value-at-risk (VaR) of the electricity price. The fluctuant electricity price data series in the Pennsylvania-New Jersey-Maryland energy market are employed to demonstrate the effectiveness of the proposed VaR estimation method for short-term electricity price volatility risk evaluation.

We examine the role of high-frequency traders (HFTs) in price discovery and price efficiency. Overall HFTs facilitate price efficiency by trading in the direction of permanent price changes and in the opposite direction of transitory pricing errors, both on average and on the highest volatility days. This is done through their liquidity demanding orders. In contrast, HFTs' liquidity supplying orders are adversely selected. The direction of HFTs' trading predicts price changes over short horizons measured in seconds. The direction of HFTs' trading is correlated with public information, such as macro news announcements, market-wide price movements, and limit order book imbalances.

The food-energy nexus has attracted great attention from policymakers, practitioners, and academia since the food price crisis during the 2007–2008 Global Financial Crisis (GFC), and new policies that aim to increase ethanol production. This paper incorporates aggregate demand and alternative oil shocks to investigate the causal relationship between agricultural products and oil markets. For the period January 2000–July 2018, monthly spot prices of 15 commodities are examined, including Brent crude oil, biofuel-related agricultural commodities, and other agricultural commodities. The sample is divided into three sub-periods, namely: (i) January 2000–July 2006, (ii) August 2006–April 2013, and (iii) May 2013–July 2018. The structural vector autoregressive (SVAR) model, impulse response functions, and variance decomposition technique are used to examine how the shocks to agricultural markets contribute to the variance of crude oil prices. The empirical findings from the paper indicate that not every oil shock contributes the same to agricultural price fluctuations, and similarly for the effects of aggregate demand shocks on the agricultural market. These results show that the crude oil market plays a major role in explaining fluctuations in the prices and associated volatility of agricultural commodities.