응용통계연구 (The Korean Journal of Applied Statistics)

  • 제34권4호
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  • Pages.611-622
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  • 2021
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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비대칭-비정상 변동성 모형 평가를 위한 모수적-붓스트랩

Asymmetric and non-stationary GARCH(1, 1) models: parametric bootstrap to evaluate forecasting performance

  • 최선우 (숙명여자대학교 통계학과) ;
  • 윤재은 (숙명여자대학교 통계학과) ;
  • 이성덕 (충북대학교 정보통계학과) ;
  • 황선영 (숙명여자대학교 통계학과)
  • Choi, Sun Woo (Department of Statistics, Sookmyung Women's University) ;
  • Yoon, Jae Eun (Department of Statistics, Sookmyung Women's University) ;
  • Lee, Sung Duck (Department of Information and Statistics, Chungbuk National University) ;
  • Hwang, Sun Young (Department of Statistics, Sookmyung Women's University)
  • 투고 : 2021.06.14
  • 심사 : 2021.06.29
  • 발행 : 2021.08.31
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⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

과제정보

This research was partially supported by a grant from the National Research Foundation of Korea (NRF-2021R1F1A1047952).

참고문헌

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