From Forecast to Forward Curve: Building a Profitable Long-Term Trading View

Fundamental analysis is intrinsically linked to long-term trading: using forecasts based on analyses of multiple data points helps build a holistic picture of pricing behaviour, allowing market participants to predict how the market may react and adjust their trading strategies accordingly.

Forecasting tends to be localised: the trading landscape changes from one country to another, with varying European regions requiring different indicators to signal future value.

From strategic traders and analysts to portfolio risk managers, building a profitable long-term trading view based on price curves is a key skill for participation in the energy market. It can enable professionals to move from tactical trading to strategic, fundamentals-based positioning.

We’ll take a look at best practice for building and interpreting forward price curves, and how this connects to long-term fundamentals, hedging and investment decisions.

How forward curves reflect market fundamentals

Used to predict demand, supply, and risk associated with energy trading, forward curve forecasting can be a vital tool in a trader’s repertoire.

Components: base vs. peak, seasonality and risk premium

Base load and peak load energy are treated differently from a pricing perspective, with base load representing the lowest demand on the grid. Peak load, on the other hand, represents the maximum level of demand on the grid, which might occur during after-work periods for residential use, during high levels of industrial activity, or during seasonal differences that might require increased energy for air conditioning or heating. Peak load curves tend to be higher and more volatile than base load curves. Risk premiums are applied to cover eventualities such as supply disruptions, geopolitical events, or storage issues.

Inputs: fuel prices, carbon and policy

Base load tends to be influenced by steady energy inputs, such as coal or nuclear, which can be turned on or off much more easily than renewable energy sources. This means that how carbon allowances evolve and change affects the base load: when carbon prices rise, so does the curve. As a knock-on effect, government policies can also affect the curve. As subsidies are applied to renewable energy and coal is phased out, the curve could rise as the supply of coal (which currently influences the base rate) becomes scarcer, raising the price of the commodity.

Constructing and interpreting forward curves

Market data is key to the construction of forward curves, with financial instruments such as spot pricing, swaps, and futures informing it.

Market data sources and interpolation methods

Energy market data sources include pricing extrapolated from commodity futures contracts, with specific exchanges such as the New York Mercantile Exchange (NYMEX). Other sources can consist of financial instruments such as swaps and futures, which use prices to inform the curves.

Curve smoothing and liquidity challenges

Liquidity challenges can sometimes arise from errors in pricing forecasting. Certain types of modelling can help smooth out the curve in illiquid markets due to their simplicity and number of parameters, for example, the Nelson-Siegel Model.

Long-term hedging and portfolio positioning

A key tool in portfolio management and long-term strategy, the forward curve helps manage risk and make data-led portfolio decisions.

Aligning hedges with generation or customer demand

Weather can affect renewable energy generation, leading to pricing volatility. This is because renewable energy can only be generated when the right weather conditions prevail. Managing this intermittent demand vs. the actual energy supply required is known as aligning hedging with customer demand. This can be done via physical hedges: making up the shortfall from renewables with a different energy source, for example, fossil fuels, which can help manage price volatility and influence the forward curve.

Varying curve shapes: backwardation and contango

A forward curve can take different shapes depending on the market type. For example, the contango market is characterised by futures trading at a higher price than the spot price at that time. Backwardation refers to an inverted market, where the curve slopes downward, with future prices below the current spot rate.

Regional market differences: UK, DACH, Nordics

Liquidity varies across markets by region, we take a look at some of the key exchanges that influence this.

Liquidity and shape comparison

The key energy exchange for the German region is EEX, which primarily trades natural gas and power and has a strong forward curve. The UK equivalent is ICE, which has lower liquidity compared to natural gas. Nasdaq Commodities was previously one of the dominant exchanges for the Nordics, but EEX has secured market share, with system liquidity good for the region. 

Policy effects: CfDs, renewable penetration and nuclear

When the government introduces policies that favour low-carbon technologies over other energy sources, such as contracts for difference, which provide revenue certainty for renewable sources, market liquidity can drop. This is because risk is lower with a long-term revenue strategy facilitated by the contracts for difference. However, the intermittency of renewable sources can also increase the risk, contributing to price volatility. Nuclear power, on the other hand, can flatten the curve due to its constant, reliable energy production.

Combining fundamentals and technical signals

Sentiment, spreads, and volatility metrics can help traders understand changes in forward curves and why they occur. Fundamental signals examine changes in bank interest rates and expected spending, with the onus on long-term strategies. Technical signals focus more on short-term strategies, examining price trends to frame how the market is changing and adapting. Scenario modelling of signals can be a suitable method for stress-testing portfolios. It’s worth bearing in mind that forward markets tend to reward insight, and those who link fundamentals and flexibility lead the curve, not follow it.