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- W3125468173 abstract "Given a set of empirical observations, conditional density estimation aims to capture the statistical relationship between a conditional variable $mathbf{x}$ and a dependent variable $mathbf{y}$ by modeling their conditional probability $p(mathbf{y}|mathbf{x})$. The paper develops best practices for conditional density estimation for finance applications with neural networks, grounded on mathematical insights and empirical evaluations. In particular, we introduce a noise regularization and data normalization scheme, alleviating problems with over-fitting, initialization and hyper-parameter sensitivity of such estimators. We compare our proposed methodology with popular semi- and non-parametric density estimators, underpin its effectiveness in various benchmarks on simulated and Euro Stoxx 50 data and show its superior performance. Our methodology allows to obtain high-quality estimators for statistical expectations of higher moments, quantiles and non-linear return transformations, with very little assumptions about the return dynamic." @default.
- W3125468173 created "2021-02-01" @default.
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- W3125468173 date "2019-03-01" @default.
- W3125468173 modified "2023-10-01" @default.
- W3125468173 title "Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks" @default.
- W3125468173 hasPublicationYear "2019" @default.
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