Abstract I provide a selective review of recent developments in financial econometrics related to measuring, modeling, forecasting, and pricing “good” and “bad” volatilities based on realized variation type measures constructed from high-frequency intraday data. An especially appealing feature of the different measures concerns the ease with which they may be calculated empirically, merely involving cross-products of signed, or thresholded, high-frequency returns. I begin by considering univariate semivariation measures, followed by multivariate semicovariation and semibeta measures, before briefly discussing even richer partial (co)variation measures. I focus my discussion on practical uses of the measures emphasizing what I consider to be the most noteworthy empirical findings to date pertaining to volatility forecasting and asset pricing.

中文翻译：

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已实现的半（共）变量：所有波动率并非均等的迹象

摘要我选择性地回顾了金融计量经济学的最新发展，这些发展与测量、建模、预测和定价“好”和“坏”波动性有关，基于从高频盘中数据构建的已实现的变化类型测量。不同度量的一个特别吸引人的特征是它们可以很容易地根据经验计算，仅涉及有符号或阈值的高频回报的交叉产品。我首先考虑单变量半变量测量，然后是多变量半协变量和半贝塔测量，然后简要讨论更丰富的部分（协）变量测量。