Structural Electricity Models and Asymptotically Normal Estimators to Quantify Parameter Risk

ABSTRACT We estimate a structural electricity (multi-commodity) model based on historical spot and futures data (fuels and power prices, respectively) and quantify the inherent parameter risk using an average value at risk approach (‘expected shortfall’). The mathematical proofs use the theory of asymptotic statistics to derive a parameter risk measure. We use far in-the-money options to derive a confidence level and use it as a prudent present value adjustment when pricing a virtual power plant. Finally, we conduct a present value benchmarking to compare the approach of temperature-driven demand (based on load data) to an ‘implied demand approach’ (demand implied from observable power futures prices). We observe that the implied demand approach can easily capture observed electricity price volatility whereas the estimation against observable load data will lead to a gap, because – amongst others – the interplay of demand and supply is not captured in the data (i.e., unexpected mismatches).

• Publication year

  2019

• Venue

  Applied Mathematical Finance

• Publication date

  2019-09-03

• Fields of study

  Mathematics, Engineering, Environmental Science, Economics

• Identifiers
  DOI 10.1080/1350486X.2020.1725582
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• A space-time random field model for electricity forward prices

  2017cited by this paper
• Parametric Model Risk and Power Plant Valuation

  2016cited by this paper
• Model Uncertainty in Commodity Markets

  2016cited by this paper
• Proof that Properly Anticipated Prices Fluctuate Randomly

  2015cited by this paper
• A Survey of Commodity Markets and Structural Models for Electricity Prices

  2014cited by this paper
• Residual Demand Modeling and Application to Electricity Pricing

  2014cited by this paper
• Hourly resolution forward curves for power: statistical modeling meets market fundamentals

  2014influential reference
• Capturing parameter risk with convex risk measures

  2013influential reference
• Understanding and Managing Model Risk

  2013cited by this paper
• Electricity price modeling and asset valuation: a multi-fuel structural approach

  2012influential reference
• Understanding and Managing Model Risk: A Practical Guide for Quants, Traders and Validators

  2011cited by this paper
• Coherent Stress Testing: A Bayesian Approach to the Analysis of Financial Stress

  2010cited by this paper
• The economic nature of the firm: From Risk, Uncertainty, and Profit

  2009cited by this paper
• Stochastic Behaviour of the Electricity Bid Stack: From Fundamental Drivers to Power Prices

  2009cited by this paper
• The EM Algorithm and Extensions: Second Edition

  2008cited by this paper
• New Introduction to Multiple Time Series Analysis

  2007influential reference
• MODEL UNCERTAINTY AND ITS IMPACT ON THE PRICING OF DERIVATIVE INSTRUMENTS

  2006cited by this paper
• Solutions Manual and Supplementary Materials for Econometric Analysis of Cross Section and Panel Data

  2003cited by this paper
• Maximum likelihood estimation using the empirical fisher information matrix

  2002cited by this paper
• Energy and Power Risk Management: New Developments in Modeling, Pricing, and Hedging

  2002cited by this paper
• Energy Derivatives: Pricing and Risk Management

  2000cited by this paper
• The EM algorithm and extensions

  1996cited by this paper
• Mixture Density Networks

  1994cited by this paper
• Asymptotic distribution theory for the kalman filter state estimator

  1984cited by this paper

• A survey of electricity spot and futures price models for risk management applications

  2021cites this paper