Study reveals dynamic linkage between Peanut and Soybean Oil futures markets.
problem Exploring interdependence between Peanut and other agricultural commodities in Chinese futures market.
method Constructed multivariate linear regression models and used VAR and DCC-EGARCH models for dynamic relationships. Applied MLP, CNN, and LSTM neural networks for price prediction.
result Significant dynamic linkage between Peanut and Soybean Oil futures markets through DCC-EGARCH, limited influence from other futures markets through VAR model.
Hidden Markov model predicts profitable statistical arbitrage in Shanghai crude oil futures.
problem Statistical arbitrage opportunities in international crude oil futures markets.
method Hidden Markov model for cointegration spread, mean-reverting regime-switching process.
result Statistical arbitrage strategies involving Shanghai crude oil futures are profitable.
The study examines how global economic policy uncertainty affects crude oil futures volatility.
problem Predicting crude oil futures volatility using global economic policy uncertainty.
method Established single-factor and two-factor models under the GARCH-MIDAS framework, tested with rolling-window and fixed-span specifications.
result GEPU changes have stronger predictive power than the GEPU index for crude oil futures volatility.
Study shows post-COVID commodity futures returns and volatility changed for different products.
problem Analyzing how the pandemic affected Chinese commodity futures markets.
method Empirical analysis of commodity futures returns and cointegration before and after the pandemic.
result Post-COVID, some commodity futures returns increased significantly, while others saw higher volatility.
Study measures risk spillovers between US and China's agricultural futures markets.
problem Interconnectedness and risk transmission in agricultural futures markets.
method TVP-VAR-DY model with quantile method.
result CBOT corn, soybean, and wheat are primary risk transmitters; DCE corn and soybean are main receivers.
The paper analyzes gold, oil, and bitcoin futures volatility and basis.
problem Understanding the volatility and basis of gold, oil, and bitcoin futures.
method Contract-by-contract analysis of spot and futures prices, trading volume, and open interest data.
result Trading volume positively affects volatility in all three assets, while open interest has a possible negative effect.
We present a new Monte-Carlo methodology to forecast the crude oil production of Norway and the U.K. based on a two-step process, (i) the nonlinear extrapolation of the current/past performances of individual oil fields and (ii) a stochastic model of the frequency of future oil field discoveries. Compared with the stan…
Study shows how COVID-19 pandemic affected China's crude oil futures market efficiency.
problem Impact of COVID-19 on China's crude oil futures market efficiency.
method Multifractal analysis to compare market efficiency before and during the pandemic.
result Market efficiency of SC and its cross-correlations with other assets increased significantly after the outbreak of COVID-19.
Study uses a bivariate model to price crude oil futures.
problem Pricing crude oil futures using latent factors and state-space models.
method Modelled short and long term factors as OU processes, estimated using Kalman Filter and maximised Gaussian likelihood.
result Successfully estimated model parameters and factors from WTI Crude Oil NYMEX futures data.
The growing conflicts in and about oil exporting regions and speculations about volatile oil prices during the last decade have renewed the public interest in predictions for the near future oil production and consumption. Unfortunately, studies from only 10 years ago, which tried to forecast the oil production during …
Model assesses systemic risk in crude oil and gasoline futures markets.
problem Systemic risk in high-frequency crude oil and gasoline futures markets.
method Hawkes flocking model examining endogeneity and interactivity.
result Significantly higher endogenous systemic risk in WTI crude oil compared to gasoline, with gasoline having a higher influence on WTI.
We perform detrending moving average analysis (DMA) and detrended fluctuation analysis (DFA) of the WTI crude oil futures prices (1983-2012) to investigate its efficiency. We further put forward a strict statistical test in the spirit of bootstrapping to verify the weak-form market efficiency hypothesis by employing th…
The paper contributes to the rare literature modeling term structure of crude oil markets. We explain term structure of crude oil prices using dynamic Nelson-Siegel model, and propose to forecast them with the generalized regression framework based on neural networks. The newly proposed framework is empirically tested …
For the first time, we apply the wavelet coherence methodology on biofuels (ethanol and biodiesel) and a wide range of related commodities (gasoline, diesel, crude oil, corn, wheat, soybeans, sugarcane and rapeseed oil). This way, we are able to investigate dynamics of correlations in time and across scales (frequencie…
Study analyzes crude oil futures markets using visibility graphs to understand their structure and dynamics.
problem Understanding the structure and dynamics of crude oil futures markets during global challenges.
method Visibility graph analysis of daily and high-frequency data.
result Crude oil futures markets exhibit small-world properties and assortative mixing, with unique sensitivities to global disruptions.
