Introduction

In today’s challenging environment of geopolitical upheaval, trade uncertainty and heightened exposure to certain technology themes, are investors’ equity portfolio risk analyses up to the task? Do practitioners have a clear view of unwanted exposures, correlations, or the reward that one might anticipate for taking a given amount of risk? Despite the existence of high-quality solutions for modelling equity portfolio risks, the operational burdens associated with more comprehensive tools have left many investors relying on simpler, less data-intensive approaches. These provide insight but create backward-looking blind-spots and biases. This Market Review revisits the importance of robust risk modelling by examining two popular indices—the S&P 500 and the STOXX Europe 600—to see whether ‘point-in-time’ portfolio risks and exposures based on holdings at end-2025 were materially different from standard ‘historical’ statistics.

Key takeaways

• Point-in-time analysis of the S&P 500 uncovers higher exposure to market beta and certain sectors (particularly Technology) than a historical analysis would reveal, as well as short exposure to certain fundamental (style) factors. As a result, S&P 500’s volatility calculated on the last three years of historical returns is underestimated by 2.2% compared to a point-in-time forward-looking volatility. A similar analysis for STOXX Europe 600 leads to a 3.9% underestimation of risk.
• Moreover, the point-in-time factor profile for both the S&P 500 and the STOXX Europe 600 implies a reduction in simulated (out of sample) long-term average returns by approximately 1.1% and 0.8% p.a. respectively. A reduction is also observed in risk-adjusted long-term average returns.
• Extreme risk needs its own analysis. Despite the higher level of volatility, the point-in-time extreme risk estimate is not materially different from its historical counterpart. This is consistent with our previous study showing that extreme risk levels can be decoupled from day to day risk.

Since Scientific Portfolio’s risk model is used for this analysis, we also briefly outline how it seeks to square the circle of resource-light risk modelling, potentially democratizing best practice by – for example – finding ways to deliver key benefits of cross-sectional analysis without the onerous data requirements that would typically be involved.

Author

Matteo Bagnara, PhD
Senior Quant Researcher,
Scientific Climate Indices ……………………………………….

Shahyar Safaee
Deputy CEO and Business Development Director,
Scientific Climate Indices

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