Asset Pricing Using Block-Cholesky GARCH and Time-Varying Betas

Research output: Working paperResearch

Standard

Asset Pricing Using Block-Cholesky GARCH and Time-Varying Betas. / Grassi, Stefano; Violante, Francesco.

Aarhus : Institut for Økonomi, Aarhus Universitet, 2021.

Research output: Working paperResearch

Harvard

Grassi, S & Violante, F 2021 'Asset Pricing Using Block-Cholesky GARCH and Time-Varying Betas' Institut for Økonomi, Aarhus Universitet, Aarhus.

APA

Grassi, S., & Violante, F. (2021). Asset Pricing Using Block-Cholesky GARCH and Time-Varying Betas. Institut for Økonomi, Aarhus Universitet. CREATES Research Papers No. 2021-05

CBE

Grassi S, Violante F. 2021. Asset Pricing Using Block-Cholesky GARCH and Time-Varying Betas. Aarhus: Institut for Økonomi, Aarhus Universitet.

MLA

Grassi, Stefano and Francesco Violante Asset Pricing Using Block-Cholesky GARCH and Time-Varying Betas. Aarhus: Institut for Økonomi, Aarhus Universitet. (CREATES Research Papers; Journal number 2021-05). 2021., 49 p.

Vancouver

Grassi S, Violante F. Asset Pricing Using Block-Cholesky GARCH and Time-Varying Betas. Aarhus: Institut for Økonomi, Aarhus Universitet. 2021 Mar 1.

Author

Grassi, Stefano ; Violante, Francesco. / Asset Pricing Using Block-Cholesky GARCH and Time-Varying Betas. Aarhus : Institut for Økonomi, Aarhus Universitet, 2021. (CREATES Research Papers; No. 2021-05).

Bibtex

@techreport{40460b625a9f45e9a9d0fc86d7294e75,
title = "Asset Pricing Using Block-Cholesky GARCH and Time-Varying Betas",
abstract = "Starting from the Cholesky-GARCH model, recently proposed by Darolles, Francq, and Laurent (2018), the paper introduces the Block-Cholesky GARCH (BC-GARCH). This new model adapts in a natural way to the asset pricing framework. After deriving conditions for stationarity, uniform invertibility and beta tracking, we investigate the finite sample properties of a variety of maximum likelihood estimators suited for the BC-GARCH by means of an extensive Monte Carlo experiment. We illustrate the usefulness of the BC-GARCH in two empirical applications. The first tests for the presence of beta spillovers in a bivariate system in the context of the Fama and French (1993) three factor framework. The second empirical application consists of a large scale exercise exploring the cross-sectional variation of expected returns for 40 industry portfolios.",
keywords = "Cholesky decomposition, Multivariate GARCH, Asset Pricing, Time Varying Beta, Two Pass Regression",
author = "Stefano Grassi and Francesco Violante",
year = "2021",
month = mar,
day = "1",
language = "English",
series = "CREATES Research Papers",
publisher = "Institut for {\O}konomi, Aarhus Universitet",
number = "2021-05",
type = "WorkingPaper",
institution = "Institut for {\O}konomi, Aarhus Universitet",

}

RIS

TY - UNPB

T1 - Asset Pricing Using Block-Cholesky GARCH and Time-Varying Betas

AU - Grassi, Stefano

AU - Violante, Francesco

PY - 2021/3/1

Y1 - 2021/3/1

N2 - Starting from the Cholesky-GARCH model, recently proposed by Darolles, Francq, and Laurent (2018), the paper introduces the Block-Cholesky GARCH (BC-GARCH). This new model adapts in a natural way to the asset pricing framework. After deriving conditions for stationarity, uniform invertibility and beta tracking, we investigate the finite sample properties of a variety of maximum likelihood estimators suited for the BC-GARCH by means of an extensive Monte Carlo experiment. We illustrate the usefulness of the BC-GARCH in two empirical applications. The first tests for the presence of beta spillovers in a bivariate system in the context of the Fama and French (1993) three factor framework. The second empirical application consists of a large scale exercise exploring the cross-sectional variation of expected returns for 40 industry portfolios.

AB - Starting from the Cholesky-GARCH model, recently proposed by Darolles, Francq, and Laurent (2018), the paper introduces the Block-Cholesky GARCH (BC-GARCH). This new model adapts in a natural way to the asset pricing framework. After deriving conditions for stationarity, uniform invertibility and beta tracking, we investigate the finite sample properties of a variety of maximum likelihood estimators suited for the BC-GARCH by means of an extensive Monte Carlo experiment. We illustrate the usefulness of the BC-GARCH in two empirical applications. The first tests for the presence of beta spillovers in a bivariate system in the context of the Fama and French (1993) three factor framework. The second empirical application consists of a large scale exercise exploring the cross-sectional variation of expected returns for 40 industry portfolios.

KW - Cholesky decomposition

KW - Multivariate GARCH

KW - Asset Pricing

KW - Time Varying Beta

KW - Two Pass Regression

M3 - Working paper

T3 - CREATES Research Papers

BT - Asset Pricing Using Block-Cholesky GARCH and Time-Varying Betas

PB - Institut for Økonomi, Aarhus Universitet

CY - Aarhus

ER -