Volatility products for different maturities display different behavioural patterns. They exhibit different risk-return characteristics. For instance, long term volatility products move less widely than short term ones.
Our investment philosophy is underpinned by modern portfolio theory (MPT) pioneered by Markowitz. MPT is a mathematical framework for assembling a portfolio of assets such that the expected return is maximized for a given level of risk. Its key insight is that an asset’s risk and return should not be assessed by itself, but by how it contributes to a portfolio’s overall risk and return.
A risk premium is a form of compensation or reward for investors who tolerate extra risk, compared to that of a risk-free asset.
Our distinctive investment philosophy is underpinned by the predictable nature of volatility risk premia. Based on economic theory, rigorous statistical studies and empirical tests, it is known that volatility risk premia are significant and time-varying. They are characterized by alternative runs of positive and negative values.
We’ve developed sophisticated quantitative models and utilised machine learning techniques based on historical data to predicate the future movement of volatility risk premia to generate returns.
The impact of human emotions on trading decisions is often the greatest hindrance to performance. Algorithms and computers make decisions and execute trades faster than any human can, and do so free from the influence of emotions. Artificial Intelligence (AI) and Machine Learning (ML) are quietly revolutionizing nearly all areas of our lives.
Machine learning involves feeding an algorithm data samples, usually derived from historical prices. The data samples consist of variables called predictors, as well as a target variable, which is the expected outcome. The algorithm learns to use the predictor variables to predict the target variable.