Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/339522 
Erscheinungsjahr: 
2026
Schriftenreihe/Nr.: 
Cardiff Economics Working Papers No. E2026/4
Verlag: 
Cardiff University, Cardiff Business School, Cardiff
Zusammenfassung: 
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.
Schlagwörter: 
Adaptive LASSO
Multivariate GARCH
Volatility Forecasting
High-Dimensional
Green Finance
JEL: 
C32
C58
G17
Dokumentart: 
Working Paper

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