The rise of retail platforms like Robinhood and the social media frenzy around so-called meme stocks have helped to drive Main Street options trading for the past year. Surging volume and volatility in these markets have introduced new kinds of risks, and vendors who offer trading solutions—such as for market risk, order management, and clearing—believe that offering more modern, advanced technologies can help mitigate them.

Individual investors are now increasingly aware of options, in the wake of events like the infamous Reddit-driven short squeeze in video game retailer GameStop’s stock; they also now have access to gamified apps that allow easy access to these derivatives contracts. Last year was a turning point for the market, with options activity poised to overtake equities for the first time.

The tricky thing about the options market is that forming and executing on trading strategies is a less automated process than in equities trading and requires a higher touch to understand price movements. This makes deploying newer, more sophisticated quant technologies—such as machine learning and natural language processing—to deliver deeper analytics or build algorithms harder to achieve. Additionally, there are less than a few thousand tradable, listed equity stocks, while options contracts, which are derivatives of those underlying tickers, change hands by the millions on any given day.

What this means is that while pricing is done throughout the day, minutes or even seconds of delay can upset an options trade or a whole portfolio. Periods of heightened volatility can further delay trading systems, potentially leading trades to be made on old information. To address these issues, vendors are working to build out more real-time risk functionality, improvements in the back office, and better market data management.

Risky business

Last month, the Options Clearing Corp. announced that 2021 saw a 32.2% increase in cleared contract volume from 2020, which itself was a 52.4% increase over 2019. In the same month, the Cboe Global Markets’ total options volume on its four options exchanges combined was 282.5 million contracts, the second-highest monthly volume on record.

With the options industry so complex and time-sensitive compared to other asset classes, tech vendors are having to consider new risks among the surging volumes.

“Idiosyncratic risk is at the highest we’ve ever seen it in the history of the markets,” says Ravi Jain, director of risk and derivatives at Sterling Trading Technology, a Chicago-based provider of professional trading technology solutions for the global equities, options, and futures markets.

Idiosyncratic risk arises from dangers that are unique to specific securities (as opposed to systematic risk, which is risk that affects a broader market). The stock of GameStop, for instance, carries idiosyncratic risk—the r/WallStreetBets investors who have shown they can and will manipulate its stock price en masse—while the stocks of comparable retailers do not carry the same risk.

In November, MSCI researchers noted that much of the underperformance of value strategies, as proxied by the MSCI Enhanced Value Index, can be attributed to idiosyncratic, or stock-specific, risks. In JP Morgan Asset Management’s 2022 Long-Term Capital Market Assumptions report, the firm said that emerging asset classes—such as private equity and cryptocurrency—remained breeding grounds for this type of risk.

Traders use complex math and modeling schemes to understand their own positions in relation to the market, such as the arduous Monte Carlo simulation or value-at-risk (VaR) calculations. But the data used in these techniques tends to be batch-oriented and processed at day’s end, as opposed to intraday or in real time, which makes navigating abnormal market conditions, which include sudden volume spikes, more difficult. (See Box: Get Smart)

Jain says that in the future, there may be opportunities to advance options markets and options trading workflows by introducing quantitative strategies using artificial intelligence and machine learning, similar to how algorithmic trading is now commonplace in equities trading.

At this point, however, applying quant techniques to options trading is tricky. Options traders have long used implied volatility—the expected volatility of a stock over the life of the option, derived by the critical Black–Scholes formula—as a basis for formulating their strategies. However, Jain says, implied volatility data is not as easy to get by each tick or price, as it is for stocks.

“It’s harder to get good, clean implied vol data on a granular basis,” Jain says. “If your data is not super clean, then your algorithms, whether they’re based on AI or ML, are going to be compromised.”

A pricey field

Adding to the challenge, options pricing is notoriously difficult. To value a contract, one must consider many variables beyond the value of the underlying asset, including frequency of trading and price movements. Options market-makers use a variety of strategies, such as volatility arbitrage, which profits off the difference between implied volatility of the asset and the volatility of its underlying asset. Because an option is a derivative of an underlying stock, those moves require traders to accurately model the underlying stock and incorporate it into their prices quickly.

For these kinds of strategies, market-makers turn to their own research, modeling various “volatility surfaces”: three-dimensional models of implied volatilities calculated using data from options that are currently being traded, says Patrick Flannery, CEO of market data provider MayStreet.

“You can imagine if you were quoting in some of these big moves”—such as Facebook parent company Meta’s options frenzy amid a 25% plunge in price on February 3, which culminated in the highest record daily volume (2.5 million contracts traded) since August 2020 (1.8 million contracts traded)—“that if the stock moves out that much and you have an options portfolio, it could be detrimental because you have a lot of exposure out there, far more than you can hedge,” Flannery says.

Market data is crucial to these efforts, and market-makers run scores of data feeds, which are expensive and bandwidth-intensive, and require some sophistication on the part of the consumer. After ingesting a swath of datafeeds, consumers must then ensure they have the appropriate reference data, can synchronize markets, and deal with the practical implications of moving assets very quickly and the relationships between them, Flannery says.

And while the front office faces a confounding market, the back office faces a growing overload of trade information that must be reconciled and processed before the market opens the next day.

RQD, a new start-up in the US clearing space, was created by spinning out the execution and custody business of Volant Trading, a market-making firm founded in 2006. Originally set up as an options market-maker, the company expanded into low-latency equities execution in 2012. But in 2018, it obtained a limited clearing license and relaunched in late November of last year on the premise that advancement is required (RQD is an abbreviation of “required”) in back-office technology—platforms and tools for data management, processing, and reconciliations.

Clearing firms are not interested in low latency in the same way the front office is—i.e., how fast a trade can be done. For them, the technology burden is about scale and capacity, the ability to ingest this data reliably and quickly, and reflect it back to their counterparts efficiently, says RQD CTO Jon Fowler.

“The interesting part here is that’s really where most of the risk, or at least a lot of the risk, lives,” he says. “Especially when we’re talking about increased volumes, and especially with options then being leveraged and derivatives themselves, it doesn’t take much slippage to find yourself in a big hole.”

When it launched, RQD wanted to be able to perform all risk management, reconciliations, and monitoring in real time. With that goal in mind, Fowler and his team built the company’s system using its own codebase and with the ability to ingest data from its clearing correspondents in real time. Many back-office providers, in contrast, often bolt their services onto legacy mainframes or clunky databases, Fowler says.

Sterling’s Jain agrees. Sterling, which offers order management and infrastructure services as well as a risk and margin system, is trying to build more sophisticated risk technologies. In August of last year, the company released an application into its Rest API cloud-based Sterling Risk and Margin product line, which introduced analytics as Risk-as-a-Service using quant and big data techniques. As a result, it has seen increased interest from some of the new clearing firms and broker-dealers.

“We’re seeing a lot of the newer, more fintech-type clearing firms and brokerages thinking about this problem and looking at it as an important part of the business up front,” Jain says. “I think we’re going to see some of the more legacy, traditional clearing firms and brokerages having to rethink, re-do, and re-invest in better risk technology.”