We propose a comprehensive treatment of the leverage effect, i.e. the relationship between returns and volatility of a specific asset, focusing on energy commodities futures, namely Brent and WTI crude oils, natural gas and heating oil. After estimating the volatility process without assuming any specific form of its b…
This paper presents a model based on multilayer feedforward neural network to forecast crude oil spot price direction in the short-term, up to three days ahead. A great deal of attention was paid on finding the optimal ANN model structure. In addition, several methods of data pre-processing were tested. Our approach is…
Improved crude oil price forecasting using multi-dimensional LLM sentiment signals.
problem Challenges in predicting crude oil prices due to unstructured news.
method Extracted five sentiment dimensions from GPT-4o, Llama 3.2-3b, and FinBERT models on energy-sector news articles.
result Combining GPT-4o and FinBERT yields the best predictive performance for weekly WTI crude oil futures returns.
New risk measures incorporate economic states to assess crude oil derivatives.
problem Assessing risk in crude oil derivatives with varying economic conditions.
method Introduced regime switching entropic risk measures using Markov chains.
result Closed formulae for risk measures derived, showing term structure and mean-reverting convenience yield.
Statistical and multiscaling characteristics of WTI Crude Oil prices expressed in US dollar in relation to the most traded currencies as well as to gold futures and to the E-mini S&P500 futures prices on 5 min intra-day recordings in the period January 2012 - December 2017 are studied. It is shown that in most of th…
Study examines spillovers between BRICS and U.S. staple grain futures markets.
problem Contemporaneous and lagged spillover effects in BRICS staple grain futures markets and their linkages with U.S. markets.
method Examines contemporaneous and lagged spillover effects using econometric models.
result Contemporaneous spillovers dominate, and net spillovers are driven by lagged connectedness. Systemic risk is lower in intra-BRICS markets compared to those including the U.S.
Crude oil is a major component in most advanced economies of the world. Accurately predicting and understanding the behavior of crude oil prices is important for economists, analysts, forecasters, and traders, to name a few. The price of crude oil has declined in the past decade and is seeing a phase of stability; but …
In this article we present a continuous time model for natural gas and crude oil future prices. Its main feature is the possibility to link both energies in the long term and in the short term. For each energy, the future returns are represented as the sum of volatility functions driven by motions. Under the risk neutr…
Study uses APT and QR to identify risk factors affecting crude oil returns.
problem Determining the risk factors impacting crude oil returns.
method Employed Arbitrage Pricing Theory and Quantile Regression.
result Identified key risk factors: industrial production, inflation, energy prices, yield curve shape, and economic policy uncertainty.
Model prices commodity futures and index options.
problem Deriving accurate prices for derivative contracts on commodity futures and indices.
method Stochastic local volatility model for commodity futures.
result Model accurately recovers prices of derivative claims.
This paper estimates VaR for corn and soybean markets using jump processes.
problem Quantifying potential losses in commodity portfolios under market conditions.
method Modeling VaR for a diversified portfolio of corn and soybean positions with standard Brownian motions and jump processes.
result Compared VaR values in markets with and without jumps, providing insights for risk management.
Eradicating hunger and malnutrition is a key development goal of the 21st century. We address the problem of optimally identifying seed varieties to reliably increase crop yield within a risk-sensitive decision-making framework. Specifically, we introduce a novel hierarchical machine learning mechanism for predicting c…
Paper introduces new indicators for forecasting crude oil prices using short news headlines.
problem Forecasting crude oil prices from short, noisy news headlines using LDA.
method Developed two novel indicators for topic and sentiment from short text data, and applied AdaBoost.RT.
result AdaBoost.RT with the proposed indicators outperforms benchmarks in crude oil forecasting.
Study reviews Bachelier model for negative oil prices post-COVID.
problem Negative oil futures prices due to COVID-19.
method Literature review and model comparison.
result Connects Black-Scholes and Bachelier models for option pricing.
A new multi-factor model improves commodity pricing accuracy.
problem Enhancing accuracy in commodity pricing by integrating multiple risk factors.
method A four-factor model using Kalman filter for simultaneous estimation and state variable filtering.
result The four-factor model outperforms existing models in capturing futures term structures and crude oil pricing.
We study a stochastic control approach to managed futures portfolios. Building on the Schwartz 97 stochastic convenience yield model for commodity prices, we formulate a utility maximization problem for dynamically trading a single-maturity futures or multiple futures contracts over a finite horizon. By analyzing the a…
Study examines grain futures connectedness during Russia-Ukraine conflict.
problem Quantile return connectedness of grain futures markets during geopolitical instability.
method Dynamic quantile VAR combined with frequency-domain decomposition.
result Heterogeneous spillovers across quantiles, with strong transmitters and persistent receivers.
