Stock Market Volatility Tests: A Classical-Keynesian Alternative to Mainstream Interpretations
Emiliano Brancaccio and
Damiano Buonaguidi
International Journal of Political Economy, 2019, vol. 48, issue 3, 253-274
Abstract:
We examine the neoclassical interpretations of Shiller’s tests on stock market volatility and analyze their theoretical and empirical limitations. We then show that volatility can be interpreted in an alternative way in the light of a new macroeconomic model whose main innovative feature is that it relates dividends to the Classical concept of “normal distribution” and stock prices to the Keynesian “principle of effective demand.” While a relatively stable normal rate of profit determines dividends, the continuous fluctuations of investment, income, and saving and the related portfolio choices influence the demand for shares and provoke stock prices volatility with respect to dividends. The Classical-Keynesian model is then extended to contemplate also a “financial instability hypothesis” and a “monetary circuit.” Neoclassical and alternative stock market models are presented here by adopting a comparative approach—that is, a single system of equations in which the causal relations among its variables change according to the theory examined.
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://hdl.handle.net/10.1080/08911916.2019.1655954 (text/html)
Access to full text is restricted to subscribers.
Related works:
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:mes:ijpoec:v:48:y:2019:i:3:p:253-274
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/MIJP20
DOI: 10.1080/08911916.2019.1655954
Access Statistics for this article
More articles in International Journal of Political Economy from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().