Nonstationarities in stock returns
Artikel i vetenskaplig tidskrift, 2005

The paper outlines a methodology for analyzing daily stock returns that relinquishes the assumption of global stationarity. Giving up this common working hypothesis reflects our belief that fundamental features of the financial markets are continuously and significantly changing. Our approach approximates the nonstationary data locally by stationary models. The methodology is applied to the S&P 500 series of returns covering a period of over seventy years of market activity. We find most of the dynamics of this time series to be concentrated in shifts of the unconditional variance. The forecasts based on our nonstationary unconditional modeling were found to be superior to those obtained in a stationary long-memory framework and to those based on a stationary Garch(1, 1) data-generating process.

structural-change

model

time-series

long-memory

Författare

Catalin Starica

Göteborgs universitet

C Granger

University of California

Review of Economics and Statistics

0034-6535 (ISSN) 1530-9142 (eISSN)

Vol. 87 3 503-522

Ämneskategorier

SAMHÄLLSVETENSKAP

Nationalekonomi

DOI

10.1162/0034653054638274