Department of Economics and Business Economics

Timo Teräsvirta

Modelling changes in the unconditional variance of long stock return series

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Modelling changes in the unconditional variance of long stock return series. / Amado, Cristina; Teräsvirta, Timo.

In: Journal of Empirical Finance, Vol. 25, 2014, p. 15-35.

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@article{d3f4163a636b45f4977830ed93584ddf,
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 daily return series. For this purpose we assume that volatility is multiplicatively decomposed into a conditional and an unconditional component as in Amado and Ter{\"a}svirta (2012, 2013). The latter component is modelled such that the unconditional time-varying component evolves 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 22,986 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 apparent long memory property in volatility may be interpreted as changes in the unconditional variance of the long series. Finally, based on a formal statistical test we find evidence of the superiority of volatility forecasting accuracy of the new model over the GJR-GARCH model at all horizons for eight subsets of the long return series.",
keywords = "Model specification, Conditional heteroskedasticity, Lagrange multiplier test, Time-varying unconditional variance, Long financial time series, Volatility persistence",
author = "Cristina Amado and Timo Ter{\"a}svirta",
note = "Campus adgang til artiklen / Campus access to the article",
year = "2014",
doi = "10.1016/j.jempfin.2013.09.003",
language = "English",
volume = "25",
pages = "15--35",
journal = "Journal of Empirical Finance",
issn = "0927-5398",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Modelling changes in the unconditional variance of long stock return series

AU - Amado, Cristina

AU - Teräsvirta, Timo

N1 - Campus adgang til artiklen / Campus access to the article

PY - 2014

Y1 - 2014

N2 - In this paper we develop a testing and modelling procedure for describing the long-term volatility movements over very long daily return series. For this purpose we assume that volatility is multiplicatively decomposed into a conditional and an unconditional component as in Amado and Teräsvirta (2012, 2013). The latter component is modelled such that the unconditional time-varying component evolves 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 22,986 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 apparent long memory property in volatility may be interpreted as changes in the unconditional variance of the long series. Finally, based on a formal statistical test we find evidence of the superiority of volatility forecasting accuracy of the new model over the GJR-GARCH model at all horizons for eight subsets 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 daily return series. For this purpose we assume that volatility is multiplicatively decomposed into a conditional and an unconditional component as in Amado and Teräsvirta (2012, 2013). The latter component is modelled such that the unconditional time-varying component evolves 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 22,986 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 apparent long memory property in volatility may be interpreted as changes in the unconditional variance of the long series. Finally, based on a formal statistical test we find evidence of the superiority of volatility forecasting accuracy of the new model over the GJR-GARCH model at all horizons for eight subsets of the long return series.

KW - Model specification

KW - Conditional heteroskedasticity

KW - Lagrange multiplier test

KW - Time-varying unconditional variance

KW - Long financial time series

KW - Volatility persistence

U2 - 10.1016/j.jempfin.2013.09.003

DO - 10.1016/j.jempfin.2013.09.003

M3 - Journal article

VL - 25

SP - 15

EP - 35

JO - Journal of Empirical Finance

T2 - Journal of Empirical Finance

JF - Journal of Empirical Finance

SN - 0927-5398

ER -