Exploring Technical Modelling in Idiosyncratic Energy Markets
Joaquin Narro and Monica Caamano
Introduction
The Energy Supply and Demand Conundrum
A Brief History of Energy Markets
Systematic Trading in Energy Markets
The Trading Edge
Defining Trading Expectations
Exploring Technical Modelling in Idiosyncratic Energy Markets
Exploring Fundamental Modelling
Introducing Cycle Analysis and Monte Carlo Simulations
Data: Boundaries and Assumptions
Estimating Model Lifetime
Optimising Performance and Risk
Pitfalls in Systematic Model Development
Model Production
Portfolio Construction
Incorporating Human Behaviour
References
As discussed in previous chapters, due to their inherent idiosyncratic complexity the energy markets are prone to inefficiencies that can be monetised by market participants that have developed a sustainable edge. In this chapter, we explore the systematisation of technical models specifically applied to energy markets, with a practical focus on a specific German electricity example. For this purpose, we have chosen the most liquid contract (front year, see historical chart in Figure 6.1) in German electricity, which is one of the most traded and developed electricity markets in the world, to demonstrate several technical modelling characteristics specifically applied to an idiosyncratic energy market.
![Figure 6.1 Figure 6.1](/sites/default/files/styles/free_crop/public/2020-03/Systematic%20Trading%20in%20Energy%20Markets%20Chapter%206%20Figure%206.1.jpg.webp?itok=70MJsAA4)
![](images/Systematic_Trading_in_Energy_Markets_fig6-1.jpg)
Within this context, we will develop and compare several technical trading models, focusing on the daily closing prices for the front calendar contracts. Our focus here is to describe the potential of autocorrelation models in the energy markets, together with the application of several trend-following techniques. As backtesting is an inherent part of evaluating technical strategies, we will be commenting on the dangers of hindsight as well as
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