인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
논문을 무제한 열람 이용할 수 있어요.
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
This paper examines the volatility of the prices of fisheries. This research shows empirical evidence there exist GARCH effects on the prices of fisheries that this study concerns. This study investigates the price volatility of mackerel, scabbard fish, cuttlefish, and pollack.
As the results, there are GARCH effect on the prices volatility all of the fisheries in this paper at 5% level of significance. Furthermore, it shows volatility clustering that they are sometimes volatile but sometimes tranquil. Conditional volatilities are estimated using GARCH(1,1)-t model. Past shock remains in the conditional volatility persistently in mackerel as 0.65, but weakly in scabbard fish, cuttlefish, and pollack. Persistency parameter, λ(=α₁+β₁), which is maintained in future with similar volatility of present, are very strong in all of the fisheries in this paper.
These quantitative information can be usefully utilized to establish forecasting model for the price of fisheries and to estimate effect for the price stabilization polices of government.
As the results, there are GARCH effect on the prices volatility all of the fisheries in this paper at 5% level of significance. Furthermore, it shows volatility clustering that they are sometimes volatile but sometimes tranquil. Conditional volatilities are estimated using GARCH(1,1)-t model. Past shock remains in the conditional volatility persistently in mackerel as 0.65, but weakly in scabbard fish, cuttlefish, and pollack. Persistency parameter, λ(=α₁+β₁), which is maintained in future with similar volatility of present, are very strong in all of the fisheries in this paper.
These quantitative information can be usefully utilized to establish forecasting model for the price of fisheries and to estimate effect for the price stabilization polices of government.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
최근 본 자료 전체보기
UCI(KEPA) : I410-ECN-0101-2013-454-001648010