Luigi Grossi

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.

Climate change has emerged as a significant global concern, attracting increasing attention worldwide. While green bubbles may be examined through a social bubble hypothesis, it is essential not to neglect a Climate Minsky moment triggered by sudden asset price changes. The significant increase in green investments highlights the urgent need for a comprehensive understanding of these market dynamics. Therefore, the current paper introduces a novel paradigm for studying such phenomena. Focusing on the renewable energy sector, Statistical Process Control (SPC) methodologies are employed to identify green bubbles within time series data. Furthermore, search volume indexes and social factors are incorporated into established econometric models to reveal potential implications for the financial system. Inspired by Joseph Schumpeter's perspectives on business cycles, this study recognizes green bubbles as a necessary evil for facilitating a successful transition towards a more sustainable future.

Operating on electricity markets requires accurately identifying, quantifying, and measuring risk coupled with their corresponding return: this appears as a crucial point, particularly during and after the COVID-19 pandemic. The aim of the present paper is twofold. First, we propose a novel econometric approach to identifying relevant market factors that capture several elements of the risk transmission mechanism inherent in energy systems. The proposed model extends Bayesian graphical models with change points to a multiple-layer set-up. Multilayer graphs encompass the two relevant channels of shock transmission: volatility and price contagion effects. The choice of these two layers seems natural because electricity prices and their spiky nature, coupled with inherent volatility, constitute essential influential elements for market players to maximize their profits. The change-point specification allows for detecting relevant changes in the electricity market. Second, we apply the proposed econometric framework to the Italian zonal markets analyzing the effects of returns and volatility contagion in several periods detected by the model. The last time intervals identified by the change-point methodology overlap the COVID-19 pandemic period. The model captures relevant abrupt changes in prices and volatility in the zonal electricity market and provides new evidence of interconnections in the zones of the Italian market related to the risk alone, price process alone, and risk versus price process relationship and their interactions.