Value & Earnings at Risk
An understanding of the new metrics of risk, Earnings at Risk (EaR), Capital at Risk (CaR), Cash Flow at Risk (CFaR), and Credit Value at Risk (CVaR) is essential to the management of risk in today’s energy company. This program explores, in non-technical terms, the methods and assumptions underlying these metrics as well as issues faced in their implementation and administration. Beginning with the core concept, Value at Risk (VaR), this seminar examines methodologies and issues common to all these approaches to assessing risk: risk distributions, volatility, confidence levels, holding period, correlation, risk aggregation, capital adequacy, etc. The program then demonstrates how these concepts can be applied in numerous variants to measure not only price risks, but also credit risks and risks to earnings. Beyond simple “closed form solution” methods, the program explains, also in non-technical terms, simulation approaches to risk measurement including historical and Monte Carlo methods.
There are no prerequisites for this program, nor is any advanced preparation required.
CPE Credits: Accounting & Auditing 2; Consulting Services 1; Management 1; Specialized Knowledge & Applications 12.
Day 1

The Concept of Value at Risk

Price Risk as a Component of Enterprise Risk

• Risk and Capital Adequacy
• Portfolio Approach to Capital Allocation in an Energy Company
• Credit Risk, Risk & Capital
• Inter-departmental Risk Transfers
• Interdependence of Risk in the Energy Enterprise

The Emergence of VaR

• Inadequacy of Earlier Risk Measures
• Evolution of Modern Risk Analytics
• Translating Subjective Probability into Objective Probability
• Measuring & Controlling Risk in an Energy Company

VaR Advantages

• As an Objective Quantifier of Risk
• To Business Enterprises
• Sarbanes-Oxley & Corporate Governance
• Managing Risk Portfolios
• VaR as the Measure of Capital Requirements
• The Efficient Allocation of Risk Capital

Risk and Maximum Potential Loss

• Types of Risk Measures
• Assigning an Acceptable Level of Uncertainty
• Measuring Worst-Case Loss
• Measuring Probabilities by Counting Price Paths
• Establishing Confidence Levels
• The Role of Time in Risk Measures

Conceptual Foundation of Risk Analytics


Risk as Dispersion of Possible Outcomes

• Probability vs. Frequency Distributions
• Relationship between Standard Deviation & Volatility
• Adjusting Volatility for Term
• Applicability of Volatility to Energy Risks

Understanding Volatility

• Types of Volatility
• Measuring Historic Volatility
• Path Dependency of Volatility
• Deriving Annual and Periodic Volatility

Measuring Confidence

• Interpreting Z values to Measure ‘Tail’ Risk
• Skewed Distributions
• Kurtosis

Aggregating Risks for Multiple Positions

• Aggregating Means and Volatilities
• Aggregating Risk for Multiple Positions
• Correlation as the Key Element in Risk Aggregation
• Volumetric and Other Non-Additive Risks

Applying Risk Analytics to Energy


Key Factors in Measuring Risk

• Holding Period and Confidence Level
• Volatility and Risk Distribution
• Return on Capital
• The Closed Form Calculation

Aggregating Risk Measures

• Additive Risks
• Basis Spread Risk
• Using Delta to Measure VaR for Option Positions

Determining the Appropriate Volatility Level

• Using the Appropriate Volatility Input for Calculation Risk
• Complexities of Energy Volatility
• Volatility Smiles & Skews
• Term Structure of Volatility
• Instantaneous vs. Implied (Average) Volatility
• Seasonality

VaR Applied to Measure Credit Exposure (CVaR)

• Metrics Used in Credit Risk Management
• Credit Exposure vs. Credit Risk
• Aggregating Credit and Price Risks to Determine Capital Adequacy

Day 2

Earnings at Risk (EaR)

The Emergence of EaR

• The Limitations of VaR for Energy Companies
• Measuring Risk for Accrual Accounting
• Earnings at Risk/Profits at Risk
• The Appropriate Holding Period for EaR

Evaluating Hedge Strategy with EaR

• Measuring Residual Risk After a Hedging
• Evaluating ‘Dirty’ (Imperfect) Hedges
• Integrating EaR with VaR
• Expanding the Scope of EaR beyond Price Risk
• Using Simulation Models to Include Volumetric and Other non-Price Risks

Historical Simulations

• Model Assumptions
• Building a Historical Simulation
• Incorporating Correlation in an Historical Simulation
• Advantages/Disadvantages of the Historical Approach

Monte Carlo Simulations

• Creating Random Price Paths
• Analyzing Distribution of Price-Path Outcomes
• Monte Carlo for Aggregating Multiple Risks
• Advantages/Disadvantages of Monte Carlo Methods
• Monte Carlo vs. Historic Method
• Aggregating Volumetric and Price Risks Using Monte Carlo

Using Historical Approach with Monte Carlo Methods

• For Single Risk VaR/EaR
• For Multiple Risk VaR/EaR

Using Risk Simulations to Evaluate Hedges Beforehand

• Evaluating alternative hedge strategies
• Advantages of simulation methods
• Differences between EaR and VaR with option hedges
• Modeling binary asymmetries in EaR models
• Limitations of EaR

Stress Testing

• Identifying Model Risk
• Divergence of Future Events from Historic Pattern
• “Fat Tails”
• Energy Stress Factors

Cash Flow at Risk (CFaR)

• Top Down Risk Measures
• The CFaR Approach
• Creating Distribution of Earnings Changes
• Weaknesses of CFaR
• Need for Sample of Earnings Risk
• Sanitizing the Earnings Data
• Inability to Evaluate Hedging Tactics






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