Encoded value-at-risk: a machine learning approach for portfolio risk measurement

DOI10.1016/J.MATCOM.2022.07.015OpenAlexW4288045340WikidataQ114149820 ScholiaQ114149820MaRDI QIDQ2168136

Hamid Arian, Shiva Zamani, Ehsan Tabatabaei, Mehrdad Moghimi

Publication date: 31 August 2022

Published in: Mathematics and Computers in Simulation (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2011.06742

zbMATH Keywords

artificial neural networksvalue-at-riskmachine learningfinancial risk managementvariational autoencoders

Mathematics Subject Classification ID

Operations research, mathematical programming (90-XX) Game theory, economics, finance, and other social and behavioral sciences (91-XX)

Related Items (1)

Addressing the economic and demographic complexity via a neural network approach: risk measures for reverse mortgages

Uses Software

• CAViaR
• Optuna
• CorrGAN

Cites Work

• Making and Evaluating Point Forecasts
• Stochastic \(L^1\)-optimal control via forward and backward sampling
• Sur la distribution limite du terme maximum d'une série aléatoire
• A class of invariant consistent tests for multivariate normality
• RANDOM MATRIX THEORY AND FINANCIAL CORRELATIONS
• Algorithms for solving high dimensional PDEs: from nonlinear Monte Carlo to machine learning
• An Introduction to Variational Autoencoders
• Neural Approximations for Optimal Control and Decision
• Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations and Forecast Rankings
• DISTRIBUTION OF EIGENVALUES FOR SOME SETS OF RANDOM MATRICES

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