From Regime Detection to Decision Rules: A Data-Driven Macro-Financial CVaR Framework for European Multi-Asset Portfolios

Abstract

Weekly macro-financial and financial market data, combined with machine learning methods, offer new possibilities for identifying latent economic states in real time, but the portfolio value of regime detection depends critically on how detected states are translated into allocation rules. This paper develops and evaluates a data-driven macro-financial framework that combines a four-state Gaussian Hidden Markov Model (HMM), estimated on eight weekly macro-financial features, with Conditional Value-at-Risk (CVaR) portfolio optimization across European multi-asset portfolios from January 2000 to April 2026. Using a strictly out-of-sample walk-forward design, we show that naive regime-conditional CVaR allocation generates excessive turnover (approximately 226% per year) that erodes net performance below a simple benchmark under any realistic transaction cost, whereas implementation-aware alternatives recover the gap substantially: regime-constrained weight bands attain a net Sharpe ratio within 0.009 of the static benchmark at roughly 29% annual turnover. Expanding the universe to include sovereign bonds improves drawdown control but introduces duration risk that materializes in rate-hiking episodes. These findings demonstrate that, in data-driven macro-financial systems, the bottleneck is not regime detection but transparent, stable, and cost-aware decision-rule design, with implications for next-generation, AI-assisted macro-financial monitoring and policy surveillance systems.