Bringing trading-desk data into derivatives pricing models

As market volatility and regulatory scrutiny prompt firms to rethink how they value complex derivatives, J.P. Morgan’s PricingDirect team introduces a new autocallable model, informed by trading-desk data, which reflects the shift towards data-driven transparency

In an era of constant market disruption – from Covid-19 pandemic-driven shocks and the 2023 banking turmoil to continuing geopolitical crises – derivatives valuation methods have struggled to keep pace with real market dynamics. Traditional valuation models, rooted in historical assumptions and a limited range of observable market data, are increasingly misaligned with current trading realities. For asset owners, asset managers and fund administrators, this gap translates into heightened valuation uncertainty, model risk and mounting regulatory pressure to justify pricing methodologies.

The issue is especially pronounced for structured products such as autocallables, which blend equity-linked performance with fixed income features. “An autocallable offers investors enhanced yield and some degree of downside protection through its barrier structures,” explains Neil Hyman, chief executive officer at PricingDirect, a J.P. Morgan subsidiary. “But that same complexity creates opacity. As these products have proliferated, clients have struggled with valuation transparency, liquidity and access to high-quality market data.”

The demand for transparency

The appeal of autocallables lies in their tailored risk/return profile, but that customisation also creates inconsistency in pricing. “You’re talking about instruments that can be linked to single stocks, indexes or baskets, each with bespoke barriers, coupons and early redemption triggers,” says Hyman. “It’s not just a lack of price discovery – it’s the varied range of reactions to different market conditions that are uniquely linked to the features of the specific instrument which can also be magnified in times of stress.”

Regulators have taken note. Rule changes such as the US Securities and Exchange Commission’s Rule 2a-5 have placed greater emphasis on fair value transparency and independent verification, compelling asset managers to demonstrate the robustness of their valuation processes.

“The bar has risen; 2a-5 now holds fund boards accountable,” Hyman adds. “Clients not only have to explain their models to internal valuation committees, but also defend them to auditors and regulators. A ‘black box’ price no longer satisfies anyone.”

For PricingDirect, which provides independent valuations across 50 asset classes for more than 3,000 clients worldwide, the response has been to embed real-world data into the valuation process – leveraging the proximity and infrastructure of J.P. Morgan’s trading ecosystem while maintaining strict independence in execution.

Complexity meets innovation

“Structured products such as autocallables are inherently difficult to value because of their non-linear and path-dependent payoffs,” explains Olga Manankova, co-head of derivatives pricing at PricingDirect. “Many of these instruments have no active secondary market, and the issuer’s quote might be the only observable data point. That creates real challenges for clients trying to validate or replicate those prices.”

Operational hurdles compound the problem. Valuing such instruments demands sophisticated models and high-quality market data, such as volatility surfaces and correlations – all of which can fluctuate rapidly in stressed conditions. “Clients often lack the infrastructure or resources to source and calibrate that data at scale,” Manankova says.

PricingDirect’s latest innovation tackles this challenge directly: its new autocallable pricing model incorporates desk-calibrated market data from J.P. Morgan’s trading operations, aligning model assumptions with real‑world market inputs. “We’re leveraging the same advanced models used by J.P. Morgan’s trading desks for their own risk management, along with market data implied from trading activity,” says Manankova. “The result is a far more realistic and defensible valuation.”

Crucially, the firm maintains a clear operational firewall between the trading and valuation functions. “While we benefit from access to those models and data sources, PricingDirect operates independently in how we calibrate and deliver prices,” she adds. “We also engage closely with clients during onboarding to incorporate their feedback and ensure the outputs align with their expectations – without compromising independence.”

The payoff has been tangible. As Hyman notes: “Feedback from institutional clients shows that our valuations are consistently closer to their internal marks than other providers’. That’s a direct reflection of how calibrating with real trading data enhances accuracy.”

From theory to reality

At the heart of this evolution is the recognition that theoretical models, however mathematically elegant, can diverge sharply from trading reality. “Traditional models are often calibrated on historical data,” Hyman says. “They perform well in stable markets but, when volatility spikes or in periods of extreme stress, those historical relationships tend to break down. The models lag behind the market.”

By contrast, integrating desk‑calibrated inputs offers a dynamic feedback loop between live trading conditions and model valuations. “That real-time data flow keeps our pricing more responsive,” he adds. “It’s a fundamental shift towards models grounded in the market as it is, not as it was.”

Expanding the data ecosystem

For Dan Dalton, managing director and head of data and analytics sales at J.P. Morgan, this integration represents more than just a technical improvement – it reflects a broader strategy around data connectivity.

“Everything starts from a price,” he says. “Once you have a reliable, defensible price, you can build analytics, indexes and risk management tools on top of it. PricingDirect actually powers all of J.P. Morgan’s fixed income indexes, which makes our valuation integrity central to the bank’s broader data ecosystem.”

He adds that PricingDirect’s international expansion – particularly across Asia and Europe – has shaped its product development strategy. “When we built out our footprint beyond North America, we didn’t just export existing products,” Dalton explains. “We spent time with institutional investors on the ground, understanding local pricing pain points and market idiosyncrasies. The autocallable model is a great example of that region-driven innovation, as structured products are seeing rapid growth in Asia.”

A human-in-the-loop approach

As the financial industry explores artificial intelligence-driven valuation methods, PricingDirect takes a measured approach. “Automation can streamline surveillance and data processing but, for evaluated pricing, human expertise remains essential,” says Dalton. “Our evaluators are specialists – many with trading-desk backgrounds – and their judgement bridges the gap between quantitative output and market nuance.”

Hyman agrees, noting that, while AI and machine learning play an important role in asset classes with abundant data, such as municipal bonds, they are less suited to such opaque or bespoke markets as derivatives. “We’ve been using AI in our municipal bond practice since 2019 as part of a hybrid human-plus-machine model,” he says. “But, for complex structured products, there is no substitute for experienced human oversight.”

That balance – using technology to augment rather than replace human expertise – remains central to PricingDirect’s valuation process. “Clients value the ability to pick up the phone and talk directly to an evaluator who understands the market context behind a price,” Hyman adds. “It’s not just about data accuracy; it’s about interpretability and trust.”

A data-driven future

The shift towards using trading-desk data in pricing marks a broader evolution in independent valuation practices.

“It is no longer sufficient to deliver a single end-of-day price,” Hyman says. “Clients want the analytics that underpin that price – historical data, scenario analysis, risk metrics. They want to peel back the layers of the model.”

Dalton sees this as the next stage in data analytics: “We’re building services that extend far beyond price delivery – historical time series, sensitivity analysis and risk metrics that clients can use in their own models. That transparency helps clients satisfy internal governance and regulatory demands.”

Closing the model-market gap

The integration of trading-desk data into autocallable pricing reflects a wider shift across the industry – away from opaque, model-driven valuations and towards methods grounded in desk-calibrated market data. As derivatives become more complex and regulatory expectations rise, aligning quantitative models with real trading conditions is becoming essential to ensuring accuracy and transparency.

“For more than 20 years, PricingDirect has been at the forefront of evaluated pricing,” Hyman says. “What is different today is the scale and immediacy of the data we can access and our ability to integrate that seamlessly into independent valuation. That’s what gives our clients confidence.”

Dalton broadens the lens: “When clients know that the same quantitative rigour used by J.P. Morgan’s trading desks underpins the independent valuations they rely on, it changes the conversation. It’s not just about price validation – it’s about trust in the data foundation of the market,” he says.

As structured products evolve and investor scrutiny deepens, that foundation will only become more critical. Transparent, data-driven valuation will remain central to how the market measures risk and value in the years ahead.

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