| By: |
Cristina Amado (University of Minho and NIPE);
Timo Teräsvirta (Aarhus University, School of Economics and Management and CREATES) |
| Abstract: |
In this paper we develop a testing and modelling procedure for describing the
long-term volatility movements over very long return series. For the purpose,
we assume that volatility is multiplicatively decomposed into a conditional
and an unconditional component as in Amado and Teräsvirta (2011). The latter
component is modelled by incorporating smooth changes so that the
unconditional variance is allowed to evolve slowly over time. Statistical
inference is used for specifying the parameterization of the time-varying
component by applying a sequence of Lagrange multiplier tests. The model
building procedure is illustrated with an application to daily returns of the
Dow Jones Industrial Average stock index covering a period of more than ninety
years. The main conclusions are as follows. First, the LM tests strongly
reject the assumption of constancy of the unconditional variance. Second, the
results show that the long-memory property in volatility may be explained by
ignored changes in the unconditional variance of the long series. Finally,
based on a formal statistical test we find evidence of the superiority of
volatility forecast accuracy of the new model over the GJR-GARCH model at all
horizons for a subset of the long return series. |
| Keywords: |
Model specification; Conditional heteroskedasticity; Lagrange multiplier test; Timevarying unconditional variance; Long financial time series; Volatility persistence. |
| JEL: |
C12 C22 C51 C52 C53 |
| Date: |
2012–02–28 |
| URL: |
http://d.repec.org/n?u=RePEc:aah:create:2012-07&r=fmk |