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외환시장의 변동성과 거래량의 관계 분석: 충격정보 확률변동성 모형 이용

A Study on the Relationship between Volatility and Trading Volumes Using a Surprising-Information- Stochastic-Volatility(SISV) Model

박범조
한국은행|2008.12|경제분석 (Economic Analysis)|제14권 제4호|pp. 47-84|DOI 10.23299/bokeri.2008.14.4.002

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참고문헌

키워드

Quantile regression실현 변동성거래량Trading volume분위수 회귀Realized volatility충격정보 확률변동성 모형수정된 혼합분포가설(MDH)마코프 체인 몬테칼로(MCMC) 알고리즘Surprising-information-stochastic-volatility(SISV) modelMarkov chain Monte Carlo(MCMC) methodModified MDH(mixture of distribution hypothesis)High frequency data of Won/Dollar exchange rates
주제분류경제학

초록

충격정보가 자산시장의 변동성과 거래량의 관계에 미치는 영향이 일반정보와 다르다는 수정된 혼합분포가설(MDH)(박범조, 2007)에 기초하여 본 연구는 충격정보의 유입에 따라 변동성의 상태(states)가 변함으로써 발생되는 두 변수의 비선형적 관계를 동태적으로 고려하기 위한 충격정보 확률변동성(surprising-information-stochastic volatility: SISV) 모형을 새롭게 제안하였다. 이 모형을 추정하기 위해 효율적인 베이지언 추정법인 마코프 체인 몬테칼로(MCMC) 알고리즘을 적용하고 고빈도 원/달러 환율 자료와 일별 거래량 자료를 이용하여 실증분석을 수행하였다. 거래량 변수만을 포함한 단변량 확률변동성 모형을 이용한 분석결과에 의하면 GARCH 유형의 모형을 이용한 일반적 연구결과와 다르게 변동성과 거래량이 통계적으로 유의한 관계를 갖지 않았다. 하지만 충격정보를 고려할 경우 유의한 관계를 가지게 되며 변동성의 지속성도 현격히 감소하였다. 한편 동시편의(simultaneity bias) 문제 없이 두 변수의 관계를 동태적으로 분석할 수 있는 이변량 SISV 모형에서도 유사한 결과를 보여줄 뿐만 아니라 충격정보가 유입되는 경우 수익률의 변동성은 통계적으로 유의하게 증가되지만 거래량의 변동성은 유의하게 변화되지 않는다는 흥미로운 사실을 보여주었다. 이런 실증분석 결과들은 수정된 혼합분포가설과 일치하며 변동성 모형에 충격정보를 고려하지 않는 경우 심각한 모형설정 오류가 발생할 수 있음을 암시한다.

Abstract

Modifying the MDH(mixture of distribution hypothesis) theory, Park(2007) showed that the effect of ‘surprising information’ on the relationship between volatility and trading volumes contrasts with that of general information. On the basis of his study, this paper proposes surprising-information-stochastic- volatility(SISV) model to capture their nonlinear relationship that is caused by the state change of volatility due to the surprising information flow. To estimate the SISV model efficiently this paper also suggests Markov chain Monte Carlo(MCMC) method. Strong evidence in favor of SISV model over the standard stochastic volatility model is based on empirical application with high frequency data of Won/Dollar exchange rates. Interestingly, while their positive relationship is not significant in the stochastic volatility model with a volume variable, it becomes significant and the persistence of volatility is remarkably reduced in the SISV model. According to the estimation results of the bivariate SISV model, furthermore, the surprising information flow increases the volatility of returns highly, whereas it little changes the volatility of trading volume. These empirical findings are consistent with the modified MDH and imply that ignoring the feature of surprising information can lead to a model misspecification.

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