A0298
Title: Evaluating clean energy's impact on forecasting probability distribution in the energy market
Authors: Laura Garcia-Jorcano - Universidad de Castilla-La Mancha (Spain) [presenting]
Lidia Sanchis-Marco - University of Castilla-La Mancha (Spain)
Abstract: Loss distribution prediction is investigated in the energy sector of the S\&P 500 using two distinct modeling approaches: The standard SAV-CAViaR model and an extended version that incorporates the Wilder-Hill clean energy index (ECO) as a proxy for the clean energy transition. Through an analysis of the distributions of daily returns for the standard and extended models, the aim is to evaluate these models' predictive power across various quantile levels ranging from 0.01 to 0.99. A comprehensive analysis of the obtained distributions is provided, including statistical moments, and assesses the out-of-sample predictive performance of the clean energy variable. New insights are offered into the impact of clean energy transitions on financial risk, underscoring the importance of integrating clean energy variables into financial risk models. It demonstrates that these variables significantly impact predictive accuracy and inform sustainable investment strategies.
