Modeling the Volatility-Return Trade-Off When Volatility May Be Nonstationary

Christian Dahl and Emma Iglesias

Journal of Time Series Econometrics, 2011, vol. 3, issue 1, 32

Abstract: In this paper, a new GARCH-M type model, denoted as GARCH-AR, is proposed. In particular, it is shown that it is possible to generate a volatility-return trade-off in a regression model simply by introducing dynamics in the standardized disturbance process. Importantly, the volatility in the GARCH-AR model enters the return function in terms of relative volatility, implying that the risk term can be stationary even if the volatility process is nonstationary. We provide a complete characterization of the stationarity properties of the GARCH-AR process by generalizing the results of Bougerol and Picard (1992b). Furthermore, allowing for nonstationary volatility, the asymptotic properties of the estimated parameters by quasi-maximum likelihood in the GARCH-AR process are established. Finally, we stress the importance of being able to choose correctly between AR-GARCH and GARCH-AR processes. We provide an empirical illustration showing the empirical relevance of the GARCH-AR model based on modeling a wide range of leading U.S. stock return series.

Keywords: quasi-maximum likelihood; GARCH-M model; asymptotic properties; volatility-return relation (search for similar items in EconPapers)
Date: 2011
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2202/1941-1928.1093 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
Working Paper: Modelling the Volatility-Return Trade-off when Volatility may be Nonstationary (2009)
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:bpj:jtsmet:v:3:y:2011:i:1:n:10

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/jtse/html

DOI: 10.2202/1941-1928.1093

Access Statistics for this article

Journal of Time Series Econometrics is currently edited by Javier Hidalgo

More articles in Journal of Time Series Econometrics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().