The Gold Standard of Robust Mixed-Frequency Portfolio Optimization? A MIDAS-CVaR Application for Renewable Asset Investments

Abstract

Integrating high‑frequency market signals with mixed‑frequency explanatory variables is essential for accurate tail risk modeling in traditionally volatile renewable energy portfolios. Characteristics heavy‑tailed return distributions and exogenous variable influence on historical prices challenge a traditional mean‑variance optimization approach. Analyzing 13 energy assets and 12 explanatory variables from 2015 to 2024 demonstrates Conditional Value-at-Risk (CVaR) optimization's superiority over traditional Markowitz frameworks in such settings. Both historical simulation and Monte Carlo analyzes reveal CVaR portfolios' superior performance across a spectrum of market conditions, establishing CVaR as the study's preferred risk measure. A Forward Search implementation enhances CVaR estimation by mitigating outlier influence while preserving essential tail information, delivering substantial risk reduction without increasing portfolio concentration. Building upon these findings, this study proposes a novel Log-Component MIDAS framework with exogenous variables (LC-MIDAS-X) that decomposes volatility into high-frequency market components and low-frequency policy/macroeconomic drivers. The LC-MIDAS-X model integrates with CVaR estimation, and empirical validation confirms the model produces accurate CVaR forecasts for renewable energy assets, with further improvements in downside risk protection. The model reveals significant differences in volatility response patterns across renewable subsectors, providing insights into how solar, wind, and infrastructure assets respond to changing economic and regulatory conditions. Portfolio construction leverages enhanced CVaR estimates to demonstrate the potential for improved risk-adjusted performance while maintaining strategic exposure to clean energy opportunities. Acknowledging that our study is limited by the absence of extensive robustness checks and an arbitrary selection of assets and variables, these constraints may temper broader generalizations. However, the findings provide valuable insights for refining mixed‑frequency tail‑risk forecasting and portfolio optimization methodologies within the renewable space.