Because of the non-linearity inherent in energy commodity prices, traditional mono-scale smoothing methodologies cannot accommodate their unique properties. From this viewpoint, we propose an extended mode decomposition method useful for the time-frequency analysis, which can adapt to various non-stationarity signals relevant for enhancing forecasting performance in the era of big data. To this extent, we employ variants of mode decomposition-based extreme learning machines namely: (i) Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-based ELM Model (CEEMDAN-ELM), (ii) Ensemble Empirical Mode Decomposition-based ELM Model (EEMD-ELM) and (iii) Empirical Mode Decomposition Based ELM Model (EMD-ELM), which cut-across soft computing and artificial intelligence to analyze multi-commodity time series data by decomposing them into seven independent intrinsic modes and one residual with varying frequencies that depict some interesting characterization of price volatility. Our findings show that in terms of the model-specific forecast accuracy measures different dynamics in the two scenarios namely the (non) COVID periods. However, the introduction of a benchmark, namely the autoregressive integrated moving average model (ARIMA) reveals a slight change in the earlier dynamics, where ARIMA outperform our proposed models in the Japan gas and the US gas markets. To check the superiority of our models, we apply the model-confidence set (MCS) and the Kolmogorov-Smirnov Predictive Ability test (KSPA) with more preference for the former in a multi-commodity framework, which reveals that in the pre-COVID era, CEEMDAN-ELM shows persistence and superiority in accurately forecasting Crude oil, Japan gas, and US gas. Nonetheless, this paradigm changed during the COVID-era, where CEEMDAN-ELM favored Japan gas, US gas, and coal market with different rankings via the Model confidence set evaluation methods. Overall, our numerical experiment indicates that all decomposition-based extreme learning machines are superior to the benchmark model.

Sozial verantwortliches Investieren (Socially Responsible Investing, SRI) gewinnt an den Finanzmärkten aus verschiedenen Gründen zunehmend an Bedeutung, beispielsweise aufgrund der sich abzeichnenden Auswirkungen des Klimawandels oder des Strebens nach einer Netto-Null-Wirtschaft. Bestehende SRI-Ansätze haben Umwelt-, Sozial- und Governancekriterien (ESG Kriterien) als weitere Dimension in die Portfolioauswahl einbezogen; diese Ansätze konzentrieren sich allerdings auf klassische Investoren und berücksichtigen nicht spezifische Aspekte von Versicherungsunternehmen. In unserem Beitrag betrachten wir das Problem der Aktienportfolioselektion von Lebensversicherungsunternehmen. Neben dem Aktienrisiko berücksichtigt unser Modell auch andere wichtige Marktrisikokategorien von Versicherern, nämlich das Zinsrisiko und das Kreditrisiko. In Übereinstimmung mit den üblichen Standards in der Solvabilitätsregulierung messen wir das Risiko anhand der Solvenzquote, d.h. anhand des Verhältnisses zwischen dem marktbasierten Eigenkapital des Versicherers und den Solvenzkapitalanforderungen aller modellierten Risikokategorien. Hierauf aufbauend verwenden wir eine Modifikation der Portfolioselektionstheorie nach Markowitz, indem wir die Solvenzquote als Maß für das Abwärtsrisiko nutzen, um mögliche optimale Portfolios in einer dreidimensionalen (Risiko, Rendite und ESG) Kapitalallokationsebene zu identifizieren. Wir stellen fest, dass bei einer gegebenen Solvenzquote Aktienportfolios mit einem moderaten ESG-Niveau zu einer höheren erwarteten Rendite führen können als solche mit einem niedrigen ESG-Niveau. Ein sehr ehrgeiziges ESG-Niveau verringert jedoch die erwartete Rendite. Aufgrund der Besonderheiten in ihrem Geschäftsmodell können die Auswirkungen des ESG-Niveaus auf die erwartete Rendite von Lebensversicherern erheblich von den entsprechenden Auswirkungen für klassische Investoren abweichen.

This paper presents the estimation methods of the Bayesian Graphical Vector Auto-regression with and without innovations such as external regressors (BG-VAR(X)) and Bayesian Graphical Systems Equation Modelling with and without exogenous variables (BG-SEM(X)), which are developed to examine risk network structures embedded in multivariate time series. This methodical approach allows for the analysis of various dynamics and persistence in the multivariate time series in terms of risk propagation. For instance, both the BG-SEMX and BG-VARX can reveal the within-day and across-day major risk transmitters as well as risk recipients from other univariate time series, which better explain risk contagion using complex network models. In addition, the procedures for models with and without exogenous variables have been explored, which shows that the former produce more network structures compared to the latter and therefore depict their influential role. This approach, therefore, provides a platform for future research in terms of extension of the method to encompass different types of multivariate data with additional innovations that might aid feasible analysis and the design of policy instruments and the implementation of relevant policy implications. • Development and application of innovative network models that enhances the efficient analysis of multivariate time series data. • Estimation of intra-day and inter-day interconnection from a daily multivariate time series data and their dynamics and persistence from contagion analysis viewpoint.

This paper examines downside hedging, tail diversification, and extreme tail risk transmission between crude oil and agricultural commodity futures over two decades. We develop complementary indicators of downside dependence, including sign‐switching frequency and persistence, to assess tail risk dynamics, hedging effectiveness, and associated costs. Unlike conventional network measures, these indicators capture the stability and duration of adverse market relationships. Using the extreme downside correlation (EDC) and extreme downside hedge (EDH) frameworks, together with rolling‐window CVaR‐based regressions and correlation estimates, we analyse the sensitivity of agricultural futures to adverse shocks from crude oil, natural gas, and a broad commodity market index. Results show that tail risk transmission intensifies during periods of financial and geopolitical stress. Among agricultural commodities, lean hogs, soybeans, and corn consistently exhibit strong downside hedging capabilities and tail diversification benefits, highlighting their value for portfolio risk management under extreme market conditions.