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  • Photo du rédacteurHélène Dufour

Deep Generative Modeling for Financial Time Series with Application in VaR: A Comparative Review

This paper explores risk factor distribution forecasting in finance, focusing on the widely used Historical Simulation (HS) model. It applies various deep generative methods for conditional time series generation and proposes new techniques. Evaluation metrics cover distribution distance, autocorrelation, and backtesting. The study reveals HS, GARCH, and CWGAN as top-performing models, with potential future research directions discussed.

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Bayesian Adaptive Sparse Copula

This paper introduces a multivariate sparse multiscale Bernstein polynomial model for copula dependence structures, utilizing a Bayesian spike-and-slab prior. The method enhances efficiency by preserv


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