Estimation of a Portfolio's Value-at-Risk Using Variational Auto-Encoders, M.Sc. Thesis Sharif University of Technology ; Arian, Hamidreza (Supervisor) ; Talebian, Masoud (Supervisor)
Abstract
One of the most crucial aspects of financial risk management is risk measurement. Advanced AI-based solutions can provide the proper tools for assessing global markets, given the complexity of the global economy and the violation of typical modeling assumptions. A new strategy for quantifying stock portfolio risk based on one of the machine learning models known as Variational Autoencoders is provided in this dissertation. The suggested method is a generative model that can learn the stocks' dependency structure without relying on assumptions about stock return covariance and produce various market scenarios using cross-sectional stock return data with a higher signal-to-noise ratio. We...
Cataloging briefEstimation of a Portfolio's Value-at-Risk Using Variational Auto-Encoders, M.Sc. Thesis Sharif University of Technology ; Arian, Hamidreza (Supervisor) ; Talebian, Masoud (Supervisor)
Abstract
One of the most crucial aspects of financial risk management is risk measurement. Advanced AI-based solutions can provide the proper tools for assessing global markets, given the complexity of the global economy and the violation of typical modeling assumptions. A new strategy for quantifying stock portfolio risk based on one of the machine learning models known as Variational Autoencoders is provided in this dissertation. The suggested method is a generative model that can learn the stocks' dependency structure without relying on assumptions about stock return covariance and produce various market scenarios using cross-sectional stock return data with a higher signal-to-noise ratio. We...
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