GARCH, EGARCH 模型을 이용한 住宅價格 變動性에 관한 硏究

• 영문명: A Study on the Volatility of Housing Price Using GARCH, EGARCH Model
• 발행기관: 한국부동산학회
• 저자명: 김종호(Kim, Jong Ho) 정재호(Chung, Jae Ho)
• 간행물 정보: 『부동산학보』不動産學報 第47輯, 367~383쪽, 전체 17쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2011.12.23

영문 초록

1. CONTENTS (1) RESEARCH OBJECTIVES This paper examines the volatility of housing price and leverage effects in korea housing markets, and documents the existence of price change and price volatility effect from Seoul housing market to local markets. (2) RESEARCH METHOD The availability of volatility model to the housing price index data was used to inspect the result. Considering the volatility in time series models to examine the feasibility of the Jarque-Bera test statistc, the null hypothesis was dismissed. Therefore ARCH flow model was applied to perform this paper. So, we used the GARCH(Generalized Autoregressive Conditional Heteroekedasticity) model and the EGARCH(Exponential GARCH) model to inspect the result. (3) RESEARCH RESULTS The results of GARCH, EGARCH model are as follows. Analysis of sale prices, GARCH(1.1) model estimation results from the mostly time series except Kwangju with the presence of ARCH effects, and except Incheon with the presence of GARCH effects, and the influence of fluctuations was expected to last for a long time. Estimated EGARCH(1.1) model results showed the presence of ARCH effects and leverage effects. Analysis of rental prices, GARCH(1.1) model estimation results from the mostly time series except Seoul with the presence of ARCH effects, and except Ulsan with the presence of GARCH effects, and the influence of fluctuations was expected to last for a long time. Estimated EGARCH(1.1) model results showed the presence of ARCH effects and leverage effects. 2. RESULTS First, we find that EGARCH(1.1) model is superior to GARCH(1.1) model in sale onces. GARCH(1.1) model is superior to EGARCH(1.1) model in rental prices, and news shock breaks out asymmetric volatility effect and leverage effect. Second, we demonstrate that price change and price volatility are spreaded from Seoul housing market to local markets. If the change of housing price of Seoul is 1%, the change of Inch eon is 0.31%, Daejeon is 0.14%, Daegu is 0.3%, Susan is 0.23%, Ulsan is 0.16%, Kwangju is 0.06%. This result is of help to forecast the housing price of local large city.

목차

ABSTRACT

Ⅰ. 序論

Ⅱ. 理論的 考察

Ⅲ. GARCH, EGARCH 模型

Ⅳ. 模型 推定 結果

Ⅴ. 結論

參考文獻

키워드

주택가격 변동성 레버리지효과 이전효과 지역시장 Housing Price Volatility Leverage Effect Spillover Effect Local Market