Quantitative research
Start with the evidence.
We study the behaviour of volatility using quantitative models, statistical analysis and financial economic theory.
Investment philosophy
The discipline
behind the decisions.
We believe an investment strategy should be grounded in rigorous quantitative research and sound financial economic analysis.
01Portfolio optimisation
The portfolio.
Not just the position.
Risk and return are considered in the context of the whole portfolio, not an asset in isolation.
Our approach draws on modern portfolio theory, pioneered by Harry Markowitz: assembling a portfolio with regard to expected return, risk and the relationships between its assets.
02Volatility risk premia
Understanding
the price of risk.
A risk premium represents compensation for taking risk. Our research examines how volatility risk premia vary over time.
Economic theory, statistical studies and empirical testing form the foundation of our models. We study the conditions in which risk premia change, rather than treating them as a constant.
The objective is to translate that understanding into a disciplined investment process. Model estimates remain uncertain and can be wrong.
01Mean reversion
02Persistent behaviour
03Asymmetric risk
03Machine learning
Patterns in data.
Discipline in application.
Machine learning is part of our research toolkit, alongside financial economics and statistical analysis.
Algorithms learn relationships between predictor variables and target outcomes using historical data. We use these techniques to examine volatility behaviour and inform model development.
A rules-based process reduces reliance on discretionary views at the point of execution. Historical relationships can change, so modelling, testing and risk oversight remain integral to the approach.
04Our investment process
From research
to implementation.
Four connected stages.
One disciplined approach.
Design & testing
Challenge the model.
Research is translated into trading strategies, with variations back-tested and refined before implementation.
Trade execution
Translate research into action.
Our automated system connects directly to the broker, applying rules-based decisions and rebalancing as opportunities arise.
Risk management
Keep risk in view.
Calendar spreads across expiries, daily profit-and-loss monitoring and regular value-at-risk reviews support portfolio oversight. Risk controls are designed to manage exposure; they cannot eliminate investment risk.
Capital at risk. Past performance, simulated performance and forecasts are not reliable indicators of future results.
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