In a world seeking effective climate action, the spotlight is increasingly focusing on renewable energy. However, within financial markets, the trading of instruments such as biofuels, voluntary carbon credits and power purchase agreements remains complex and opaque. Advances in technology can lead to more automation, standardisation and transparency, but questions remain. How do we ensure effective price discovery in these developing markets? How can firms effectively manage and leverage their data? What role does risk management play, and how should margins be calculated?
In this feature, we will delve into these issues and explore how the renewable energy trading sector is evolving in the age of digital innovation.
The Wild West?
One of the fastest-growing markets in the renewable energy sector is that of Voluntary Carbon Credits. These are tradable certificates that represent the removal of carbon dioxide emissions and are predominantly traded over-the-counter (OTC) through bilateral contracts, although as demand grows, they are increasingly being listed on exchanges.
“The voluntary carbon market, being relatively new, presents challenges around data integrity and pricing for clients worldwide,” says Calvin Ang, Senior Product Manager (E/CTRM) at Murex, a trading & risk management solutions vendor. “To address these, companies need to carry out thorough due diligence and one of the possibilities consists of entering into long-term agreements with project developers to guarantee the quality of the credits. Monitoring the spot price is also important, and many new market players are exploring and testing various market data providers.”
Clive Furness, CEO at Contango Markets, a consulting firm that focuses on the commodities markets, put things a little more bluntly. “Voluntary carbon markets are like the Wild West – chaotic, highly inefficient and desperately in need of better data and centralisation,” he says. “Establishing true value in these markets is challenging, although there are many venues attempting to do it. The eventual goal is to unite around a valuation proposition understood by the entire market, not just by individual agencies or companies. But that will require one or more significant players to drive it.”
Other commonly traded renewable markets include: Power Purchase Agreements (PPAs), long-term contracts under which a business agrees to purchase electricity directly from an energy producer, mostly traded OTC with contract structures that can vary significantly, encompassing both long-term and short-term aspects; Renewable Energy Certificates (RECs), which certify that the bearer owns electricity generated from a renewable energy resource, again usually traded bilaterally OTC; Biofuel Credits, tradable certificates that provide proof of production of biofuels, traded both on spot markets and OTC, depending on the country’s regulatory structure; and renewable power Futures and Options, a range of exchange-traded derivative (ETD) contracts listed on exchanges such as EEX, ICE and CME.
Handling the complex variations of each of these markets can be challenging. For example, with long-term contracts, many markets lack liquidity and reliable forward prices beyond a few years. Hence, it’s crucial that systems are flexible enough to allow firms to build models that enable them to view exposures and to plan and execute long-term hedges.
Digital Evolution
The transformation of energy trading – and particularly renewable energy trading – into the digital realm mirrors a broader market evolution curve, from phone-based or open outcry markets, through chat-based trading, to hybrid models, and ultimately fully electronic, standardised markets such as exchange-traded futures and options. However, the pace and extent of this transformation varies significantly across different sectors.
Certain markets, such as biofuels for example, are highly niche, attracting a specialised set of players that includes energy majors, trading merchants, and a small number of banks. These markets deal with a variety of contracts, each having its unique forward curve, and trading generally occurs in an OTC environment rather than on standardised exchanges. Brokers play a crucial role in connecting potential buyers and sellers in these non-standardised, thin markets.
“Banks are keen to adopt a more digital data environment,” says Furness. “However, banks largely withdrew from energy just as digitalisation began to take hold, passing control to the merchants, who are more resistant to change. If the banks were more involved, there would likely be a greater push towards automation and electronification, as they’ve embraced digitalisation in other asset classes. Energy and commodities remain among the last major asset classes still predominantly voice and chat-based, with minimal automation, especially in the oil and distillates OTC markets.”
Energy traders commonly use Excel spreadsheets as their ‘deal-making front-end’, to track prices, quotes and trades across multiple brokers. The spreadsheet, despite its simplicity, fills a void as there are very few systems that can effectively monitor and manage these OTC broker markets. However, relying on spreadsheets can lead to risk and compliance challenges, points out Furness.
“An interesting discrepancy exists between the CLOB (central limit order book) markets and OTC markets from a compliance perspective,” he says. “While the former benefit from sophisticated compliance software and continuous curves, the latter often have compliance managed via Excel spreadsheets, based on prices supplied by brokers. And while traders might be content with Excel spreadsheets, large organisations struggle to conduct real-time VAR risk management and compliance without a curve.”
