Crude Oil, Natural Gas and Refined Products Price Modeling, Forecast and Predictions

Why Select this Training Course?

An essential component of decision-making in the energy sector is oil prices. Stakeholders in the energy industry are constantly confronted with factors and data that could negatively or positively impact oil prices. These data are drawn from analysis of geopolitical events, changes in supply and demand, and the financial markets.

What are the factors that determine the price of oil?

As a crucial aspect of the global economy, various factors play a role in determining oil prices. First, demand and supply are vital factors that impact oil prices. An increased supply of natural gases typically results in reduced oil prices, while a decrease in supply means higher oil prices. Aside from these factors, other factors that impact the prices of oil are:

• The level of oil in storage
• Volumes of oil import and export
• Investing in oil and gas drilling
• Temporary price fluctuations

What are the five pricing models used in forecasting energy prices?

Forecasting involves predicting the prices of energy based on the consideration of various factors. According to the economic forecasting theory, natural gas and crude oil prices are predictable based on the equilibrium between demand and supply. However, prices of energy and energy products are often subject to fluctuations, and as a result, experts have developed five pricing models for natural gas and crude oil. These five models include oil futures prices, Bayesian autoregressive models, regression-based structural models, time-series analysis, and dynamic stochastic general equilibrium graphs.

The Rcademy Crude Oil, Natural Gas, and Refined Products Price Modelling, Forecast, and Predictions training course aim to provide participants with the rudiments of price modeling and forecasting in the crude oil and refined products industry. Participants will also learn the factors influencing crude oil prices and how to analyze and predict energy prices. Focus is also given to the role of machine learning in forecasting the cost of energy and energy products. Upon conclusion of the course, participants will have mastered oil and gas price modeling using different supporting techniques.

Who Should Attend?

The Rcademy Crude Oil, Natural Gas, and Refined Products Price Modelling, Forecast and Predictions Training Course are designed for a wide range of professionals within the oil and gas sectors and individuals who wish to learn about crude oil, natural gas, refined product price modeling, forecasting, and predictions. The following personnel should undertake the course:

• Financial Managers: tasked with performing financial analyses and managing the finances of an organization or government body
• Financial Planners: responsible for meeting the short-term and long-term financial needs of clients
• Maintenance supervisors: tasked with supervising the safe and efficient management of refinery equipment
• Energy traders: charged with facilitating transactions among buyers and sellers of energy products
• Risk managers: responsible for managing the risks of a firm, its reputation, assets, employees, and interests of stakeholders
• Quantitative analysts: tasked with developing and implementing complex models to solve risk management and financial challenges
• Investment bankers: charged with helping oil firms profit from financial services such as debt and equity financing
• Policymakers: responsible for researching and evaluating energy data and developing policies that impact the energy sector
• Chief Accounting Officers: responsible for overseeing the accounting functions of a company and also ensuring the company is tax compliant
• Energy forecasting analysts: tasked with applying mathematical and statistical modeling to design short and long-term energy forecasts
• Energy analysts: responsible for evaluating data on energy use, analyzing energy efficiency, and designing energy models for oil and gas companies
• Professionals interested in learning about the principles and practice of price modeling and forecasting in the crude oil and natural gas industry

What are the Course Objectives?

The Rcademy Crude Oil, Natural Gas, and Refined Products Price Modelling, Forecast and Predictions Training Course are geared towards assisting participants in attaining the following objectives:

• Understand how to manage and optimize an organization’s energy risk exposure
• Understand the principles involved in crude oil and natural gas price modeling and forecasting
• Recognize estimated returns and how to calculate volatilities in energy prices
• Identify the basics of Excel mechanics and its functionality in the oil and gas sectors
• Execute both comparable company and transaction analysis
• Understand how to implement valuation and financial modeling best practices
• Learn about option pricing and the factors that affect crude oil and natural gas price modeling
• Understand how to utilize machine learning in crude oil and natural gas forecasting
• Understand the impacts of forecasting and prediction analysis on the energy sector

How will this Course be Presented?

This course is curated to meet participants’ satisfaction while improving their knowledge and skill; it is purely participant-oriented. Different practical approaches to ensure active and constant learning by the attendees will be utilized to deliver the course. Experts who will teach the course within this field have gathered numerous experiences and practice. The course modules are curated from extensive and in-depth research on the subject matter.

