How News Expectations Can Produce Fat Tails and Volatility Clustering

Summary

This paper explains two recurring patterns in financial returns: extreme moves occur more often than a simple normal model predicts, and periods of high volatility tend to persist. It proposes an account based on two kinds of market participants. Long-run investors update asset valuations with a news-driven random walk, retaining the accumulated influence of fundamental news. Short-term speculators form anticipated returns through a news-driven autoregressive process, representing shorter memories and feedback associated with trend-following or herding.

The paper argues that these simple linear expectation processes can generate both fat-tailed returns and volatility clustering in a broad, robust way. It presents the framework as an alternative to explanations relying on complex interactions among agents, and does not assume rational expectations. The supplied text describes the proposed mechanism but gives no data, parameter estimates, or tests against competing models. Its claims therefore summarize a theoretical explanation; the excerpt does not establish how well it fits particular assets, markets, or time scales.

Key ideas

• Long-run investors’ valuations are modeled as a random walk driven by news.
• Short-term speculators’ return expectations are modeled as an autoregressive process driven by news.
• The speculator process represents short memory and feedback associated with trend-following or herding.
• The paper argues that these expectation dynamics can produce fat tails and clustered volatility.
• The excerpt describes a theoretical mechanism but provides no empirical tests or parameter estimates.

Tags

# News-driven Expectations and Volatility Clustering


# News-driven Expectations and Volatility Clustering









Financial volatility obeys two fascinating empirical regularities that apply to various assets, on various markets, and on various time scales: it is fat-tailed (more precisely power-law distributed) and it tends to be clustered in time. Many interesting models have been proposed to account for these regularities, notably agent-based models, which mimic the two empirical laws through a complex mix of nonlinear mechanisms such as traders' switching between trading strategies in highly nonlinear way. This paper explains the two regularities simply in terms of traders' attitudes towards news, an explanation that follows almost by definition of the traditional dichotomy of financial market participants, investors versus speculators, whose behaviors are reduced to their simplest forms. Long-run investors' valuations of an asset are assumed to follow a news-driven random walk, thus capturing the investors' persistent, long memory of fundamental news. Short-term speculators' anticipated returns, on the other hand, are assumed to follow a news-driven autoregressive process, capturing their shorter memory of fundamental news, and, by the same token, the feedback intrinsic to the short-sighted, trend-following (or herding) mindset of speculators. These simple, linear, models of traders' expectations, it is shown, explain the two financial regularities in a generic and robust way. Rational expectations, the dominant model of traders' expectations, is not assumed here, owing to the famous no-speculation, no-trade results

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