Paper: arXiv 2601.05085

Authors: Emma Hubert, Dimitrios Lolas, Ronnie Sircar

Abstract

We study the problem of forecasting and optimally trading day-ahead versus real-time (DART) price spreads in U.S. wholesale electricity markets. Building on the framework of Galarneau-Vincent et al., we extend spike prediction from a single zone to a multi-zone setting and treat both positive and negative DART spikes within a unified statistical model. To translate directional signals into economically meaningful positions, we develop a structural and market-consistent price impact model based on day-ahead bid stacks. This yields closed-form expressions for the optimal vector of zonal INC/DEC quantities, capturing asymmetric buy/sell impacts and cross-zone congestion effects. When applied to NYISO, the resulting impact-aware strategy significantly improves the risk-return profile relative to unit-size trading and highlights substantial heterogeneity across markets and seasons.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced statistical modeling (multi-zone spike forecasting, structural price impact models) and derives closed-form optimal position solutions, indicating high math complexity. It is heavily data-driven with out-of-sample validation across multiple ISO markets (NYISO, ISO-NE, ERCOT) over several years, demonstrating high empirical rigor and backtest readiness.

Research Flowchart

  flowchart TD
  A["Research Goal: Forecast & trade DART price spreads<br>Optimize zonal positions in multi-zone markets"] --> B["Data Inputs: NYISO day-ahead & real-time prices<br>Market bid stacks, zonal correlations"]
  B --> C["Methodology: Unified statistical model<br>Positive/Negative spike prediction across zones"]
  C --> D["Computational Process: Structural price impact model<br>Closed-form optimal INC/DEC quantities"]
  D --> E["Execution: Impact-aware trading strategy<br>Day-ahead commitment vs Real-time adjustment"]
  E --> F["Key Findings: Significantly improved risk-return profile<br>vs unit-size trading; Market/seasonal heterogeneity"]

Cited References (47)

  1. Spillovers Into the German Electricity Market From the Gas, Coal, and CO2 Emissions Markets (2025) — link
  2. Financial Arbitrage and Efficient Dispatch in Wholesale Electricity Markets (2015)
  3. Electricity pricing using a periodic GARCH model with conditional skewness and kurtosis components (2021) — link
  4. Foreseeing the Worst: Forecasting Electricity DART Spikes (2023) — link
  5. An optimal trading problem in intraday electricity markets (2016)
  6. Virtual Trading (2025) — link
  7. Virtual Bid Financial Assurance Methodology (2008) — link
  8. Locational Based Marginal Pricing (2025) — link
and 39 more
  1. FAQ s: Locational Marginal Pricing (2025) — link
  2. NYISO Energy Marketplace: E-Learning Module (2021) — link
  3. Day-Ahead Energy Markets (NEWEM) — link
  4. DAM Overview and Timeline — link
  5. Congestion Price Component (2025) — link
  6. Locational Minimum Installed Capacity Requirements Study for the 2024–2025 Capability Year (2024) — link
  7. Qualified Scheduling Entities (2025)
  8. Registration and Qualification (2025)
  9. Optimal execution of portfolio transactions (2001)
  10. No-dynamic-arbitrage and market impact (2010)
  11. Optimal trading strategy and supply/demand dynamics (2013)
  12. The price impact of order book events (2014)
  13. Price impact (2010)
  14. Electricity forward prices: A high-frequency empirical analysis (2004)
  15. It never rains but it pours: modeling the persistence of spikes in electricity prices (2009)
  16. Modeling and forecasting electricity spot prices: A functional data perspective (2013)
  17. Forecasting spikes in electricity prices (2012)
  18. An empirical comparison of alternative schemes for combining electricity spot price forecasts (2014) — link
  19. Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark (2021) — link
  20. Forecasting day-ahead price spikes for the Ontario electricity market (2016) — link
  21. Measuring Market Inefficiencies in California ’s Restructured Wholesale Electricity Market (2002) — link
  22. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2009)
  23. Divergence measures based on the S hannon entropy (1991)
  24. ERCOT Market Data (2025)
  25. ISO N ew E ngland Markets and Operations Data (2025)
  26. NYISO Market Data (2025)
  27. GridStatus.io
  28. Deep Learning-Based Electricity Price Forecast for Virtual Bidding in Wholesale Electricity Market — → 2412.00062
  29. Electricity intraday price modelling with marked H awkes processes (2022)
  30. Modelling interregional links in electricity price spikes (2015)
  31. Self-exciting jump processes with applications to energy markets (2018)
  32. The modeling and forecasting of extreme events in electricity spot markets (2014)
  33. Estimating fast mean-reverting jumps in electricity market models (2020)
  34. Hedging local volume risk using forward markets: Nordic case (2017)
  35. Learning in S tackelberg Games with Application to Strategic Bidding in the Electricity Market (2024)
  36. Stochastic behaviour of the electricity bid stack: From fundamental drivers to power prices (2009)
  37. A model for hedging load and price risk in the T exas electricity market (2013) — link
  38. Distributional forecasting of electricity DART spreads with a covariate-dependent mixture model (2025) — link
  39. Virtual Bidding and Electricity Market Design (2016) — link

1 of 47 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.