Paper: arXiv 2510.10807

Authors: Ali Atiah Alzahrani

Abstract

We examine whether regime-conditioned generative scenarios combined with a convex CVaR allocator improve portfolio decisions under regime shifts. We present MARCD, a generative-to-decision framework with: (i) a Gaussian HMM to infer latent regimes; (ii) a diffusion generator that produces regime-conditioned scenarios; (iii) signal extraction via blended, shrunk moments; and (iv) a governed CVaR epigraph quadratic program. Contributions: Within the Scenario stage we introduce a tail-weighted diffusion objective that up-weights low-quantile outcomes relevant for drawdowns and a regime-expert (MoE) denoiser whose gate increases with crisis posteriors; both are evaluated end-to-end through the allocator. Under strict walk-forward on liquid multi-asset ETFs (2005-2025), MARCD exhibits stronger scenario calibration and materially smaller drawdowns: MaxDD 9.3% versus 14.1% for BL (a 34% reduction) over 2020-2025 out-of-sample. The framework provides an auditable pipeline with explicit budget, box, and turnover constraints, demonstrating the value of decision-aware generative modeling in finance.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 8.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced mathematics including diffusion models, Gaussian HMMs, mixture-of-experts denoisers, and convex optimization with CVaR epigraph QP, supported by theoretical theorems and proofs. Empirically, it demonstrates rigorous walk-forward backtesting on real ETF data (2005-2025) with explicit constraints, transaction costs, and detailed out-of-sample metrics, showing a 34% reduction in max drawdown.

Research Flowchart

  flowchart TD
  A["Research Goal: Assess Regime-Conditioned Generative Models for CVaR-Constrained Portfolios under Shifts"] --> B["Data: Liquid Multi-Asset ETFs (2005-2025)"]

  subgraph C ["MARCD Methodology"]
      C1["Gaussian HMM<br>Latent Regime Inference"]
      C2["Diffusion Generator<br>Tail-Weighted &<br>Regime-Conditioned Scenarios"]
      C3["Signal Extraction<br>Blended & Shrunk Moments"]
      C4["CVaR Epigraph QP<br>Explicit Constraints"]
  end

  B --> C1
  C1 --> C2
  C2 --> C3
  C3 --> C4

  C4 --> D["Walk-Forward Validation<br>Out-of-Sample: 2020-2025"]

  D --> E["Key Findings"]
  E --> E1["Superior Scenario Calibration"]
  E --> E2["Max Drawdown: 9.3% (vs. 14.1% for BL)"]
  E --> E3["34% Reduction in Max Drawdown"]
  E --> E4["Validated Decision-Aware Generative Modeling"]