Measuring systematic risk with neural network factor model

DOI10.1016/j.physa.2019.123387OpenAlexW2986336750WikidataQ126845082 ScholiaQ126845082MaRDI QIDQ2137662

Jeonggyu Huh

Publication date: 16 May 2022

Published in: Physica A (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1809.04925

zbMATH Keywords

neural networkBayesian statisticsnonparametric modelsystematic risk

Mathematics Subject Classification ID

Statistical mechanics, structure of matter (82-XX)

Uses Software

• JAGS
• Stan
• word2vec
• Adam

Cites Work

• Comparing stochastic volatility models through Monte Carlo simulations
• Using neural networks to forecast the systematic risk of stocks
• The dynamics of stochastic volatility: evidence from underlying and options markets
• Alternative models for stock price dynamics.
• Multiscale Stochastic Volatility for Equity, Interest Rate, and Credit Derivatives
• Transform Analysis and Asset Pricing for Affine Jump-diffusions
• Deep learning for finance: deep portfolios
• CORRELATION UNDER STRESS IN NORMAL VARIANCE MIXTURE MODELS
• A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options
• Approximation by superpositions of a sigmoidal function
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