A0757
Title: Nonparametric modelling of EUA market returns, volatility, and financial market links: A data-driven approach
Authors: Cristiano Salvagnin - University of Brescia (Italy) [presenting]
Aldo Glielmo - Bank of Italy (Italy)
Maria Elena De Giuli - University of Pavia (Italy)
Antonietta Mira - University of Lugano (Switzerland)
Abstract: An exhaustive analysis of volatility dynamics in the European Union Emissions Trading System (EU ETS) market is conducted using a nonparametric framework enhanced by differentiable information imbalance (DII). By incorporating time-varying information imbalances, the primary drivers of volatility are revealed, and complex patterns such as volatility clusters and dependence structures are captured. The approach leverages DII's flexibility and adaptability to effectively model the intricate relationships in financial data. Applying the methodology to the financial returns and realized volatility of the EU ETS market, key volatility determinants are identified, including commodities, energy indices, exchange rates, uncertainty indicators, and macroeconomic factors. Findings provide deep insights into the nature of volatility within the EU ETS market, offering actionable intelligence for market participants, policymakers, and researchers. Specifically, it is found that the EU ETS market exhibits a strong one-way connectedness with commodities in terms of financial returns. When analyzing realized volatility, energy indices display significant connectedness and spillover effects, impacting commodities with high relevance. The newly developed methodology, focused on data dispersion into space and data manifold, provides novel insights and advanced tools for understanding non-linear relationships in a nonparametric framework.
