• 제목/요약/키워드: Bivariate GARCH-BEKK Model

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국채선물과 현물시장의 이변량 변동성 추정에 관한 연구 (Estimating the Volatility in KTB Spot and Futures Markets)

  • 장국현;윤병조;조영석
    • 재무관리연구
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    • 제21권2호
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    • pp.183-209
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    • 2004
  • 본 연구에서는 금리변동에 따른 헤지수단의 목적으로 도입된 국채선물과 해당 기초자산인 국채현물의 일별 자료를 통해 두 시계열의 상호관계를 변동성에 초점을 두고 Bivariate GARCH 모형인 BEKK 모형과 국면전환 및 백터 오차수정항이 포함된 Bivariate-AR(1)-Markov-Switching-VECM 모형을 이용하여 비교 분석하였다. 본 연구의 분석기간은 2000년 1월 4일부터 2003년 10월 30일까지이며 분석대상은 일별 국채현물지수와 국채선물지수 935 관측치 이다. 본 연구의 결과 우리나라에서 국채선물과 현물시장의 분석에 있어서 두 시장을 한꺼번에 아우를 수 있는 Bivariate 모형설정의 중요성이 강하게 대두되었다. 특히 본 연구의 분석기간 중에는 국채시장의 상승국면과 하락국면이라는 두 상태보다는 국채가격의 변동성국면이 훨씬 더 강하게 국채시장에 작용하고 있음이 밝혀졌다. 이는 투자자가 보다 나은 헷징결과를 기대한다면 국채시장의 분석시 현물과 선물, 각각의 분산과정뿐만 아니라 공분산과정도 반드시 시계열모형내에서 동시에 고려해야함을 시사하고 있다.

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Multivariate GARCH and Its Application to Bivariate Time Series

  • Choi, M.S.;Park, J.A.;Hwang, S.Y.
    • Journal of the Korean Data and Information Science Society
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    • 제18권4호
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    • pp.915-925
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    • 2007
  • Multivariate GARCH has been useful to model dynamic relationships between volatilities arising from each component series of multivariate time series. Methodologies including EWMA(Exponentially weighted moving-average model), DVEC(Diagonal VEC model), BEKK and CCC(Constant conditional correlation model) models are comparatively reviewed for bivariate time series. In addition, these models are applied to evaluate VaR(Value at Risk) and to construct joint prediction region. To illustrate, bivariate stock prices data consisting of Samsung Electronics and LG Electronics are analysed.

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소비 대체 양식어종 간의 가격 인과성과 변동성 전이에 관한 연구 (The Causality and Volatility Spillover between Farming fish Species in Consumption Replacement Relation)

  • 강석규
    • 수산경영론집
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    • 제46권3호
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    • pp.119-127
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    • 2015
  • This study is to analyse the causality and volatility spillover between farming fish species in consumption replacement relation using flatfish(oliver flounder) and rockfish's wholesale market price data from September 2006 to July 2015. For the analysis, VAR(5) model and bivariate asymmetric GARCH-BEKK model are employed. The empirical results of this study are summarized as follows: First, the price volatility of flatfish and rockfish is very large without the trend during the sample period. Second, the correlation coefficient between flatfish and rockfish wholesale markets has positive 0.1059 value. Third, causality relation is unidirectional from rockfish market to flatfish market. Fourth, conditional volatility spillover effect is unidirectional from rockfish market to flatfish market, but asymmetric volatility effect is bidirectional between flatfish and rockfish markets that implies the bad news arising from flatfish wholesale market impact on rockfish market's volatility and the bad news arising from rockfish wholesale market impact on flatfish market's volaltilty. Consequently, based on the thus results, the volatility spillover effect interacts and is bidirectional between flatfish and rockfish wholesale markets.

Information Transmission of Volatility between WTI and Brent Crude Oil Markets

  • Kang, Sang Hoon;Yoon, Seong-Min
    • 자원ㆍ환경경제연구
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    • 제22권4호
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    • pp.671-689
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    • 2013
  • Transmission mechanisms of volatility between two crude oil markets (WTI and Brent markets) have drawn the attention of numerous academics and practitioners because they both play crucial roles in portfolio and risk management in crude oil markets. In this context, we examined the volatility linkages between two representative crude oil markets using a VECM and an asymmetric bivariate GARCH model. First, looking at the return transmission through the VECM test, we found a long-run equilibrium and bidirectional relationship between two crude oil markets. However, the estimation results of the GARCH-BEKK model suggest that there is unidirectional volatility spillover from the WTI market to the Brent market, implying that the WTI market tends to exert influence over the Brent market and not vice versa. Regarding asymmetric volatility transmission, we also found that bad news volatility in the WTI market increases the volatility of the Brent market. Thus, WTI information is transmitted into the Brent market, indicating that the prices of the WTI market seem to lead the prices of the Brent market.