Trading and risk management in volatile intraday power markets

Trading successfully in these markets requires structured learning and discipline, and it’s worth bearing in mind the risks associated with trading in the intraday power market and how overnight conditions affect execution.

This article will act as a capstone playbook for managing risk and opportunity in highly volatile intraday power markets driven by renewable uncertainty.

How intraday volatility regimes differ from normal conditions

The reason volatile intraday and regimens can't be approached from the same trading perspective as in normal conditions is that pricing can change at a moment's notice. In normal situations, the day-ahead market is utilised, with stability and predictability at its core. Spreads tend to be narrower, while liquidity is high. High-volatility regimes or crises, by contrast, feature sharp price drops and spikes, closely linked to the peaks and troughs of renewable energy generation. 

What can follow these crisis states is a breakdown of the relationship between connected markets.

Position sizing and timing in volatile hours

As our reliance on renewables increases, these crisis states become more pronounced, creating difficult conditions for accurate generation forecasting and making trading these commodities more challenging. Intraday volatility changes trading dynamics by forcing strategies that require nimble, reactive pricing. The higher the risk, the higher the reward, as decisions need to be made as close to the end of the trading day as possible. 

When trading in these environments, liquidity is often lower, with bid-ask spreads wider to reduce the risk of high-risk trades. This is a marked departure from day-ahead trading, which is a longer-term approach that uses weather data, news, and real-time signals to inform trades. Traders try to predict the impact of a gamble - will it pay off, or will the penalties applied make it less profitable overall?

Managing execution and liquidity risk

One risk in intraday trading is execution: price swings can occur at any time, so executing trades at the exact right moment is critical. Timing is everything, and as the market approaches the end of the day, it becomes even more critical that well-informed trades are executed. ‘One way to manage execution risk is to employ optimal execution algorithms. These speciality algorithms are models that can help traders to recognise when volatility is occurring through clustering. Trades can then be executed at the optimal time. The Hawkes process is a good example of an Optimal Execution Algorithm. Automation can also come into the fray: tools such as bots can react in real time more quickly than human counterparts, catching spikes before they affect profit margins. 

Orders can be broken into smaller orders instead of large orders, which can cause problems in markets with thin liquidity. 

Scenario-based intraday stress testing

The most informed trades are executed with the most advanced understanding of market behaviour, so it's important to incorporate stress-testing measures well before trade strategies are built. This involves examining all best- and worst-case scenarios in the intraday trading environment, such as accounting for the differing effects of high and low liquidity and for situations when a trader's position is near delivery of energy. It might also involve stress-testing how prices move up and down, especially at the more extreme end of price movements. The actual behaviour of the power producers themselves is also a key data point for stress testing, to try to identify patterns in power outages and how they might affect trading decisions. 

There are tried-and-tested methodologies that traders use to engage in some of these stress-testing techniques: one of the more common ones is Monte Carlo simulation, examining intraday probability distributions.

Once stress-testing has been determined, it's onto scenario planning for forecast shocks, which are unexpected differences between forecasted renewable generation and the actual renewable energy generated.

Forecasting can be improved to help mitigate forecast shocks by using the most up-to-date data, with real-time weather data informing forecasts. AI can even be incorporated to identify patterns before they occur or predict rarer or more unexpected risks. 

Building a repeatable intraday playbook

In conclusion, successfully trading on the intraday market is all about being prepared, or as prepared as you can be, in such an unpredictable market. One way to do this is to develop a tried-and-tested playbook for quickly responding to changing prices with informed, researched actionables. 

The areas a successful intraday playbook may touch on could include:

• Pre-Market: while intraday relies mostly on reactive decisions, it can be bolstered with knowledge gleaned from the day-ahead market, with some learnings from the day before applied to the current intraday market. 

• During the session: real-time monitoring is critical, with intraday market modelling required continuously until the market closes. 

• Post-Market: a full debrief is required for the previous day, including learnings and potentially alternative strategies for future intraday participation. 

• Entry and exit rules: intraday trading can be impulsive; set predetermined limits to get out before making informed decisions. 

• Leverage technology: use trading tools and AI to reduce the time required to make real-time decisions.