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        "result": "supported"
    },
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        "idx": 2,
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<reference>
[2606.09104] Addressing Market Regime Changes and Heavy-Tailed Returns in Portfolio Optimization via Bayesian VAR and Elliptical Black-Litterman

Abstract page for arXiv paper 2606.09104: Addressing Market Regime Changes and Heavy-Tailed Returns in Portfolio Optimization via Bayesian VAR and Elliptical Black-Litterman

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Computer Science > Machine Learning

arXiv:2606.09104
(cs)

[Submitted on 8 Jun 2026]

Title:
Addressing Market Regime Changes and Heavy-Tailed Returns in Portfolio Optimization via Bayesian VAR and Elliptical Black-Litterman

Authors:
Daniil Mikriukov
(1 and 2),
Ruoyu Sun
(2),
Angelos Stefanidis
(2),
Jionglong Su
(2),
Zhengyong Jiang
(2) ((1) University of Liverpool, (2) Xi'an Jiaotong-Liverpool University)

View a PDF of the paper titled Addressing Market Regime Changes and Heavy-Tailed Returns in Portfolio Optimization via Bayesian VAR and Elliptical Black-Litterman, by Daniil Mikriukov (1 and 2) and 5 other authors

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Abstract:
Deep reinforcement learning (DRL) frameworks for portfolio optimization have shown promise for their ability to learn allocation rules dynamically from market data. However, these models fail to account for fat-tailed returns, which characterize actual market behavior with more frequent extreme events. Furthermore, historical data is treated homogeneously, without accounting for temporal importance, leading models to fail during regime changes. We propose a new BAVAR-BLED algorithm that combines methods derived from Bayesian-Averaging Vector Autoregressive (BAVAR) and the Black-Litterman model using Elliptical Distributions (BLED) within a TD3 architecture. BAVAR captures a set of vector autoregressive representations that consider multi-scale temporal features, enabling adaptive allocation decisions based on regime-aware estimates of return expectations and dispersion matrices. These estimates serve as prior inputs to BLED, a model that uses Student's t-distributions, allowing for more realistic fat tail return estimates. The BAVAR-BLED algorithm uses transformer networks for view construction and CNNs for risk-aversion estimates, which modify dynamic allocation decisions based on market conditions. An evaluation of 29 Dow Jones Industrial Average constituents over a decade-long market period shows that BAVAR-BLED significantly outperforms state-of-the-art methods, achieving Sharpe and Sortino ratios of 1.72 and 2.70, respectively, and total returns of 57.26%.

Comments:

9 pages, 3 figures, 4 tables. Extends our prior work [Mikriukov et al., ICIC 2025] on Black-Litterman under Elliptical Distributions (BLED). Manuscript under review

Subjects:

Machine Learning (cs.LG)
; Artificial Intelligence (cs.AI); Portfolio Management (q-fin.PM)

ACM
classes:

I.2.6; I.2.8; G.3

Cite as:

arXiv:2606.09104
[cs.LG]

(or

arXiv:2606.09104v1
[cs.LG]
for this version)

https://doi.org/10.48550/arXiv.2606.09104

Focus to learn more

arXiv-issued DOI via DataCite

Submission history
From: Daniil Mikriukov [
view email
]

[v1]

Mon, 8 Jun 2026 06:58:11 UTC (277 KB)

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</reference>

<statements>
1. Furthermore, during structural regime shifts or financial liquidity crises that deviate from the training distribution, unanchored deep learning models can suffer catastrophic policy collapse
2. Deep Reinforcement Learning agents are particularly vulnerable to regime-shift failure
3. While modern deep learning and reinforcement learning models can capture complex non-linear dynamics, pure end-to-end neural allocators often struggle with out-of-distribution regime shifts, market frictions, and fiduciary interpretability requirements
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.