A century and a half of the monetary base-stock market relationship
Nahiyan Faisal Azad and
Apostolos Serletis
The Quarterly Review of Economics and Finance, 2022, vol. 85, issue C, 118-124
Abstract:
Using the longest spanning monthly data ever studied in the empirical literature, dating back to 1870, we investigate the relationship between the money supply and the stock market in the United Kingdom. In the context of bivariate and multivariate structural generalized autoregressive conditional heteroskedasticity (GARCH)-in-Mean VAR models, we document that increased money growth volatility has significant negative effects on the stock market. Our results are broadly consistent with the empirical literature and provide conclusive evidence that increased uncertainty about the growth rate of money, has a negative and statistically significant effect on economic activity. Also, accounting for money growth uncertainty, we find that an unanticipated increase (decrease) in money growth leads to fall (rise) in the unemployment rate and the interest rate and to a rise (fall) in share prices.
Keywords: Money growth uncertainty; Stock prices; Multivariate GARCH-in-Mean VAR (search for similar items in EconPapers)
JEL-codes: C22 E44 E5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S1062976920301587
Full text for ScienceDirect subscribers only
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:eee:quaeco:v:85:y:2022:i:c:p:118-124
DOI: 10.1016/j.qref.2020.11.002
Access Statistics for this article
The Quarterly Review of Economics and Finance is currently edited by R. J. Arnould and J. E. Finnerty
More articles in The Quarterly Review of Economics and Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().