Continuous-Time Models, Realized Volatilities, and Testable Distributional ...

是 否 +2 论坛币

k人 参与回答

经管之家送您一份

应届毕业生专属福利!

求职就业群

赵安豆老师微信：zhaoandou666

经管之家联合CDA

送您一个全额奖学金名额~ !

立即领取

感谢您参与论坛问题回答

经管之家送您两个论坛币！

+2 论坛币

英文文献：Continuous-Time Models, Realized Volatilities, and Testable Distributional Implications for Daily Stock Returns-连续时间模型，已实现的波动率，以及可测试的每日股票回报分布含义
英文文献作者：Torben G. Andersen,Tim Bollerslev,Per Houmann Frederiksen,Morten ?rregaard Nielsen
英文文献摘要：
We provide an empirical framework for assessing the distributional properties of daily specu- lative returns within the context of the continuous-time modeling paradigm traditionally used in asset pricing finance. Our approach builds directly on recently developed realized variation measures and non-parametric jump detection statistics constructed from high-frequency intra- day data. A sequence of relatively simple-to-implement moment-based tests involving various transforms of the daily returns speak directly to the import of different features of the under- lying continuous-time processes that might have generated the data. As such, the tests may serve as a useful diagnostic tool in the specification of empirically more realistic asset pricing models. Our results are also directly related to the popular mixture-of-distributions hypoth- esis and the role of the corresponding latent information arrival process. On applying our sequential test procedure to the thirty individual stocks in the Dow Jones Industrial Average index, the data suggest that it is important to allow for both time-varying diffusive volatility, jumps, and leverage effects in order to satisfactorily describe the daily stock price dynamics. At a broader level, the empirical results also illustrate how the realized variation measures and high-frequency sampling schemes may be used in eliciting important distributional features and asset pricing implications more generally.

我们提供了一个经验框架，以评估日常特殊回报的分布性质，在连续时间模型的背景下，传统上用于资产定价金融。我们的方法直接建立在最近开发实现的变化措施和非参数跳跃检测统计从高频日内数据构建。一系列相对容易实现的基于矩的测试，包括每日收益的各种转换，直接说明可能产生数据的地下连续时间过程的不同特征的导入。因此，这些测试可以作为一种有用的诊断工具，以说明经验上更现实的资产定价模型。我们的结果也直接与流行的混合分布假设和相应的潜在信息到达过程的作用有关。在对道琼斯工业平均指数的30只股票应用我们的连续测试程序时，数据表明，为了令人满意地描述每日股票价格动态，考虑时变扩散波动、跳跃和杠杆效应是重要的。在更广泛的层面上，实证结果还说明了如何实现的变化措施和高频抽样方案，可用于提取重要的分布特征和更普遍的资产定价影响。

扫码加我 拉你入群

请注明：姓名-公司-职位

以便审核进群资格，未注明则拒绝

分享0 收藏0 回帖