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A Study for Long memory GARCH Models

Title
A Study for Long memory GARCH Models
Authors
김혜미
Issue Date
2006
Department/Major
대학원 통계학과
Publisher
이화여자대학교 대학원
Degree
Master
Advisors
이외숙
Abstract
For the past twenty years, autoregressive conditionally heteroskedastic(ARCH) models, generalized autoregressive conditionally heteroskedastic(GARCH) model and their generalization have been developed and successfully applied in finance and macroeconomics. Even though there are many studies about GARCH-type models which have stationarity and long memory , several is-sues remain open. In this paper, we consider the FIGARCH model and MD-ARCH model. We show that the two models can share their solution and obtain regions on which the process satisfy stationarity and long memory.;과거 20여년동안 ARCH(autoregressive conditionally heteroskedastic)모형과 GARCH(generalized autoregressive conditionally heteroskedastic)모형 및 그의 일반화된 모형들은 성공적으로 재정학과 거시경제학에 적용되어 왔다. 안정성(stationarity)과 long memory를 동시에 갖는 GARCH-type모형에 관한 방대한 연구 결과들이 발표되었으나, 아직도 많은 문제들이 미해결 상태로 남아있다. 이 논문에서는 FIGARCH 모형과 MD-ARCH 모형을 상호 비교하면서 안정성과 long memory의 성질을 동시에 갖는 계수들의 조건을 보이겠다.
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