Risk-sensitive routing of volatility forecasting specialists reduces high-volatility forecast loss by 24% and underprediction loss by 22% versus a rolling-best baseline on six ETFs.
A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix,
2 Pith papers cite this work. Polarity classification is still indexing.
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EvoNash-MARL achieves 19.6% annualized returns on equity allocation from 2014-2024 versus 11.7% for SPY, with evidence of robustness under constraints but no strong statistical superiority per WRC and SPA-lite tests.
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Risk-Sensitive Specialist Routing for Volatility Forecasting
Risk-sensitive routing of volatility forecasting specialists reduces high-volatility forecast loss by 24% and underprediction loss by 22% versus a rolling-best baseline on six ETFs.
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EvoNash-MARL: A Closed-Loop Multi-Agent Reinforcement Learning Framework for Medium-Horizon Equity Allocation
EvoNash-MARL achieves 19.6% annualized returns on equity allocation from 2014-2024 versus 11.7% for SPY, with evidence of robustness under constraints but no strong statistical superiority per WRC and SPA-lite tests.