Limitations of Correlation Measures in Systematic Strategies
An short article by Aref Karim, CEO & CIO, Quality Capital Management Ltd
May 2023
General
Correlation is a statistical measure that describes the relationship between two or more variables. These can be price movements of different securities, assets, or strategies.
When trading in active markets, futures or otherwise, understanding the limitations of correlation measures is essential for proper risk assessment and decision-making. While correlation analysis can provide valuable insights into the relationships between variables, it is crucial to be aware of its inherent weaknesses.
Below are some limitations of correlation measures in the context of systematic strategies. They are particularly relevant to portfolio management in the quantitative hedge-fund world.
Limitations
Time-Varying Nature
A significant weakness of correlation measures is their assumption of a stable relationship over time. In financial markets, correlations can change due to shifting market dynamics, economic conditions, or regulatory changes. Failing to account for the time-varying nature of correlations can lead to inaccurate risk assessments and flawed trading strategies.
Non-Linear Relationships
Correlation measures capture linear relationships between variables but often fail to capture non-linear dependencies. In futures markets, the relationship between different assets or strategies can exhibit complex patterns that extend beyond simple linear correlations. Relying solely on linear correlations may therefore overlook significant risk factors, thus misrepresenting the true interdependencies between variables.
Tail Risk and Extreme Events
Correlation measures may not adequately capture extreme events and tail risk, which are critical considerations in trading. During periods of market stress or crises, correlations between markets can change significantly, leading to increased co-movement. Failing to incorporate tail risk analysis can underestimate portfolio risk and expose traders to unexpected losses during extreme market conditions. In the GFC of 2008 or the covid pandemic of 2020, correlations broke down with significant market spikes amid unprecedented exogenous shocks.
Hidden Factors and Spurious Correlations
While correlation analysis identifies apparent relationships between variables, it does not always account for hidden factors or spurious correlations. In financial markets, correlations between variables may arise due to unrelated factors or mere coincidence. Failing to distinguish between meaningful relationships and spurious correlations can lead to erroneous trading decisions and ineffective risk management strategies.
Limited Perspective
Correlation measures provide insights into the linear relationship between two variables but overlook other crucial factors such as volatility, market regimes, and fundamental drivers. In futures trading, considering a broader range of risk measures and market indicators beyond correlation is essential for a comprehensive understanding of portfolio risk. Ignoring these additional factors may result in incomplete risk assessments and misguided trading strategies.
Conclusion
While correlation analysis can be a useful starting point and offer valuable insights into relationships between variables, it is important to be aware of their limitations in systematic trading. At QCM, the assessment of risk extends beyond statistical correlations, and a greater emphasis is placed on proprietary measures of relative risk for both assets and strategies. QCM employs its own unique models and methodologies to dynamically shape position sizes and portfolio exposure.
QCM is a 27-year-old UK hedge fund manager. It offers investors a liquid, fully diversified investment portfolio of 100 financial and commodity futures, managed through its QCM Systematic Macro Programme.