Generative Modeling for Quantitative Finance
From historical returns to forward-looking scenarios, Skanalytix builds generative models that capture the structure of financial time series — supporting synthetic data generation, forecasting, and risk analysis.
Financial Modeling, Reinvented Through Synthetic Data
At Skanalytix, we specialize in quantitative financial modeling — from synthetic time series generation to forecasting and risk analysis. Built for finance professionals, our tools replicate the complexity of real markets while overcoming data scarcity, sensitivity, and structural limitations. Whether you’re modeling asset prices, simulating interest rates, or stress testing portfolios, Skanalytix offers a purpose-built platform grounded in financial insight and statistical integrity.
Powered by the UNCRi Framework
At the core of our technology is the Unified Numerical-Categorical Representation and Inference (UNCRi) framework — a proprietary system developed in-house at Skanalytix. Originally designed for complex, mixed-type tabular data, UNCRi uses a graph-based modeling approach that seamlessly integrates numerical and categorical features. This makes it especially well-suited to financial time series enriched with discrete factors like market regimes, macroeconomic classifications, or sector identifiers. By capturing both temporal dependencies and cross-asset relationships without relying on rigid assumptions, UNCRi enables a powerful range of applications — from yield curve simulation to macro-consistent scenario generation.
Built for Quantitative Finance
Our technology is purpose-built for the challenges of financial modeling. Financial time series data is noisy, fat-tailed, and structurally complex. Cross-asset dependencies, macroeconomic regimes, and volatility clustering make traditional modeling approaches fragile or misleading.
Skanalytix bridges this gap with a statistically grounded framework tailored to:
- Equity and portfolio simulations
- Yield curve and interest rate modeling
- Risk forecasting (VaR, CVaR, stress scenarios)
- Macro-consistent synthetic datasets
Whether you’re a quant building trading models or a risk team navigating regulatory constraints, we provide robust tools that mirror the statistical structure of financial markets.
Challenges in Financial Time Series Modeling
Financial time series exhibit complex behaviors that make modeling difficult using standard statistical or machine learning techniques. Volatility clustering, fat-tailed return distributions, mean reversion, and dynamic cross-asset dependencies violate key assumptions — such as normality or independence — that underpin classical models. Capturing these nuanced dynamics requires moving beyond simple point forecasts or marginal distributions to model the joint conditional distribution of asset returns — understanding how future returns depend on past behavior across all assets. Such holistic modeling enables accurate forecasting, risk assessment, and synthetic data generation at the portfolio level.
The Skanalytix Difference
Many popular generative models in machine learning — such as GANs and VAEs — focus on learning thousands of parameters through data-intensive optimization processes. These approaches often require large, clean historical datasets that are not always available in finance. Additionally, they may rely on assumptions or lack interpretability, making them difficult to trust for high-stakes financial decision-making.
By contrast, UNCRi modeling makes no distributional assumptions: it does not assume returns follow a Gaussian (or any other parametric) distribution, nor that they are independent and identically distributed (i.i.d.). This flexibility allows the model to capture both temporal structure and realistic cross-sectional relationships among assets.
UNCRi models also involve many parameters — but with a crucial difference: these are fixed at initialization rather than learned through backpropagation. Rather than fitting complex parametric models, UNCRi employs a non-parametric, memory-based approach governed by a small set of interpretable hyperparameters that reflect real-world statistical features such as volatility clustering, heavy tails, mean reversion, and cross-asset dependencies. This design supports transparent control over the model and generates outputs that closely mirror the statistical properties of real financial time series.
In short, not all generative or predictive models are created equal. Unlike opaque, data-hungry systems, our models are grounded in statistical reasoning and built for interpretability. By using structured probabilistic modeling driven by real-world financial properties, we deliver synthetic data, forecasts, and risk metrics that are transparent, robust, and aligned with the realities of finance.
“Trustworthy synthetic data starts with sound foundations. At Skanalytix, we combine statistical integrity with interpretable modeling to deliver synthetic data you can rely on in high-stakes financial environments.”
This structured foundation not only ensures reliability — it also scales naturally from individual assets to entire portfolios.
Portfolio-Ready and Scalable
Crucially, by modeling the full joint conditional distribution, our framework naturally extends from individual assets to entire portfolios — without assuming independence or relying solely on linear correlation structures. As a result, our framework enables simulation, forecasting, and risk assessment at the portfolio level — capturing joint behavior and dynamic dependencies across assets.
Applications and Capabilities
The capability to estimate joint conditional distributions enables a broad spectrum of predictive and simulation applications:
- Synthetic data generation: Simulating realistic time series that preserve fat tails, volatility clustering, and cross-asset dependencies — closely matching the statistical structure of real markets.
- Forecasting: By computing summary statistics such as the mean, median, or mode of the conditional distribution, along with measures of uncertainty like variance or spread, we produce point forecasts and quantify the range of possible future returns.
- Risk modeling: Simulating forward paths under different market regimes, enabling estimation of Value at Risk (VaR) and stress testing under both typical and extreme scenarios — offering richer insights than traditional parametric models.
See Use Case 1 and Use Case 2 for real-world demonstrations of our synthetic data and risk modeling tools.
Ready to Learn More?
Whether you’re managing portfolio risk, building trading strategies, or generating synthetic data under tight compliance constraints, Skanalytix delivers models built for real-world finance.
Get in touch with us to explore how our models can support your goals — from realistic data generation to forecasting and risk analysis.