원문정보
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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
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
저자정보
참고문헌
자료제공 : 네이버학술정보