Department of Economics and Business Economics

Timo Teräsvirta

Parameterizing unconditional skewness in models for financial time series

Research output: ResearchWorking paper

Standard

Parameterizing unconditional skewness in models for financial time series. / He, Changli; Silvennoinen, Annastiina; Teräsvirta, Timo.

Aarhus : Institut for Økonomi, Aarhus Universitet, 2008.

Research output: ResearchWorking paper

Harvard

He, C, Silvennoinen, A & Teräsvirta, T 2008 'Parameterizing unconditional skewness in models for financial time series' Institut for Økonomi, Aarhus Universitet, Aarhus.

APA

He, C., Silvennoinen, A., & Teräsvirta, T. (2008). Parameterizing unconditional skewness in models for financial time series. Aarhus: Institut for Økonomi, Aarhus Universitet.

CBE

He C, Silvennoinen A, Teräsvirta T. 2008. Parameterizing unconditional skewness in models for financial time series. Aarhus: Institut for Økonomi, Aarhus Universitet.

MLA

He, Changli, Annastiina Silvennoinen, and Timo Teräsvirta Parameterizing unconditional skewness in models for financial time series. Aarhus: Institut for Økonomi, Aarhus Universitet. 2008., 22 p.

Vancouver

He C, Silvennoinen A, Teräsvirta T. Parameterizing unconditional skewness in models for financial time series. Aarhus: Institut for Økonomi, Aarhus Universitet. 2008.

Author

He, Changli ; Silvennoinen, Annastiina ; Teräsvirta, Timo. / Parameterizing unconditional skewness in models for financial time series. Aarhus : Institut for Økonomi, Aarhus Universitet, 2008.

Bibtex

@techreport{d0cef860cd8d11dcabe4000ea68e967b,
title = "Parameterizing unconditional skewness in models for financial time series",
abstract = "In this paper we consider the third-moment structure of a class of time series models. It isoften argued that the marginal distribution of financial time series such as returns is skewed.Therefore it is of importance to know what properties a model should possess if it is toaccommodate unconditional skewness. We consider modelling the unconditional mean andvariance using models that respond nonlinearly or asymmetrically to shocks. We investigatethe implications of these models on the third-moment structure of the marginal distributionas well as conditions under which the unconditional distribution exhibits skewness andnonzero third-order autocovariance structure. In this respect, an asymmetric or nonlinearspecification of the conditional mean is found to be of greater importance than the propertiesof the conditional variance. Several examples are discussed and, whenever possible, explicitanalytical expressions provided for all third-order moments and cross-moments. Finally, weintroduce a new tool, the shock impact curve, for investigating the impact of shocks on theconditional mean squared error of return series.",
keywords = "Asymmetry; GARCH; Nonlinearity; Shock Impact Curve; Time series; Unconditional",
author = "Changli He and Annastiina Silvennoinen and Timo Teräsvirta",
year = "2008",
publisher = "Institut for Økonomi, Aarhus Universitet",
type = "WorkingPaper",
institution = "Institut for Økonomi, Aarhus Universitet",

}

RIS

TY - UNPB

T1 - Parameterizing unconditional skewness in models for financial time series

AU - He,Changli

AU - Silvennoinen,Annastiina

AU - Teräsvirta,Timo

PY - 2008

Y1 - 2008

N2 - In this paper we consider the third-moment structure of a class of time series models. It isoften argued that the marginal distribution of financial time series such as returns is skewed.Therefore it is of importance to know what properties a model should possess if it is toaccommodate unconditional skewness. We consider modelling the unconditional mean andvariance using models that respond nonlinearly or asymmetrically to shocks. We investigatethe implications of these models on the third-moment structure of the marginal distributionas well as conditions under which the unconditional distribution exhibits skewness andnonzero third-order autocovariance structure. In this respect, an asymmetric or nonlinearspecification of the conditional mean is found to be of greater importance than the propertiesof the conditional variance. Several examples are discussed and, whenever possible, explicitanalytical expressions provided for all third-order moments and cross-moments. Finally, weintroduce a new tool, the shock impact curve, for investigating the impact of shocks on theconditional mean squared error of return series.

AB - In this paper we consider the third-moment structure of a class of time series models. It isoften argued that the marginal distribution of financial time series such as returns is skewed.Therefore it is of importance to know what properties a model should possess if it is toaccommodate unconditional skewness. We consider modelling the unconditional mean andvariance using models that respond nonlinearly or asymmetrically to shocks. We investigatethe implications of these models on the third-moment structure of the marginal distributionas well as conditions under which the unconditional distribution exhibits skewness andnonzero third-order autocovariance structure. In this respect, an asymmetric or nonlinearspecification of the conditional mean is found to be of greater importance than the propertiesof the conditional variance. Several examples are discussed and, whenever possible, explicitanalytical expressions provided for all third-order moments and cross-moments. Finally, weintroduce a new tool, the shock impact curve, for investigating the impact of shocks on theconditional mean squared error of return series.

KW - Asymmetry; GARCH; Nonlinearity; Shock Impact Curve; Time series; Unconditional

M3 - Working paper

BT - Parameterizing unconditional skewness in models for financial time series

PB - Institut for Økonomi, Aarhus Universitet

ER -