However, there are good reasons why Excel remains ubiquitous, counters Stanislav Ermilov, CEO of Tallarium, a provider of specialised trade analytics for price discovery in off-exchange energy markets. “A firm might want to keep on using Excel because they’ve built all sorts of integrations around it, and it allows for a lot of flexibility and customisation,” he says, adding: “Some clients take Tallarium’s biofuels data and forward curves for example, and stream those into Excel for a range of day-to-day use cases. Or they might take our theoretical ‘fair value’ prices and then use Excel as a supplement to calculate things like profit and loss.”
Manual processes are increasingly being complemented with more structured digital tools. On one end of the spectrum, there are the high-volume, low-margin commodities markets that are heavily automated (ETDs, for example). On the other end, the less liquid OTC markets need workflow tools to manage contracts and APIs to integrate with internal systems, providing firms with some level of control and structure, rather than full-scale automation.
“Flexibility is paramount in OTC trading,” says Ang. “While setting up processes like deal capturing and creating forward curves (the challenge lies in data quality) are quite standard, workflows require integration with downstream systems, which are often bespoke. It’s important to start from a good baseline and make necessary adjustments to implement the solution as the market evolves. Adaptability is key as changes are inevitable, and APIs play a significant role in data integration.”
Price Discovery & Liquidity
Ermilov highlights some of the complex challenges around price discovery and the sourcing of liquidity in the renewable energy markets. “Renewables and biofuels are generally traded in the opaque OTC market, where there is no common marketplace or standardised data. So you’re generally dependent on your broker networks to provide you with pricing. That means it’s often difficult to figure out where the market is trading, and there’s often a really wide spread between the bid and offer prices, especially in emerging markets like biofuels.”
In response to these challenges, companies like Tallarium are using innovative solutions to enhance market clarity by converting disparate data into a real-time, consolidated view of market pricing, says Ermilov. “Tallarium sits on top of the existing chat communication channels that traders and brokers use on a day-to-day basis. We apply machine learning to convert all this unstructured information into a single source of truth for market pricing in real time, allowing traders to achieve more efficient price discovery and eventually trade more effectively across multiple products and multiple brokers in real time. This in turn facilitates trading in those markets and makes them more liquid.”
The interconnected nature of energy markets further complicates price discovery and liquidity. As market conditions fluctuate, the impact on related contracts can be substantial. Solutions such as Tallarium recognise these dynamics and offer predictive models for determining theoretical fair prices, Ermilov explains. “Different products are interrelated, so if one contract moves, a related product is often going to be moving as well. Hence, we’ve built a model which takes those into account and predicts with a high percentage accuracy where a market should be trading, or its fair value taking into account those relationships.”
As renewable energy markets continue to evolve, having such efficient price discovery tools and liquidity solutions is becoming essential for traders. Harnessing such technological advancements can make these markets more accessible, fostering their growth and thus contributing to the broader transition to renewable energy sources.
Data Management
Handling the large volumes of detailed data associated with the renewable energy market requires a strategic approach to data management. From the increasing demand for granular data from diverse sources such as shipping tracking systems, to long-term valuations spanning decades, market participants are faced with larger and more complex data sets than ever before.
Technology vendors are stepping up to help firms deal with the challenges. André Jaeger, SVP, Product Management at trading technology vendor ION, states, “We’re very focused on making the data from our CTRM (Commodity Trading & Risk Management) systems accessible and capable of integration with other data. In recent years for example, we have invested heavily in streaming technology like Kafka to push out our data in a real-time, scalable manner, so clients can combine it with data from other sources. The challenge lies in not confining our data to specific displays or decisions, which can often be a dead end. It’s vital to ensure data is open and can be combined with data from other sources, without channelling everything through a proprietary solution. Well-documented APIs are key to this.”
Accuracy and reliability of the data are paramount. Inaccurate data, no matter how efficiently delivered or well-integrated, can be misleading and potentially harmful. Ensuring the effective management of accurate, quality data from various sources, and facilitating seamless integration of this data into the existing systems are therefore critical steps, as Murex’s Ang explains. “Although we’re not a market data provider, we support clients by importing data and creating a flexible structure within our system. If clients are engaged in basis trading, for example, they require fine-tuned data about project factors and attributes (e.g., project location/country, vintages, etc.) We provide a flexible, extendible structure for sourcing data, facilitating seamless data integration, and managing that data through the entire post-trade lifecycle.”
