News-Driven Expectations and Volatility Clustering

Sabiou Inoua

Working Papers from Chapman University, Economic Science Institute

Abstract: Financial volatility obeys two well-established empirical properties: it is fattailed (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 computational models, which typically invoke complicated mechanisms, however. It can be shown that trend-following speculation generates the power law in an intrinsic way. But this model cannot exaplain clustered volatility. This paper extends the model and offers a simple explanation for clustered volatility: the impact of exogenous news on traders’ expectations. Owing to the famous no-trade results, rational expectations, the dominant model of news-driven expectations, is hard to reconcile with the incessant high-frequency trading behind the volatility clustering. The simplest alternative model of news-driven expectations is to assume that traders have prior views about the market (an asset’s future price change or its present value) and then modify their views with the advent of a news. This simple news-driven random walk of traders’ expectations explains volatility clustering in a generic way. Liquidity plays a crucial role in this dynamics of volatility, which is emphasized in a dicussions section.

Keywords: Volatility Clustering; Power Law; Trend Following; Efficient Market Hypothesis; Liquidity (search for similar items in EconPapers)
Date: 2019
New Economics Papers: this item is included in nep-mst and nep-rmg
References: Add references at CitEc
Citations:

Downloads: (external link)
https://digitalcommons.chapman.edu/esi_working_papers/294/

Related works:
Journal Article: News-Driven Expectations and Volatility Clustering (2020)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:chu:wpaper:19-33

Access Statistics for this paper

More papers in Working Papers from Chapman University, Economic Science Institute Contact information at EDIRC.
Bibliographic data for series maintained by Megan Luetje ().