Price volatility in electricity markets – why are energy prices so high?

Electricity price volatility in Australia demands new risk models as energy transition, renewables, and market shocks reshape the national electricity market

Electricity prices in Australia have long been unpredictable, but the past year has seen particularly high prices and heightened market volatility. The National Electricity Market (NEM) has faced significant electricity price volatility, driven by coal generator outages, the impact of extreme weather conditions, transmission constraints, and the growing integration of renewable energy sources like hydro (given the need to transition away from fossil fuels). These pressures have strained the power market’s ability to meet electricity demands, resulting in sharp price fluctuations across the wholesale electricity market. 

Recently, the Australian Energy Regulator announced another increase to the default market offer – the price cap on what energy retailers can charge households and small businesses  – with prices set to climb by up to 9 per cent in some areas from July. These higher prices are driven by rising electricity generation costs and the ongoing maintenance of the nation’s poles and wires network. 

As Australian households brace for higher prices, energy pricing experts say there are still ways to cut costs – and they're calling on governments to support more sustainable energy policy solutions. 

A new study, The stochastic behavior of electricity prices under scrutiny: Evidence from spot and futures markets, co-authored by UNSW Business School’s Senior Research Associate Dr Fabio Gómez and Scientia Associate Professor Katja Ignatieva, Associate Professor Han Li, Department of Economic and Centre for Actuarial Studies at the University of Melbourne, and Associate Professor Jean-François Bégin, Department of Statistics and Actuarial Science at Simon Fraser University in Canada, provides a framework for electricity price forecasting in power systems, improving the accuracy of price forecasting models in the electricity market. 

“Our research highlights just how complex and volatile the NEM has become,” explains A/Prof. Ignatieva. “We found that electricity prices are driven by a combination of factors – sharp price spikes, strong seasonal patterns, clustering in volatility, and changes in the long-term expected price level.” 

Behind Australia’s electricity market volatility

A standout insight from the paper is that prices tend to whiplash back after a significant power market jump – a term the researchers term ‘price reversals’. This means there is generally a market correction after high prices. 

“Large upward price movements are often followed quickly by downward movements, which reflect the market’s response to short-term supply-demand shocks,” explains A/Prof. Ignatieva. “Importantly, we also show that mean reversion doesn’t happen toward a fixed level, but rather to a moving target that shifts with broader structural changes like the energy transition.” 

In practical terms, this means electricity prices in Australia no longer return to a stable, predictable “normal” after periods of volatility. Instead, the level to which prices tend to revert is itself shifting over time, influenced by major structural changes in the energy system – such as the closure of coal-fired power stations, the rapid growth of renewables, and evolving market regulations.

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“For households and businesses, this creates more uncertainty around energy costs, as prices may remain elevated or suppressed depending on broader trends. For energy planners and policymakers, it highlights the need for flexible strategies that account for a moving baseline, rather than relying on historical averages when making decisions about pricing, investment, or risk management," said A/Prof. Ignatieva.

A new model for calculating electricity prices 

To help businesses navigate this turbulence, the researchers developed a new econometric pricing model tailored to the unique dynamics of the wholesale electricity market. 

“The model we’ve developed provides a more realistic and robust framework for understanding electricity price dynamics,” says A/Prof. Ignatieva. “It accounts for key features like time-varying volatility, dynamic jump intensities, and changing long-run expectations.” 

“This makes it a valuable tool for risk management, particularly for those relying on futures markets to hedge. By capturing the drivers of price behaviour more accurately, the model reduces basis risk and improves the reliability of hedging and pricing strategies. It gives energy retailers, generators, and investors a clearer picture of how risks evolve – enabling more informed decision-making in a highly uncertain environment.” 

She says their work responds to a rapidly evolving market structure in the energy sector. “We were motivated by the ongoing transformation of electricity markets, particularly in Australia,” she says. “The rapid growth of renewable generation, the retirement of coal-fired power plants, and policy reforms have all contributed to increased electricity price volatility and uncertainty.” 

“Traditional models weren’t capturing these dynamics well enough, especially the effects of intermittency and structural change. We saw an opportunity – and a need – to develop tools that better reflect the realities of today’s electricity markets. With the energy transition accelerating, the ability to model and manage pricing risk has never been more critical for market participants and policymakers.” 

Particle filtering: a new technique for tracking price trends 

In their study, the researchers built a detailed electricity price statistical model to better understand how electricity prices behave in the Australian market. Electricity prices can change suddenly and unpredictably – due to things like weather, demand spikes, or supply issues – so the model includes features like volatility (how much prices swing), sudden jumps (spikes or drops), and seasonal patterns (like higher prices in winter). 

To estimate this model accurately, they used a method called particle filtering, which helps track and update all the hidden or changing factors influencing prices over time. By combining spot prices (current market prices) and futures prices (what people expect prices to be later), the model gives a clearer picture of how electricity prices move and how to better predict them. 

“Particle filtering is a state-of-the-art estimation method, ideal for dealing with the kind of complex, non-linear, and high-dimensional models that electricity markets require,” explains A/Prof. Ignatieva. 

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“In our study, we used it to estimate latent variables – things like volatility, jump intensity, and long-run price expectations – that can’t be directly observed.” 

“By incorporating both electricity spot prices and futures data, we were able to get a much richer and more accurate picture of market dynamics. This approach allowed us to calibrate the model in real-time and to ensure that it fits both the short-term behaviour of electricity spot prices and the expectations embedded in future contracts.” 

Busting energy market price myths 

One of the paper’s most surprising findings challenges a well-known assumption in financial markets. 

“In equity markets, it’s often assumed that investors demand a premium for jump risk – but in electricity markets, we see a different story,” says A/Prof. Ignatieva. “Because positive and negative jumps tend to offset each other, jump risk doesn’t appear to earn a positive premium. Instead, it’s volatility risk that gets priced in.” 

Unlike in stock markets, where investors are usually paid extra for sudden price jumps, in electricity markets, these jumps often cancel each other out. So, it's the overall price volatility, not the jumps, that ends up costing more. 

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“This has important implications for how we think about risk, regulation, and energy policy design. It suggests that policy should focus more on managing electricity price volatility – through mechanisms like energy storage or demand-side firming – rather than just trying to smooth out extreme price events,” says A/Prof. Ignatieva. “It also offers new insights for how derivatives should be structured and priced in energy markets.” 

In short, this new model helps improve electricity price forecasting, manage risk in the Australian energy market, and support more effective pricing of electricity derivatives like futures contracts.