Study exchange option pricing with stochastic volatility and correlation.
problem Pricing exchange options under stochastic volatility and correlation.
method Approximation using a closed-form solution with Taylor expansion.
result Numerical results show the effectiveness of the proposed method.
A new Bachelier model explains oil option volatility during the pandemic.
problem Describing and predicting the volatility surface of oil options during the pandemic.
method Additive Bachelier model with three parameters: volatility term structure, vol-of-vol, and skew.
result The model accurately describes the volatility surface and supports efficient pricing of exotic options.
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.
The Heston model is validated for option pricing using theoretical derivations and empirical market data.
problem Validating the Heston model for accurate option pricing.
method Theoretical derivations and empirical validations using Monte Carlo simulations and machine learning.
result The Heston model is robust and relevant for current financial markets.
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 surveys options pricing under arithmetic Brownian motion and derives formulas for various types of options.
problem The use of arithmetic Brownian motion in finance is not widely adopted.
method Risk-neutral valuation and derivation of formulas for European options under three types of underlying assets.
result Derivation of formulas for European options and partial differential equations for American options.
Deep Q-Learning system for straddle options in volatile markets.
problem High computational costs and unstable performance in high-volatility markets.
method Attention mechanisms in Transformer-DDQN, novel reward function, and resistance level identification.
result Transformer-DDQN model exhibits lowest maximum drawdown and highest average return.
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…
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 …
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…
Oil prices affect Russian banks' stability, with negative impacts from decreases.
problem The impact of international oil prices on Russian public banks' financial stability.
method Data from 17 Russian public banks (2008-2016), Pool Mean Group (PMG) estimator.
result An increase in international oil prices and price to book value ratio positively affects Russian public banks' stability in the long run, while negative shocks have the opposite effect.
Coronavirus impacts oil prices through volatility and direct effects.
problem Impact of coronavirus on oil prices and volatility.
method ARDL estimation controlling for financial volatility and US economic policy uncertainty.
result COVID-19 daily infections have a negative long-term impact on oil prices.
Study examines oil and US stock market interactions during coronavirus crisis.
problem Understanding the impact of coronavirus on oil and stock markets.
method Wavelet analysis of daily data from February 18, 2020 to August 15, 2020.
result Oil prices lead US stock prices at 3-5-day cycles during the first and second parts of March and April 2020.
This paper examines the short-run relationships between oil prices and GCC stock markets. Since GCC countries are major world energy market players, their stock markets may be susceptible to oil price shocks. To account for the fact that stock markets may respond nonlinearly to oil price shocks, we have examined both l…
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.
Study examines impact of oil and gold prices on Tehran Stock Exchange.
problem Impact of oil and gold prices on Tehran Stock Exchange.
method ARIMA-Copula model, cross-validation, Clayton copula.
result TSE is indirectly influenced by gold price through other factors such as oil; TSE is not independent of oil price volatility.
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.
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.
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…
This study analyzes global oil trade networks to assess their efficiency and robustness.
problem Dynamic monitoring and warning of international trade risks in global oil trade.
method Constructing unweighted and weighted global oil trade networks (OTNs) using UN Comtrade data from 1988 to 2017, and applying complex network theories.
result Efficiency of oil flows increases with complexity of OTNs, and weighted efficiency indicators highlight major events.
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.
A phase plot of the oil economy is built using the literature data of world oil production, price, and EROEI (Energy Returned on Energy Invested). An analogy between the oil economy and the Benard convection is proposed; some methods of interpretation and forecast of the system behavior are also shown based on "phase p…
Recently developed machine learning techniques, in association with the Internet of Things (IoT) allow for the implementation of a method of increasing oil production from heavy-oil wells. Steam flood injection, a widely used enhanced oil recovery technique, uses thermal and gravitational potential to mobilize and dilu…
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.
Paper uses neural networks to analyze oil price impact on Iranian stock and industry indices.
problem Impact of oil price volatility on Tehran stock and industry indices.
method Feed-forward neural networks analysis of two periods: sanctions and post-sanctions.
result Neural networks predict stock and industry indices well, showing significant oil price volatility impact.
