本文應用順序統計量的統計特性以及日內資料的訊息提出一個檢定價格跳躍的新方法。有別於晚近盛行的價格跳躍檢定僅能區別跳躍的存在與否，由於我們的新方法能預先設定跳躍幅度的門檻值，更能彈性的區別出價格跳躍之方向與大小。根據數值模擬分析結果，新檢定對於操作門檻值的選取、日內資料中所存在的微結構雜訊，還有價格中所伴隨的隨機波動性等效果，都具有相當程度的穩健性。在實證中，我們發現不僅正負跳躍的頻率和大小幅度並不相同，發生價格跳躍的時間亦有群聚現象；同時，跳躍的群聚與 VIX 有相當的關聯性。此外，我們也進一步利用不同的門檻值設定，認定出不同幅度的價格跳躍在橫斷面上以及時序上所呈現的各種跳躍的特徵，並確認正、負跳躍對於價格波動有高度不對稱性的影響。新檢定法與其實證發現對日後的風險管理實務與投資有重要的啟發。

This paper proposes a newly-developed jump test based on the order statistics from intraday returns to identify the direction and magnitude of jumps. Our identification strategy, as suggested in the recent high-frequency finance, hinges on the local Gaussianity of the intraday return distribution in absence of jumps. Our test allows for an operational threshold in examining and characterizing a potential spectrum of jump sizes with signs. Besides its better statistical size and power properties, our numerical results demonstrate its robustness to the threshold, market microstructure noises, and the underlying stochastic volatility; our empirical evidence delivers several interesting and meaningful points. First, the numbers of identified positive and negative jumps are significantly fdfi erent in their intensities and sizes, respectively. We find that jumps come in clusters and such clustering patterns can be linked to forward looking variables such as VIX. Moreover, by varying the operational threshold, our jump test allows us to sketch a rough picture understanding various features of finite-activity jumps. Finally, we observe asymmetries in the intensities as well as the jump-contributed price variations between the positive and negative ones. Given these observations, we expect this jump test to be potentially useful in investments or risk management.