ReSGA, a large autoencoder, outperforms prior methods on joint VaR-ES forecasting for US equities and converts the edge into economic gains via a size-enhanced momentum strategy, with gains attributed to data complexity.
Hansen, Asger Lunde, and James M
5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
Benchmark of 15 time-series architectures on equity portfolios finds no model dominates, with TransEnc-8 at 0.352 rank-1 acceptability and all promoted models showing negative net Sharpe at 20 bps costs under constraints.
Matrix-HAR model with multi-horizon lags and renewable generation inputs improves one-week forecasts of realized covariation and spread risk premia versus standard backward-looking volatility methods in electricity markets.
Proposes fMSV framework using factor decomposition, two-stage estimation, and derived asymptotics for high-dimensional multivariate stochastic volatility, tested via simulations and portfolio applications.
A literature survey finds no peer-reviewed Bitcoin price models beat the naive baseline at medium horizons and proposes methodological improvements including walk-forward testing and Diebold-Mariano tests.
citing papers explorer
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ReSGA: A Large Tail Risk Model for Learning Value-at-Risk and Expected Shortfall
ReSGA, a large autoencoder, outperforms prior methods on joint VaR-ES forecasting for US equities and converts the edge into economic gains via a size-enhanced momentum strategy, with gains attributed to data complexity.
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Benchmarking Deep Time Series Models for Equity Portfolios
Benchmark of 15 time-series architectures on equity portfolios finds no model dominates, with TransEnc-8 at 0.352 rank-1 acceptability and all promoted models showing negative net Sharpe at 20 bps costs under constraints.
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Forecasting of volatility and risk premia in electricity markets
Matrix-HAR model with multi-horizon lags and renewable generation inputs improves one-week forecasts of realized covariation and spread risk premia versus standard backward-looking volatility methods in electricity markets.
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Factor multivariate stochastic volatility models of high dimension
Proposes fMSV framework using factor decomposition, two-stage estimation, and derived asymptotics for high-dimensional multivariate stochastic volatility, tested via simulations and portfolio applications.
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Bitcoin Price Prediction: Peer-Reviewed Evidence and Social Media Discourse
A literature survey finds no peer-reviewed Bitcoin price models beat the naive baseline at medium horizons and proposes methodological improvements including walk-forward testing and Diebold-Mariano tests.