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Cross-product Modelling in Continuous Intraday Electricity Markets via Hawkes Processes

Ayoub Jhabli, Tarek Alskaif, Kwabena Ebo Bennin, Bedir Teki̇nerdoğan, Joost M. E. Pennings · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.23619 · cited by 0 works (OpenAlex) · open-access PDF

Korean

강력한 쿼리 에너지 통합이 가속화됨에 따라 매일 지속적인 (IDC) 전기 시장은 단기적 균형을 유지하는 데 필수적이되었습니다. 그러나, 동시에 쿼리 시간과 Hourly IDC 계약 사이의 교차 유동성 구조를 모델링하기 위해 짧은 기간 동안 균형을 유지하는 계약은 복잡하고 숨겨진 유동성 의존성을 생성합니다. 시장 안정성에 중요하지만 여전히 잘 이해되지 않습니다.이 논문은 'Normal'과 'Stressed'이라는 두 개의 분리 된 체계로 거래 세션을 분류합니다.이 모델은 쿼리 시간과 Hourly IDC 계약 사이의 복잡한 유동성 역학을 모델링합니다. 교차 제품 효과가 다른 시장 조건에서 어떻게 변하는지를 결정하기 위해, 우리는

Machine translation (Meta M2M100); the English original follows.

Abstract

As renewable energy integration accelerates, intraday continuous (IDC) electricity markets have become essential for short-term balancing. However, the simultaneous trading of overlapping contracts with varying granularities creates complex, hidden liquidity dependencies that are critical to market stability but remain poorly understood. This paper proposes a multivariate State-Dependent Hawkes Process (SDHP) to model cross-product liquidity dynamics between Quarter-Hourly and Hourly IDC contracts. To determine how cross-product effects vary under different market conditions, we classify the trading sessions into two distinct regimes: 'Normal' and 'Stressed', based on a composite metric of the bid-ask spread, mid-price volatility and queue imbalance. The model is calibrated on the first month of each quarter of 2024 using level 3 limit order book (LOB) data from the Dutch EPEX SPOT IDC market. We find that order flow is overwhelmingly self-driven, with roughly 95\% of orders arriving in reaction to earlier orders in both regimes. Cross-excitation runs mainly from the Quarter-Hourly to the Hourly contract, but this leadership weakens under stress: the ratio of cumulative cross-excitation falls from 2.8 to 1.5, so the two contracts influence each other almost equally. Stress also makes the Quarter-Hourly to Hourly channel much faster, cutting its mean lag from 6.05 to 0.93 seconds. The strong seasonal variation found in calm periods largely disappears under stress, where all four months converge to the same structure.

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Topics: Econometrics, Volatility (finance), Market liquidity, Lag, Mean reversion, Economics

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