A Penalised OLS Framework for High-Dimensional Multivariate Stochastic Volatility Models

Material type: 文書・図像類

Author: Poignard, Benjaminほか

Publisher: Graduate School of Economics, Osaka University

Publication date: 2020-01

Material Format: Paper

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Note (General)：: Although multivariate stochastic volatility (MSV) models usually produce more accurate forecasts compared to multivariate GARCH models, their estimati...

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Paper

Material Type: 文書・図像類
Title: A Penalised OLS Framework for High-Dimensional Multivariate Stochastic Volatility Models
Author/Editor: Poignard, Benjamin
Asai, Manabu
Author Heading: Poignard, Benjamin
https://nrid.nii.ac.jp/nrid/1000040845252
Asai, Manabu
https://nrid.nii.ac.jp/nrid/1000090319484
Publication, Distribution, etc.: Graduate School of Economics, Osaka University
Publication Date: 2020-01
Publication Date (W3CDTF): 2020-01
Periodical title: Discussion Papers In Economics And Business
No. or year of volume/issue: 20-02
Volume: 20-02
Pages: 1-46
Text Language Code: eng
Subject Heading: Forecasting
Multivariate Stochastic Volatility
Oracle Property
Penalised M-estimation
Target Audience: 一般
Note (General): Although multivariate stochastic volatility (MSV) models usually produce more accurate forecasts compared to multivariate GARCH models, their estimation techniques such as Monte Carlo likelihood or Bayesian Markov Chain Monte Carlo are computationally demanding and thus suffer from the so-called "curse of dimensionality": using such methods, the applications are typically restricted to low-dimensional vectors. In this paper, we propose a fast estimation approach for MSV models based on a penalised ordinary least squares framework. Specifying the MSV model as a multivariate state-space model, we propose a two-step penalised procedure for estimating the latter using a broad range of potentially non-convex penalty functions. In the first step, we approximate an EGARCH type dynamic using a penalised AR process with a suffciently large number of lags, providing a sparse estimator. Conditionally on this first step estimator, we estimate the state vector based on a AR type dynamic. This two-step procedure relies on OLS based loss functions and thus easily accommodates high-dimensional vectors. We provide the large sample properties of the two-step estimator together with the so-called support recovery of the first step estimator. The empirical performances of our method are illustrated through in-sample simulations and out-of-sample variance-covariance matrix forecasts, where we consider as competitors commonly used MGARCH models.
References (URI): http://www2.econ.osaka-u.ac.jp/econ_society/dp/2002.pdf
URI
http://www2.econ.osaka-u.ac.jp/econ_society/dp/2002.pdf
Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）
https://irdb.nii.ac.jp
Original Data Provider (Database): 大阪大学 : 大阪大学学術情報庫　桜華