Model predicts methane emissions from oil sands tailing ponds, suggesting significant environmental impact.
problem Estimating methane emissions from inactive oil sands tailing ponds.
method Physics constrained machine learning model using real-time weather data and laboratory experiments.
result Active oil sands tailing ponds emit between 950 to 1500 tonnes of methane per year, equivalent to 6000 gasoline vehicles.
We introduce a multi-factor stochastic volatility model for commodities that incorporates seasonality and the Samuelson effect. Conditions on the seasonal term under which the corresponding volatility factor is well-defined are given, and five different specifications of the seasonality pattern are proposed. We calcula…
Research uses SWT and BDLSTM to forecast stock and oil prices amid COVID-19.
problem Impact of COVID-19 on stock and oil prices forecasting.
method Integrates Stationary Wavelet Transform and Bidirectional Long Short-Term Memory networks.
result BDLSTM+WT-ADA achieved satisfactory results in Crude Oil price forecasting.
We analyze the market efficiency of 25 commodity futures across various groups -- metals, energies, softs, grains and other agricultural commodities. To do so, we utilize recently proposed Efficiency Index to find that the most efficient of all the analyzed commodities is heating oil, closely followed by WTI crude oil,…
Bayesian neural networks improve uncertainty in data-driven VFMs for oil and gas wells.
problem Uncertainty and robustness in data-driven VFMs for oil and gas wells.
method Bayesian neural networks with variational inference for uncertainty quantification.
result Variational inference provides more robust predictions on future data.
The study improves gold's role as a hedge and safe haven using new correlation measures.
problem Investing in gold for medium and extreme market movements.
method Developed a new correlation measure based on fractal approach.
result Gold is a better hedge than safe haven for extreme market movements.
We develop a Vector Quantized Spectral Clustering (VQSC) algorithm that is a combination of Spectral Clustering (SC) and Vector Quantization (VQ) sampling for grouping Soybean genomes. The inspiration here is to use SC for its accuracy and VQ to make the algorithm computationally cheap (the complexity of SC is cubic in…
In a highly interdependent economic world, the nature of relationships between financial entities is becoming an increasingly important area of study. Recently, many studies have shown the usefulness of minimal spanning trees (MST) in extracting interactions between financial entities. Here, we propose a modified MST n…
It is commonly accepted that Commodities futures and forward prices, in principle, agree under some simplifying assumptions. One of the most relevant assumptions is the absence of counterparty risk. Indeed, due to margining, futures have practically no counterparty risk. Forwards, instead, may bear the full risk of def…
The paper develops a new model for rough volatility in commodity markets.
problem Calibration of rough volatility models for commodity futures prices.
method Developed a general rough volatility model with automatic calibration and treatment of the Samuelson effect.
result Calibrated rBergomi and rHeston models to WTI Crude Oil futures options data.
Russia-Ukraine conflict impacts global agricultural futures and spot markets' extreme risks.
problem Impact of Russia-Ukraine conflict on global agricultural futures and spot markets' extreme risks.
method Analytical framework for tail dependence, Copula-CoVaR method, ARMA-GARCH-skewed Student-t model.
result The outbreak of the conflict intensified risks in the wheat market the most and showed significant asymmetries in extreme risk spillovers.
Risk is an inherent feature of agricultural production and marketing and accurate measurement of it helps inform more efficient use of resources. This paper examines three tail quantile-based risk measures applied to the estimation of extreme agricultural financial risk for corn and soybean production in the US: Value …
Study improves prediction of commodity futures using multi-factor model.
problem Improving accuracy in predicting commodity futures prices.
method State-space functional regression model incorporating yield curve dynamics.
result Functional regression model outperforms Schwartz-Smith model in estimating short-end of futures curve.
Using a two-point correlation technique, we study emergence of market efficiency in the emergent Russian futures market by focusing on lagged correlations. The correlation strength of leader-follower effects in the lagged inter-market correlations on the hourly time frame is seen to be significant initially (2009-2011)…
The dissertation investigates the application of Probabilistic Graphical Models (PGMs) in forecasting the price of Crude Oil. This research is important because crude oil plays a very pivotal role in the global economy hence is a very critical macroeconomic indicator of the industrial growth. Given the vast amount of m…
This paper proposes a novel selective autoencoder approach within the framework of deep convolutional networks. The crux of the idea is to train a deep convolutional autoencoder to suppress undesired parts of an image frame while allowing the desired parts resulting in efficient object detection. The efficacy of the fr…