Adaptive LASSO-MGARCH for Multivariate Volatility Forecasting

Yongdeng Xu, Juyi Lyu () and Wenna Lu ()
Additional contact information
Juyi Lyu: Loughborough University, UK
Wenna Lu: Cardiff Metropolitan University, Cardiff, UK, https://www.cardiffmet.ac.uk/staff/wenna-lu/

No E2026/4, Cardiff Economics Working Papers from Cardiff University, Cardiff Business School, Economics Section

Abstract: This paper evaluates an Adaptive LASSO-MGARCH model for multivariate volatility forecasting, with an application to green and conventional bonds, equities, energy commodities, and EU carbon allowances. By introducing coefficient-specific adaptive penalisation directly into the multivariate GARCH variance equations, the model delivers a sparse and data-driven volatility spillover structure while preserving positive definiteness of the conditional covariance matrix. Using daily data on green and conventional bonds, equities, energy commodities, and carbon allowances, we show that adaptive regularisation substantially reduces model complexity and improves economic interpretability relative to an unpenalised MGARCH benchmark. Out-of-sample forecasting experiments at multiple horizons demonstrate that the Adaptive LASSO-MGARCH model consistently achieves lower covariance forecast losses, and statistical tests based on the White reality check confirm that these improvements are significant across alternative loss functions.

Keywords: Adaptive LASSO; Multivariate GARCH; Volatility Forecasting; High-Dimensional; Green Finance (search for similar items in EconPapers)
JEL-codes: C32 C58 G17 (search for similar items in EconPapers)
Pages: 26 pages
Date: 2026-03
New Economics Papers: this item is included in nep-ets
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Journal Article: Adaptive LASSO-MGARCH for Multivariate Volatility Forecasting (2026)
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Persistent link: https://EconPapers.repec.org/RePEc:cdf:wpaper:2026/4

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