Research output: Working paper › Research

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

Research output: Working paper › Research

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

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

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

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.

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

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).

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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",

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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 -