Risk Management & Margining
Renewable energy markets are uniquely complex, fraught with a series of distinct risk factors. Chief among these is the inherent volatility of energy prices, not only due to recent ‘market shock’ events like the Russia/Ukraine war, but also due to environmental influences like weather and seasonality, which impact the output of solar and wind power, leading to price fluctuations. Compounding this, resource availability is geographically uneven, creating locational risks tied to the abundance (or lack thereof) of solar and wind resources in a given area.
This market also faces political risks. Policy changes or shifts in government subsidies and regulations can alter market conditions unexpectedly. Additionally, due to the nature of long-term PPAs, counterparty risks are always present. Hence, sophisticated risk management strategies, advanced data analytics, and robust modelling techniques are essential tools for market participants.
“Asset developers venturing into the renewable space often underestimate the complexity of the market,” says Josh Gray, Chief Scientist, FEA Risk Analytics at ION. “They face significant risk when signing long-term PPA contracts due to the volatility of the power market and their assets’ production capabilities. As they attempt to hedge these assets in the financial market with futures, they get exposed to market volatility.”
Firms are now leveraging technology and data to help assess, manage and mitigate these risks, says Gray. “Many clients provide us with vast amounts of historical data and ask us to construct models that estimate volatility for risk assessment purposes for example, particularly for power markets. We often combine that with other data sources into a single model to offer a comprehensive view of portfolio risk.
“The development of renewable assets is reshaping the power market fundamentals to some extent,” continues Gray. “The impact of new, more efficient assets producing cheaper power can depress prices more than anticipated. It’s important to accurately model these factors from a risk management perspective. By understanding their risk, asset developers can create stress scenarios to evaluate how their assets or the entire renewable sector might affect intraday power prices. These scenarios might involve unexpected weather conditions, such as a sunny spell or high winds that flood the market with cheap power, or a stretch of cold, cloudy days that reduce solar production.”
In parallel with risk management, accurate and timely margining of positions is critical. Although in the energy markets, margining comes with its own set of challenges, explains Liam Huxley, CEO & Founder at Cassini Systems, specialists in OTC collateral and margin analytics software.
“While futures or ETD margining has typically been a standard SPAN-type model across most of the industry, we’re now shifting towards VAR-type models. There are also a number of outlier exchanges that adopt entirely different approaches,” he says. “As one of the earliest ISDA SIMM (Standard Initial Margin Model) vendors, we’ve been modelling OTC contracts, even very exotic ones, across all asset types for several years. We use various data sources and have developed a comprehensive market data and risk platform within Cassini to manage these. We can calculate and model margins for clients both for calling margin and also to replicate what they expect the counterparties to call.”
Given the inherent risk factors involved in renewable energy trading, it’s important to be able to perform rapid margin calculations, suggests Huxley. “On-demand margining is beneficial because it can be invoked whenever it is needed, as opposed to overnight or end of day. Also, OTC bilateral margining can be performed pre-trade, in near real-time (i.e. seconds not sub-seconds) so that for example, when a client is considering an energy swap, a margin check can be run at that precise moment. Clients often run periodic intraday updates against their portfolios based on portfolio changes or updated intraday pricing. Some of our biggest industrial clients update their portfolio margins every two minutes and run over 1,500 daily pre-trade checks.”
Accurate pricing data is a key requirement for margin calculations, but in the renewables sector that is not always available. Huxley outlines Cassini’s approach where this is the case. “With renewable energy contracts, the key is to establish whether there’s sufficient liquid market data that we can use to price the contract and generate risk sensitivities. In some parts of the OTC world, there is no observable market data and the dealers tend to not disclose the pricing models they use. In these situations, the UMR (Uncleared Margin Rules) GRID may be utilised for margin calculation or the client may source their own proprietary pricing data.”
Conclusion
As the renewable energy sector continues to grow and evolve, technological advancements are increasingly central to navigating the complex challenges it presents. Solutions that improve data integration, enhance market transparency, and provide robust risk management are essential in ensuring effective trading operations.
However, it’s also crucial to acknowledge that the sector, much like the Wild West, is still somewhat untamed and unpredictable. The pace of digital transformation varies significantly across different areas, and there are still plenty of uncertainties surrounding price discovery, data management, and risk mitigation. To navigate this landscape, firms must remain adaptable and flexible, ready to harness the opportunities that these challenges present. With advancements in technology, continued efforts towards market standardisation, and a growing commitment to sustainability, the renewable energy trading sector is well-positioned for the future.
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