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.
top of page
Rechercher
Posts récents
Voir toutThe paper examines climate litigation's growing impact on banks, noting limited current effects but a projected increase. Key risks...
Effective risk management requires understanding aggregate risks, individual business unit riskiness, and systemic risks. Realistic...
The paper explains Artificial Intelligence (AI), focusing on Generative AI, its role in finance, and its differences from Machine...
bottom of page
Commentaires