본 연구는 기대하지 못하였으나 중요하기 때문에 금융시장에 막대한 영향 ...

본 연구는 기대하지 못하였으나 중요하기 때문에 금융시장에 막대한 영향을 미치게 되는 정보를 충격정보로 정의하여 일반정보와 구별한다. 그리고 혼합분포가설 (MDH)의 수정을 통해 금융시장의 변동성과 거래량의 관계에 정의 영향을 미치는 일반정보와 달리 충격정보는 부의 영향을 미칠 수 있다는 사실을 밝히고, 충격정보를 고려하지 못한 기존 모형들의 경험적 연구결과들이 잘못될 수 있음을 지적하였으며 관측 불가능한 충격정보를 탐지하기 위한 방법을 제시하였다. 이런 충격정보와 부호효과(sign effect)를 고려하기 위해 세 가지 유형의 정보유입 (충격정보 부재, 정의 충격정보, 부의 충격정보)을 반영하기 위해 정보유형전환 GARCH-V 모형을 제안한다. 외환시장의 고빈도 자료를 이용한 경험적 연구결과에 의하면 변동성과 거래량의 관계가 정보의 유형에 영향을 받으며 정보유형전환 GARCH-V 모형이 표준 GARCH-V 모형에 비해 우수함을 알 수 있다. 또한 흥미롭게도 부호효과를 반영한 충격정보와 거래량을 동시에 고려하면 거래량만을 고려하는 경우보다 GARCH 효과가 현격하게 감소함을 보여준다.

This report introduces the concept of ？surprising information,？ which is unexpected information that greatly impacts markets. Employing the Mixture of Distribution Hypothesis (MDH), this report also theoretically demonstrates that the effect of surpri ...

This report introduces the concept of ？surprising information,？ which is unexpected information that greatly impacts markets. Employing the Mixture of Distribution Hypothesis (MDH), this report also theoretically demonstrates that the effect of surprising information on the relationship between volatility and trading volume contrasts with that of general information. Therefore, a failure to account for surprising information might result in conflicting empirical evidence on the relationship between volatility and trading volume. To detect the unobservable surprising information, this report proposes a method based upon a quantile regression of trading volumes on realized volatility. Furthermore, incorporating surprising information with a sign effect, this report suggests an information-type-switching GARCH-V model, which allows for three types of information arrivals ？ ？non-surprising information,？ ？positive surprising information,？ and ？negative surprising information？. Strong evidence in favor of the model specification over the standard GARCH models is based on empirical application with high frequency data, supporting the dependence of the relationship between volatility and trading volume on the type of information and, interestingly, showing that trading volume with the specification of surprising information absorbs GARCH effects remarkably while trading volume alone does not. These empirical findings substantially support the reliability of the modified MDH with surprising information classified into two types: positive and negative surprising information.

This report introduces the concept of ？surprising information,？ which is unexpected information that greatly impacts markets. Employing the Mixture of Distribution Hypothesis (MDH), this report also theoretically demonstrates that the effect of surpri ...

This report introduces the concept of ？surprising information,？ which is unexpected information that greatly impacts markets. Employing the Mixture of Distribution Hypothesis (MDH), this report also theoretically demonstrates that the effect of surprising information on the relationship between volatility and trading volume contrasts with that of general information. Therefore, a failure to account for surprising information might result in conflicting empirical evidence on the relationship between volatility and trading volume. To detect the unobservable surprising information, this report proposes a method based upon a quantile regression of trading volumes on realized volatility. Furthermore, incorporating surprising information with a sign effect, this report suggests an information-type-switching GARCH-V model, which allows for three types of information arrivals ？ ？non-surprising information,？ ？positive surprising information,？ and ？negative surprising information？. Strong evidence in favor of the model specification over the standard GARCH models is based on empirical application with high frequency data, supporting the dependence of the relationship between volatility and trading volume on the type of information and, interestingly, showing that trading volume with the specification of surprising information absorbs GARCH effects remarkably while trading volume alone does not. These empirical findings substantially support the reliability of the modified MDH with surprising information classified into two types: positive and negative surprising information.

Much research has empirically examined the positive relationship between volatility and trading volume. Nonetheless, a consensus on the relationship has not been reached. In search of a feasible cause for this mixing outcome, this notes that the exist ...

Much research has empirically examined the positive relationship between volatility and trading volume. Nonetheless, a consensus on the relationship has not been reached. In search of a feasible cause for this mixing outcome, this notes that the existing literature ignores the characteristic of surprising information that influences the relationship negatively. Therefore, this report has applied the concept of surprising information with the sign effect to modify the MDH theory. The report has also, from a practical perspective, proposed a suitable method for picking up on unobservable surprising information on the basis of the quantile regression of trading volume on realized volatility.
To detect surprising information in empirical work, we have used four-minute frequency data on the KRW/ USD spot exchange rates, covering the period between April 9, 2004, and October 31, 2006. Trading-volume data in the Korean foreign exchange market are systematically reliable. Further, due to the sign effect of return shock, generally observed in real markets, it is plausible to classify surprising information into two types: positive surprising information and negative surprising information. Therefore, the information-type-switching GARCH(1,1)-V model has been estimated to analyze the relationship between volatility, trading volume, and the GARCH effect and to verify whether the modified MDH with surprising information is reliable in empirical work. The estimation results worth mentioning are as follows: First, absolute return residuals widely used to estimate daily volatility are noisy estimates as compared to realized volatility. Second, from estimating the quantile regression model of realized volatility on trading volume at six representative quantiles , we discover that, as we pass from the median to upper quantiles, the relationship coefficient becomes less significant while its estimate tends to increase consistently. This finding is quite consistent with the modified version of the MDH in which surprising information that influences the relationship negatively, is distinguished from general information. Further, as expected, the negative effect of negative surprising information on the relationship is greater than that of positive surprising information. Third, trading volume alone cannot sufficiently absorb the GARCH effects. This may arise because although the trading-volume series is generally regarded as a proxy for the rate of general information arrival, for surprising information, it becomes a poor proxy because it cannot account for the arrival rate of surprising information that has conflicting effects on trading volume and volatility. As such, our empirical results are sufficiently supportive of the modified version of the MDH with surprising information classified into two types: positive and negative surprising information..
With respect to future research, our study suggests that it would be interesting to replace the variance equation in GARCH(p,q) models with a sophisticated nonlinear specification that sufficiently accounts for the role of surprising information in a volatility model. We might also consider a stochastic volatility representation for returns as a more reliable model accounting for surprising information arrivals.