비대칭-비정상 변동성 모형 평가를 위한 모수적-붓스트랩Asymmetric and non-stationary GARCH($1,1$) models: parametric bootstrap to evaluate forecasting performance
- Other Titles
- Asymmetric and non-stationary GARCH($1,1$) models: parametric bootstrap to evaluate forecasting performance
- Authors
- 황선영; 최선우; 윤재은; 이성덕
- Issue Date
- Aug-2021
- Publisher
- 한국통계학회
- Keywords
- asymmetric volatility; non-stationary volatility; parametric bootstrap; 비대칭 변동성; 비정상 변동성; 모수적 붓스트랩
- Citation
- 응용통계연구, v.34, no.4, pp 611 - 622
- Pages
- 12
- Journal Title
- 응용통계연구
- Volume
- 34
- Number
- 4
- Start Page
- 611
- End Page
- 622
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/146452
- DOI
- 10.5351/KJAS.2021.34.4.611
- ISSN
- 1225-066X
2383-5818
- Abstract
- 본 논문에서는 변동성의 비대칭성과 비정상성을 동시에 고려하고 있다. 다양한 변동성 모형을 분석하고 있으며 모수적-붓스트랩을 통한 예측분포를 이용하여 변동성 모형의 예측 성능을 비교하고 있다. 오차항 분포로서 표준정규분포 및 표준화 t-분포를 고려하였으며 1-시차 후 예측과 2-시차 후 예측을 미국의 다우지수 사례를 통해 설명하였다.
With a wide recognition that financial time series typically exhibits asymmetry patterns in volatility so called leverage effects, various asymmetric GARCH(1, 1) processes have been introduced to investigate asymmetric volatilities. A lot of researches have also been directed to non-stationary volatilities to deal with frequent high ups and downs in financial time series. This article is concerned with both asymmetric and non-stationary GARCH-type models. As a subsequent paper of Choi et al. (2020), we review various asymmetric and non-stationary GARCH(1, 1) processes, and in turn propose how to compare competing models using a parametric bootstrap methodology. As an illustration, Dow Jones Industrial Average (DJIA) is analyzed.
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