Quantitative research focused on realistic market simulation.
Skanalytix Pty Ltd is a Melbourne-based quantitative research company developing generative models for financial time series.
Generative modeling for financial time series.
Our work focuses on realistic simulation of financial markets: generating synthetic time series that capture important characteristics of observed market behaviour without treating the historical record as the only possible outcome.
The models are intended to provide a practical basis for scenario analysis, stress testing, portfolio risk analysis and model validation.
Data-driven and non-parametric.
Rather than assuming a particular distribution for market returns or training a neural generative model, Skanalytix draws directly on information contained in historical market observations.
As a simulation evolves, the distribution of what happens next depends on the market situation that has developed up to that point. This allows simulations to evolve conditionally while capturing important distributional, temporal and cross-asset characteristics.
The objective is not to predict a single future path or simply replay the historical record, but to generate a range of realistic market scenarios.
Andrew Skabar, PhD
Founder, Skanalytix Pty Ltd
Andrew Skabar founded Skanalytix in 2023 following more than two decades working in artificial intelligence, machine learning and decision systems, initially in academia and subsequently through independent research and development. He holds a PhD in artificial intelligence and has a background in physics and mathematics.
His research has centred on methods for learning from complex data and estimating relationships under uncertainty. This work eventually led to the modelling framework underlying Skanalytix and its application to financial time series.
Equity time series and portfolios.
Current development focuses on equity time series and multi-asset portfolios, with particular attention to fat tails, volatility clustering, mean reversion and cross-asset dependencies.
The broader objective is to provide a flexible basis for generating and analysing realistic market scenarios for portfolio risk analysis, stress testing and model validation.