Earticle

초록

영어

We propose a novel method to quantify the volatility clustering behavior observing in the ¯nancial time series generally. To create the solid results calculated by the proposed method in terms of the volatility clustering behavior, we used the international market indices of 14 countries. We ¯nd that regardless of used data sets, although the degree of volatility clustering of each country is di®erent, all data exhibits the volatility clustering properties, whereas those which eliminate the volatility clustering e®ect by the GARCH model reduce volatility clustering signi¯cantly. To test the usefulness of proposed method in this paper, we generates the arti¯cial time series by the GARCH(1,1) model with the coe±cients of original time series estimated by the GARCH(1,1) model. We also ¯nd that the degree of volatility clustering of arti¯cial data is very similar to those of the original time series. That is, we assert that this method can estimate the volatility clustering behavior in the ¯nancial markets.

목차

Abstract
 I. INTRODUCTION
 II. DATA AND METHODS
  A. DATA
  B. Method to calculate the volatility clustering
  C. Generalized Autoregressive Conditional Heteroscedasticity(GARCH)
 III. RESULTS
 IV. CONCLUSIONS

키워드

저자

• Gabjin Oh [ NCSL, Department of Physics, Pohang University of Science and Technology, Asia Paci¯c Center for Theoretical Physics, Pohang, Gyeongbuk, 790-784, Korea ]
• Seunghwan Kim [ NCSL, Department of Physics, Pohang University of Science and Technology, Asia Paci¯c Center for Theoretical Physics, Pohang, Gyeongbuk, 790-784, Korea ]
• Cheoljun Eom [ Division of Business Administration, Pusan National University, Busan 609-735, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국재무학회 학술대회

• 간기

  부정기

• 수록기간

  2006~2024

• 십진분류

  KDC 325 DDC 330