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Archmodels.Jl: Estimating Arch Models in Julia


Broda, Simon A; Paolella, Marc S (2020). Archmodels.Jl: Estimating Arch Models in Julia. Econometrics: Computer Programs & Software SSRN eJournal 3551503, University of Zurich.

Abstract

This paper introduces ARCHModels.jl, a package for the Julia programming language that implements a number of univariate and multivariate ARCH-type models. This model class is the workhorse tool for modelling the conditional volatility of financial assets. Their distinguishing feature is that they model the latent volatility as a (deterministic) function of past returns and volatilities. This recursive structure results in loop-heavy code which, due to its just-in-time compiler, Julia is well-equipped to handle. As such, the entire package is written in Julia, without any binary dependencies. We benchmark the performance of ARCHModels.jl against popular implementations in MATLAB, R, and Python, and illustrate its use in a detailed case study.

Abstract

This paper introduces ARCHModels.jl, a package for the Julia programming language that implements a number of univariate and multivariate ARCH-type models. This model class is the workhorse tool for modelling the conditional volatility of financial assets. Their distinguishing feature is that they model the latent volatility as a (deterministic) function of past returns and volatilities. This recursive structure results in loop-heavy code which, due to its just-in-time compiler, Julia is well-equipped to handle. As such, the entire package is written in Julia, without any binary dependencies. We benchmark the performance of ARCHModels.jl against popular implementations in MATLAB, R, and Python, and illustrate its use in a detailed case study.

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Additional indexing

Item Type:Working Paper
Communities & Collections:03 Faculty of Economics > Department of Banking and Finance
Dewey Decimal Classification:330 Economics
JEL Classification:C87
Uncontrolled Keywords:ARCH, GARCH, CCC, DCC, Value at Risk, Julia
Language:English
Date:9 March 2020
Deposited On:14 Apr 2020 07:05
Last Modified:21 Apr 2020 14:24
Series Name:Econometrics: Computer Programs & Software SSRN eJournal
Number of Pages:17
ISSN:1556-5068
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.2139/ssrn.3551503
Project Information:

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