Market implied GDP

Harris Ntantanis () and Lawrence Pohlman ()
Additional contact information
Harris Ntantanis: NP Investment Research
Lawrence Pohlman: University of Massachusetts-Boston

Journal of Asset Management, 2020, vol. 21, issue 7, No 8, 636-646

Abstract: Abstract GDP is the most important and widely studied macroeconomic variable. It indicates the state of an economy and is used as a measure of the economic strength of a country. Due to its comprehensive nature, calculating GDP takes a great deal of work and is often revised over time. This has led to the common practice of forecasting GDP using econometric models. This paper introduces a new method for estimating GDP using a unique data set of options whose values are determined by the levels of GDP and the GDP growth rate. The option is market priced which makes it distinct since it is available daily, subject to no revisions and aggregates the market’s opinion about GDP. These option implied values for GDP and GDP growth rate are similar to the concept of implied volatilities. We show that this option improves the GDP growth rate forecasts by 21% compared to conventional econometric models.

Keywords: GDP; GDP-linked securities; Implied GDP; Forecasting GDP; Nowcasting; Bivariate probability distribution; Option pricing (search for similar items in EconPapers)
JEL-codes: C12 C13 C22 C32 C43 C53 E17 E44 F30 F34 G13 G15 G18 H12 H63 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1057/s41260-020-00176-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:pal:assmgt:v:21:y:2020:i:7:d:10.1057_s41260-020-00176-z

Ordering information: This journal article can be ordered from
http://www.springer.com/finance/journal/41260

DOI: 10.1057/s41260-020-00176-z

Access Statistics for this article

Journal of Asset Management is currently edited by Marielle de Jong and Dan diBartolomeo

More articles in Journal of Asset Management from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().