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تخمین ارزش در معرض ریسک سبد سهامی به وسیله‌ شبکه خودرمزنگار تغییراتی
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تخمین ارزش در معرض ریسک سبد سهامی به وسیله‌ شبکه خودرمزنگار تغییراتی

مقیمی، مهرداد Moghimi, Mehrdad

Estimation of a Portfolio's Value-at-Risk Using Variational Auto-Encoders

Moghimi, Mehrdad | 2021

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 54313 (44)
  4. University: Sharif University of Technology
  5. Department: Management and Economics
  6. Advisor(s): Arian, Hamidreza; Talebian, Masoud
  7. Abstract:
  8. 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 compare the proposed model's out-of-sample findings to those of twelve existing approaches, demonstrating that it is comparable with many of the well-known models for predicting the value at risk
  9. Keywords:
  10. Generative Models ; Variational Autoencoder ; Artificial Intelligence ; Machine Learning ; Dependence Structure ; Financial Risk Management ; Value at Risk ; Stock Portfolio ; Portfolio Generating Function

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