Department of Economics and Business Economics

Timo Teräsvirta

Modelling Changes in the Unconditional Variance of Long Stock Return Series

Research output: ResearchWorking paper

Standard

Modelling Changes in the Unconditional Variance of Long Stock Return Series. / Amado, Cristina; Teräsvirta, Timo.

Aarhus : Institut for Økonomi, Aarhus Universitet, 2012.

Research output: ResearchWorking paper

Harvard

Amado, C & Teräsvirta, T 2012 'Modelling Changes in the Unconditional Variance of Long Stock Return Series' Institut for Økonomi, Aarhus Universitet, Aarhus.

APA

Amado, C., & Teräsvirta, T. (2012). Modelling Changes in the Unconditional Variance of Long Stock Return Series. Aarhus: Institut for Økonomi, Aarhus Universitet.

CBE

Amado C, Teräsvirta T. 2012. Modelling Changes in the Unconditional Variance of Long Stock Return Series. Aarhus: Institut for Økonomi, Aarhus Universitet.

MLA

Amado, Cristina and Timo Teräsvirta Modelling Changes in the Unconditional Variance of Long Stock Return Series. Aarhus: Institut for Økonomi, Aarhus Universitet. 2012., 30 p.

Vancouver

Amado C, Teräsvirta T. Modelling Changes in the Unconditional Variance of Long Stock Return Series. Aarhus: Institut for Økonomi, Aarhus Universitet. 2012.

Author

Amado, Cristina ; Teräsvirta, Timo. / Modelling Changes in the Unconditional Variance of Long Stock Return Series. Aarhus : Institut for Økonomi, Aarhus Universitet, 2012.

Bibtex

@techreport{5b42b8cdaaf7456db7c4e917e1433a02,
title = "Modelling Changes in the Unconditional Variance of Long Stock Return Series",
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",
author = "Cristina Amado and Timo Teräsvirta",
year = "2012",
publisher = "Institut for Økonomi, Aarhus Universitet",
type = "WorkingPaper",
institution = "Institut for Økonomi, Aarhus Universitet",

}

RIS

TY - UNPB

T1 - Modelling Changes in the Unconditional Variance of Long Stock Return Series

AU - Amado,Cristina

AU - Teräsvirta,Timo

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

KW - Model specification; Conditional heteroskedasticity; Lagrange multiplier test; Timevarying unconditional variance; Long financial time series; Volatility persistence

M3 - Working paper

BT - Modelling Changes in the Unconditional Variance of Long Stock Return Series

PB - Institut for Økonomi, Aarhus Universitet

ER -