LOG IN창 닫기

  • 회원님의 아이디와 패스워드를 입력해 주세요.
  • 회원이 아니시면 아래 [회원가입]을 눌러 회원가입을 해주시기 바랍니다.

아이디 저장

   

아이디 중복검사창 닫기

HONGGIDONG
사용 가능한 회원 아이디 입니다.

E-mail 중복확인창 닫기

honggildong@naver.com
사용 가능한 E-mail 주소 입니다.

우편번호 검색창 닫기

검색

SEARCH창 닫기

비밀번호 찾기

아이디

성명

E-mail

학술자료 검색

Value-at-Risk Analysis of the Long Memory Volatility Process:The Case of Individual Stock Returns

  • Sang Hoon Kang School of Commerce, University of South Australia.
  • Seong-Min Yoon Department of Economics, Pusan National University
This article investigated the relevance of the skewed Student-t distribution innovation in analyzing volatility stylized facts, namely, volatility clustering, volatility asymmetry, and volatility persistence, in three individual Korean shares. For this purpose, we assessed the performance of RiskMetrics and two long memory Value-at-Risk (VaR) models (FIGARCH and FIAPARCH) with the normal, Student-t, and skewed Student-t distribution innovations. From the results of the empirical VaR analysis, the skewed Student-t distribution innovation provided more accurate VaR calculations, in capturing stylized facts in the volatility of three sample returns. Thus, the correct assumption of return distribution might improve the estimated performance of VaR models in the Korean stock market.

  • Sang Hoon Kang
  • Seong-Min Yoon
This article investigated the relevance of the skewed Student-t distribution innovation in analyzing volatility stylized facts, namely, volatility clustering, volatility asymmetry, and volatility persistence, in three individual Korean shares. For this purpose, we assessed the performance of RiskMetrics and two long memory Value-at-Risk (VaR) models (FIGARCH and FIAPARCH) with the normal, Student-t, and skewed Student-t distribution innovations. From the results of the empirical VaR analysis, the skewed Student-t distribution innovation provided more accurate VaR calculations, in capturing stylized facts in the volatility of three sample returns. Thus, the correct assumption of return distribution might improve the estimated performance of VaR models in the Korean stock market.
Value-at-Risk (VaR); Long Memory; Asymmetry; Fat Tails; Rescaled Range (R/S) Analysis