The Rcademy course on Crude Oil, Natural Gas, and Refined Products Price Modelling, Forecast, and Predictions includes theoretical and practical learning by providing slides on the concept, case studies, real-life scenarios, and lecture notes. In addition, participants will be able to partake in seminar workshops, quizzes, presentations, and regular feedback on lessons learned to confirm their optimum satisfaction.

What are the Topics Covered in this Course?

Module 1: Introduction to Crude Oil and Natural Gas Price Modelling

• Introduction and definition of terms
• Forecasting natural gas and crude oil prices
• Standard formatting best practices
• The importance of volume and price hedges
• Excel best practices
• Designing comprehensive crude oil and natural price models
• Fixing iteration, circularity, and other common modeling problems
• Using data to present sensitivities to projected financial metrics
• Balancing accounts, including excess cash and revolver

Module 2: Forecasting the Prices of Crude Oil, Natural Gas, and Refined Products

• The market price of risk
• Using regression analysis
• Observing forecast prices
• Applying the jump-diffusion model to oil futures options
• Difference between forecast prices and future prices
• Capital Asset Pricing Model (CAPM) and price forecasts
• Estimating risk premium in finance and its applications to energy prices
• The market cost of risk

Module 3: Designing an Oil and Gas Trading Model

• Establishing oil and gas evaluation models
• OPEC trading model
• Calculating LTM operating results
• Normalizing operating results
• Excluding nonrecurring charges
• Collating financial projections
• Inputting financial data and calculating price and market ratios
• Evaluating outstanding shares using the treasury stock technique
• Structuring output schedules
• Generating multiple tables
• Choosing key valuation multiples through VLOOKUP

Module 4: Option Pricing

• Black-Scholes formula
• Valuation of American-style options
• Payoffs and putt-call parity
• The Binomial model
• Option sensitivities/The Greeks
• Delta and Gamma
• The strip of spark spread options
• Real options in the energy market
• Oil fields as the valuation of extraction option
• Commodity swaps

Module 5: Data-Driven Natural Gas Price Prediction Models Using Machine Learning Methods

• Data-based predictive models
• Machine learning equipment for energy price prediction
  – ­Gaussian process regression (GPR)
  – Support vector machines (SVM)
  – Artificial neural networks (ANN)
  – Gradient boosting machines (GBM)
• The cross-validation method
• Evaluation of natural gas and crude oil spot prices

Module 6: A Primer on the Interest Rate Markets

• Floating rate securities
• Time and time value of money
• Basics of Excel functions of bond costing and valuation
• Interest rate risk as the key bond risk
• Bond risks and interest rate volatility
• Economic limit test
• Convexity and duration: hedging interest rate exposure
• Forecasting future interest rates through
  – ­Practitioners’ approaches
  – Financial-economics approach

Module 7: Statistics of Price Processes in Energy Markets

• Historical volatility
• Historical volatility vs implied volatility
• Characterizing the volatility surface across strike and time
• The term structure of volatility (TSOV)
• Estimating a mean-reverting process

Module 8: The Present State of Commodity and Equity Markets

• Upstream petroleum fiscal valuation and modeling
• Measuring nervousness
• Uncertainty of commodity and equity markets
• The Paradox of the world crude oil prices
• Interdependence and complexities of the oil market
• Refined oil products and retail gasoline prices
• Natural gas
  – ­Pricing
  – Demand determinants
  – Trade
  – Reserves and productions
  – Physical attributes and supply
• Throughput, refining capacity, and refined oil products

Module 9: Introduction to Forwards, Futures, and Statistical Concepts

• Regression analysis
• Amortization and depreciation
• Basic statistical concepts
  – Stationarity of time variables
  – Average and volatility
• Constrained optimization problems
• Basics of futures and forwards: definitions, pricing by arbitrage, and payoff diagram
• Forward/futures prices and forecast prices

Module 10: Crude Oil Prices, Geopolitical and Economic Events

• Oil prices, inventory, and production
• Role of Shale Oil
• Diversification in the Gulf Corporation Council (GCC)
• API reports
• Cost breakeven oil prices
• Geopolitical events and impacts on oil prices
• Oil market rebalancing
• Oil Production and the Challenges of green energy
• Challenges in designing the oil safety stock
• How countries deal with changes in oil prices