Measure-valued processes for energy markets

TL;DR

This paper develops a measure-valued process framework for energy markets, enabling arbitrage-free modeling of electricity and gas futures, with neural network calibration and applications to renewable energy production.

Contribution

It introduces a novel measure-valued process approach for energy markets, extending the Heath-Jarrow-Morton framework to infinite dimensions with neural network calibration.

Findings

Framework guarantees arbitrage-free modeling in infinite dimensions

Neural network parameterization enables tractable calibration

Application demonstrated on market data and renewable energy modeling

Abstract

We introduce a framework that allows to employ (non-negative) measure-valued processes for energy market modeling, in particular for electricity and gas futures. Interpreting the process' spatial structure as time to maturity, we show how the Heath-Jarrow-Morton approach can be translated to this framework, thus guaranteeing arbitrage free modeling in infinite dimensions. We derive an analog to the HJM-drift condition and then treat in a Markovian setting existence of non-negative measure-valued diffusions that satisfy this condition. To analyze mathematically convenient classes we build on Cuchiero et al. (2021) and consider measure-valued polynomial and affine diffusions, where we can precisely specify the diffusion part in terms of continuous functions satisfying certain admissibility conditions. For calibration purposes these functions can then be parameterized by neural networks yielding measure-valued analogs of neural SPDEs. By combining Fourier approaches or the moment formula with stochastic gradient descent methods, this then allows for tractable calibration procedures which we also test by way of example on market data. We also sketch how measure-valued processes can be applied in the context of renewable energy production modeling.