How Conditioned Diffusion Models Enhance Fidelity in Synthetic Market Data Generation

Synthetic market data is becoming an essential tool for quants, traders, and risk managers seeking to test models under diverse conditions. But what if you could condition synthetic data on specific macroeconomic regimes or volatility environments – without sacrificing its statistical fidelity?

Recent advances in conditioned diffusion models suggest that this is no longer a theoretical ambition but an engineering possibility.

Conditioned Diffusion Distillation (CoDi), introduced by Bińkowski et al. (2023), integrates conditioning inputs without compromising the broad knowledge captured during large-scale pre-training [1]. This was a breakthrough in image generation, but its conceptual approach inspires potential applications in financial time series.

Why Real Data Alone Isn’t Enough

Markets are complex, path-dependent, and non-stationary. Yet most quant models are trained and backtested on historical data which, despite spanning decades, suffers from inherent limitations.

Firstly, true tail events are rare. Even if your dataset covers multiple crises, it contains only a handful of extreme events, insufficient for robust tail risk modelling. Secondly, macro and volatility regimes are unevenly distributed. A strategy trained only on bull markets may fail spectacularly under stagflation or persistent negative rates. Thirdly, historical data reflects the structure of the past, not the range of potential futures. As structural breaks occur, models dependent on historical data alone are vulnerable to regime shifts.

Traditional augmentation techniques such as bootstrapping or time-series GANs offer improvements in data quantity but not necessarily quality. They tend to reproduce historical patterns rather than synthesising genuinely new, plausible yet unobserved scenarios, and they lack systematic conditioning capability.

What Makes CoDi Different?

CoDi introduces a two-stage distillation process. In the first stage, a diffusion model learns general features from unconditioned data. In the second, it distils this knowledge into a conditioned model that generates outputs aligned with specific input prompts without retraining from scratch.

CoDi uses a two-stage distillation: pre-training on unconditioned data followed by conditioning without retraining.

While originally developed for text-conditioned image generation, the underlying principle – preserving general knowledge while integrating new conditioning efficiently – is highly relevant for financial data generation.

Imagine generating synthetic returns conditioned on a high-volatility macro regime, or simulating future price paths under a specific yield curve scenario, without retraining your entire generative model from the ground up. This is precisely what CoDi’s architecture enables in its domain.

Who Else is Exploring Diffusion Models for Finance?

Although CoDi emerged from image generation research, diffusion models for financial data have been an active area of investigation.

In mid-2023, JP Morgan AI Research released a diffusion-based model for financial time series simulation [2]. Their approach focused on generating realistic temporal sequences, ensuring that synthetic data preserved market dynamics critical for backtesting and risk modelling. Unlike CoDi, which emphasises distillation efficiency, JP Morgan’s work tailored diffusion architectures specifically for financial realism.

At Ahead Innovation Labs, we have been researching adaptation of diffusion models to financial time series since before 2023. Our focus has been on integrating macro-financial conditioning while retaining essential statistical features such as skewness, kurtosis, and tail behaviour – properties critical for risk management and strategy robustness.

In 2024, researchers at Tokyo University and Nomura proposed a model applying diffusion processes to generate financial time series conditioned on market factors [3]. Their work is conceptually similar to CoDi in terms of conditioning but designed specifically for financial datasets rather than images.

Finally, the Technical University of Munich (TUM) extended diffusion models to model market microstructure, capturing granular order book dynamics and high-frequency behaviours [4]. This highlights the flexibility of diffusion architectures across scales and use cases in finance.

How Do These Approaches Compare?

What emerges from these different strands of research is a clear divergence of priorities.

• CoDi’s primary innovation lies in its two-stage distillation, which allows models to integrate new conditioning inputs efficiently without losing the generalised knowledge gained during large-scale pre-training. This is ideal when conditioning is an add-on to an existing broad model.

• JP Morgan, Tokyo University/Nomura, and TUM focus instead on developing diffusion architectures explicitly designed for financial data, ensuring temporal coherence, financial realism, and domain-specific conditioning from the outset.

  
  

Both approaches are needed. For quant teams seeking efficient adaptation to new regimes, CoDi-like distillation frameworks could reduce computational overheads dramatically. For teams building foundation models for finance, tailored architectures remain essential to ensure that outputs reflect underlying market structures rather than abstract statistical artefacts.

What’s the Path Forward?

Adapting CoDi directly to financial time series is non-trivial. Challenges include:

• Reshaping diffusion architectures from spatial to temporal domains

• Managing computational complexity for high-dimensional multi-asset universes

• Designing conditioning inputs (e.g. macro states, volatility regimes) that meaningfully influence output distributions without introducing unintended distortions

  
  

Nevertheless, the promise is compelling: regime-conditioned synthetic data generation, robust backtesting under unobserved scenarios, and stress testing of models for tail events – all with improved efficiency.

At Ahead Innovation Labs, we are exploring precisely these questions: how to integrate the architectural efficiency of CoDi with the financial realism of time-series diffusion models. Our goal is to build generative systems that don’t just extend the past but create plausible futures aligned with your risk and strategy requirements.

👉 If you’re curious about how conditioned diffusion models could enhance your trading and risk workflows, or want to integrate these approaches into your data science stack, contact us today. We’d love to explore possibilities together.

References

[1] Bińkowski et al. (2023). CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation. arXiv:2310.01407

[2] JP Morgan AI Research (2023). Diffusion Models for Financial Time Series. arXiv:2307.01717v2

[3] Tokyo University & Nomura (2024). Financial Time Series Generation Using Diffusion Models. arXiv:2503.04164v1

[4] Technical University of Munich (2024). Market Microstructure Modelling with Diffusion Models. arXiv:2502.07071v2