Earticle

초록

영어

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.

목차

Abstract
 Ⅰ. Introduction
 Ⅱ. Methodology
  1. Long memory volatility models
  2. VaR models
  3. Tests of accuracy for VaR estimates
 Ⅲ. Empirical Results
  1. Preliminary Analysis of Data
  2. Long Memory and Asymmetry in Volatility
  3. Empirical Results for VaR Estimation
 Ⅳ. Conclusions
 References

키워드

저자

• Sang Hoon Kang [ School of Commerce, University of South Australia. ]
• Seong-Min Yoon [ Department of Economics, Pusan National University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  재무연구 [Asian Review of Financial Research]

• 간기

  계간

• pISSN

  1229-0351

• eISSN

  2713-6531

• 수록기간

  1988~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 325 DDC 330