Forward-looking scenario analysis is a tool that can provide a more realistic assessment of the downside risks and upside potential of investment portfolios than traditional approaches. It captures the sensitivity of portfolios to events through the mathematical articulation of expert opinion. It challenges and enhances our understanding of likely portfolio behavior in extreme scenarios. And it allows us to prepare for the unexpected.
Two common weaknesses in stress-testing methodologies were exposed by the financial crisis: an overreliance on historical statistical relationships and a lack of qualitative expert judgement.
Traditional risk models generally fail to capture the downside risks of portfolios during crises. They assume that market returns follow a normal distribution. They treat returns as “independent and identically distributed”—like tossing a coin, where the result of the last throw has no impact on what comes next. They fail to take into account that risk assets become increasingly correlated in times of stress. This means most risk models significantly underestimate the probability–and impact–of “tail events,” those extreme market moves that occur far more regularly than predicted under a normal distribution.
Scenario analysis uses data that more closely represent the actual returns of asset classes—and the variable correlations between them. Our approach recognizes that markets oscillate across a wide set of different regimes characterized by varying levels of volatility and the changing interplay among assets. We employ experts who analyze the fundamental drivers of market returns. We blend expert opinion with quantitative analysis to generate a forward-looking distribution of expected returns that is consistent with the expert views but appropriately informed by history.
Scenario analysis uses data that more closely represent the actual returns of asset classes ‐ and the variable correlations between them.
In this paper, we describe how we use the mathematics of “maximum entropy” to achieve this aim. This principal has a long history in the world of physics. The global financial crisis triggered an exploration of its potential to improve the robustness of stress-testing and scenario analysis.
This approach provides us with the expected behavior when the unexpected happens. In particular, it allows portfolio managers to identify any unintended concentration risks in a portfolio.
We illustrate our approach in relation to the historic returns of the U.S. equity and U.S. credit markets to demonstrate that an approach incorporating multiple scenarios improves the fit to actual data. It captures the changing relationship between these markets at different points in the economic cycle.
We then use our approach to compare the impact of a single scenario–a theoretical China crisis–on two portfolios: a typical US institutional strategic asset allocation and a diversified multi-asset portfolio that targets absolute returns. The U.S. institutional asset allocation combines long-only exposures to a limited number of markets. By contrast, the diversified multi-asset portfolio combines a mix of long-only exposures to a broader range of markets with long-short positions designed to extract relative value. We find that the absolute return portfolio is much more resilient in this scenario, significantly reducing the decline in value relative to that expected for the U.S. institutional asset allocation. This is consistent with our expectations across a broad range of stress scenarios.
We think that forward-looking scenario analysis better captures the pernicious nature of tail risks and helps to ensure that portfolio performance is robust to a range of possible states of the world. It is a powerful tool for asset managers to explore the potential weaknesses in their portfolios.
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