One major hurdle in the road toward a low carbon economy is the present entanglement of developed economies with oil. This tight relationship is mirrored in the correlation between most of economic indicators with oil price. This paper addresses the role of oil compared to the other three main energy commodities -coal,…
Study examines value relevance of oil and gas reserve disclosures in London Stock Exchange.
problem Uncertainty in oil and gas reserves poses accounting challenges for investors.
method Empirical analysis using archival data and multifactor framework.
result Changes in reserves and their components are associated with share returns, but insignificantly due to oil price and longitudinal effects. Quality of disclosures positively impacts share returns.
Study shows oil prices but not COVID-19 cases affect US economic policy uncertainty.
problem Effect of COVID-19 and crude oil prices on US economic policy uncertainty.
method Used ARDL model with daily data from January 21-March 13, 2020.
result Crude oil price dynamics increase US economic policy uncertainty, while COVID-19 cases have mixed effects.
Deep learning outperforms Black-Scholes in Brazilian Petrobras option pricing.
problem Improving option pricing accuracy for Petrobras stocks.
method Trained deep residual networks using a custom loss function with historical data.
result Deep learning achieved a 64.3% reduction in mean absolute error compared to Black-Scholes.
The paper presents a method for detecting jump sizes in crude oil prices.
problem Detecting jump sizes in crude oil price data.
method Sequential hypothesis testing using infinitesimal generators and super-solutions.
result The method improves the Barndorff-Nielsen and Shephard model for derivative and commodity market analysis.
Oil markets profoundly influence world economies through determination of prices of energy and transports. Using novel methodology devised in frequency domain, we study the information transmission mechanisms in oil-based commodity markets. Taking crude oil as a supply-side benchmark and heating oil and gasoline as dem…
Wavelet analysis reveals financialization effects on oil-food price correlation.
problem Investigating the correlation between oil and food prices and their determinants.
method Wavelet analysis and energy-based measures to differentiate high and low frequency movements.
result Significant local correlation between food and oil is due to financialization and emerging economies' demand.
This paper uses SampEn to measure and predict oil price volatility.
problem Measuring and predicting volatility in international oil prices.
method Sample Entropy (SampEn) compared with standard deviation; machine learning algorithms used.
result SampEn effectively predicts traditional volatility measures, especially during financial crises.
Methodology that recently lead us to predict to an amazing accuracy the date (July 11, 2008) of reverse of the oil price up trend is briefly summarized and some further aspects of the related oil price dynamics elaborated. This methodology is based on the concept of discrete scale invariance whose finance-prediction-or…
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 …
Investment risk on a regulated market is influenced by gold prices and oil trading.
problem Systematic risk of loss in investment portfolios under sanctions.
method Statistical analysis of tail dependence between oil, gold, and Tehran Stock Exchange Index.
result Tail dependence should be considered for systematic risk, and active bartering of oil can prevent market collapse.
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 shows adding correlated features doesn't improve LSTM model interpretability for oil stocks.
problem Improving interpretability of LSTM models for predicting oil company stocks.
method Designed and trained Standard LSTM networks using various correlated datasets.
result Adding correlated features does not enhance LSTM model interpretability.
The study finds a long-term relationship between Dubai crude oil and US natural gas prices.
problem Examining the relationship between Dubai crude oil and US natural gas prices.
method Used unit root and cointegration tests, ARDL cointegration technique, and Toda-Yamamoto causality test.
result There is a long-run relationship with unidirectional causality from Dubai crude oil to US natural gas.
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.
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.
This paper analyzes the direction of the causality between crude oil, gold and stock markets for the largest economy in the world with respect to such markets, the US. To do so, we apply non-linear Granger causality tests. We find a nonlinear causal relationship among the three markets considered, with the causality go…
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…
Hybrid approach improves crude oil price forecasting using multi-scale data.
problem Forecasting crude oil prices with multi-scale data.
method Hybrid approach combining K-means, KPCA, and KELM.
result Hybrid approach outperforms traditional methods in both level and directional forecasting accuracy.
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 …
We develop a proxy model based on deep learning methods to accelerate the simulations of oil reservoirs--by three orders of magnitude--compared to industry-strength physics-based PDE solvers. This paper describes a new architectural approach to this task, accompanied by a thorough experimental evaluation on a publicly …
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.
Belief networks are a new, potentially important, class of knowledge-based models. ARCO1, currently under development at the Atlantic Richfield Company (ARCO) and the University of Southern California (USC), is the most advanced reported implementation of these models in a financial forecasting setting. ARCO1's underly…