The Demand for Long-term Government Securities
Neil Thompson
Additional contact information
Neil Thompson: University of Salford
Chapter 9 in Portfolio Theory and the Demand for Money, 1993, pp 131-135 from Palgrave Macmillan
Abstract:
Abstract In comparison to the plethora of empirical studies on the demand for money function, there have been few attempts to model the non-bank private sector’s demand for long-term government securities (gilts). The issue was analysed by Norton (1969) in a pioneering study, while more recent models of the demand for government bonds are developed in Spencer (1981) and Hoggarth and Ormerod (1985). One of the main issues, analysed by these later studies, is the role and importance of expected capital gains in determining the demand for gilts. In each case, an attempt is made to model the relative return on gilts in each period, defined as the long-term rate of interest plus the ex post capital gain over the period, less the rate of interest obtainable on alternative capital-safe short-term assets.1 Various models are used for this purpose, ranging from simple autoregressive forecasting schemes to more sophisticated ‘structural’ models, which relate the relative return to explicit economic information.
Keywords: Interest Rate; Money Supply; Capital Gain; Portfolio Theory; Financial Wealth (search for similar items in EconPapers)
Date: 1993
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
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:palchp:978-1-349-22827-0_9
Ordering information: This item can be ordered from
http://www.palgrave.com/9781349228270
DOI: 10.1007/978-1-349-22827-0_9
Access Statistics for this chapter
More chapters in Palgrave Macmillan Books from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().