News-driven Expectations and Volatility Clustering
Sabiou Inoua

TL;DR
This paper presents a simple, news-driven model of trader expectations that explains the empirical regularities of fat-tailed and clustered volatility in financial markets without relying on complex nonlinear agent-based models or rational expectations.
Contribution
It introduces a linear, news-based framework distinguishing long-term investors and short-term speculators to explain volatility regularities.
Findings
Long-term investors' valuations follow a news-driven random walk.
Short-term speculators' expectations follow a news-driven autoregressive process.
The model robustly explains fat-tailed and clustered volatility in various markets.
Abstract
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…
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Taxonomy
TopicsComplex Systems and Time Series Analysis · Financial Markets and Investment Strategies · Economic theories and models
