Earticle

초록

영어

This study aims to forecast Portfolio VaR using Noise-reduced Correlation Matrix based on Denoising Autoencoders, which helps anticipate extreme losses in the future. The dataset comprises the US stocks with daily adjusted closing prices. The portfolio weights for each asset were calculated by applying the Risk Parity method to the entire asset group using the Noise-reduced Correlation Matrix. Forecasting VaR through Delta-Normal, Historical, GARCH, and Denoising Autoencoders methods, and subsequently conducted Backtest VaR. Empirical analysis revealed that DAE-GARCH-VaR (Student’s T Distribution) exhibited a significant noise reduction effect on the correlation matrix and the superior performance.

목차

Abstract
Introduction
Background Research
Autoencoders
Denoising Autoencoders
Risk Parity Asset Allocation
Methodology
Delta-Normal VaR
GARCH-VaR (Normal Distribution)
Historical VaR
Denoising Autoencoders-VaR
Calculating the Portfolio VaR
Data
Data description
Data pre-processing
Results of the Empirical Analysis
Conclusion
References

키워드

저자

• Woogeon Kim [ MA candidate Department of Business Administration, Pusan National University ] First author
• Minhyuk Lee [ Assistant Professor Department of Business Administration, Pusan National University ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658