Nobel Prize Winner Thomas Sargent on Risk, Ambiguity, and Investment Decision Making

Nobel Prize winner and NYU Professor Thomas Sargent

Delegates at the 66th CFA Institute Annual Conference in Singapore were treated to a discourse on rational expectations and ambiguity from Nobel Prize winner Thomas Sargent, the William R. Berkley Professor of Economics and Business at the New York University Stern School of Business.

Investment decision making is challenging because it involves both risk and ambiguity, argued Sargent, who earned his Nobel Prize in economics in 2011 for his research on cause and effect in the macroeconomy. By risk, he means uncertainty about the future. By ambiguity, he means ignorance about the appropriate probability distribution or appropriate model. Professor Sargent contends that many financial models fail to distinguish between the two concepts and, consequently, that investors often mistake ambiguity premiums for risk premiums.

Historically, decision makers of all types have used rational expectations as a basic underlying assumption of their economic and financial models. While it has been a useful tool and was a significant leap forward, it is nevertheless an incomplete model. Sargent’s work is pioneering the way in articulating and separating the various components of risk.

Some highlights from his lecture:

  • Many models inherently assume a universal probability distribution for all actors, Sargent said. While there are strengths to this argument, there are also weaknesses, as people do live in a complex world where actors are not always free to participate in various activities, thus rendering markets incomplete.
  • Citing the Ellsberg paradox, Sargent demonstrated that we live in a heterogeneous world where different people can have different probability distributions. In particular, the Ellsberg paradox demonstrates the inherent conflict of Bayesians.
  • Model ambiguity leads to rapid changes in beliefs. As events unfold, the world gathers more evidence, which collectively pushes people into believing one model may be more appropriate than another. While the data may or may not be conclusive, the shifting of attitudes about which model is right creates tremendous volatility in markets. Consequently, a small amount of ambiguity can substitute for a large amount of risk.
  • Sargent demonstrated how to compare known probability distributions when the choice is ambiguous, using the log-likelihood ratio.
  • The Nobel Prize winner concluded his remarks by illustrating that the robust rule can remove model ambiguity and thereby enable investors to focus on risk.

Please note that the content of this site should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute.

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