본 논문에서는 경제시계열의 방향성여부를 검정하고 방향성의 정도를 추정하는 새로운 통계적 방법론을 제시한다. 본 논문에서 고려한 검정통계량은 Quantile Hit sequence의 correlogram의 함수로 정의된다. 이 논문에서는 검정통계량의 점근적 분포를 도출하고 현실적으 ...

본 논문에서는 경제시계열의 방향성여부를 검정하고 방향성의 정도를 추정하는 새로운 통계적 방법론을 제시한다. 본 논문에서 고려한 검정통계량은 Quantile Hit sequence의 correlogram의 함수로 정의된다. 이 논문에서는 검정통계량의 점근적 분포를 도출하고 현실적으로 쉽게 계산할 수 있는 임계치를 제시한다. SP500자료에 직접 검정법을 적용한 결과 주가수익률에는 방향성이 존재한다는 유의적인 검정결과를 얻을 수 있었다.

We propose a simple method of measuring directional predictability and testing for the hypothesis that a given time series has no directional predictability. The test is based on comparing the correlogram of quantile hits to a pointwise confidence int ...

We propose a simple method of measuring directional predictability and testing for the hypothesis that a given time series has no directional predictability. The test is based on comparing the correlogram of quantile hits to a pointwise confidence interval or on comparing the cumulated squared autocorrelations with the corresponding critical value. We provide the distribution theory needed to conduct inference, propose some model free upper bound critical values, and apply our methods to S&P500 stock index return data. The empirical results suggests some directional predictability in returns. The evidence is strongest in mid range quantiles like 5%-10% and for daily data. The evidence for predictability at the median is of comparable strength to the evidence around the mean, and is strongest at the daily frequency.

We propose a simple method of measuring directional predictability and testing for the hypothesis that a given time series has no directional predictability. The test is based on comparing the correlogram of quantile hits to a pointwise confidence int ...

We propose a simple method of measuring directional predictability and testing for the hypothesis that a given time series has no directional predictability. The test is based on comparing the correlogram of quantile hits to a pointwise confidence interval or on comparing the cumulated squared autocorrelations with the corresponding critical value. We provide the distribution theory needed to conduct inference, propose some model free upper bound critical values, and apply our methods to S&P500 stock index return data. The empirical results suggests some directional predictability in returns. The evidence is strongest in mid range quantiles like 5%-10% and for daily data. The evidence for predictability at the median is of comparable strength to the evidence around the mean, and is strongest at the daily frequency.

국제학술지 Journal of Econometrics에 제출예정

국제학술지 Journal of Econometrics에 제출예정