论文标题
在纯跳跃过程中实现波动率的长马返回可预测性
Long-Horizon Return Predictability from Realized Volatility in Pure-Jump Point Processes
论文作者
论文摘要
我们开发并证明方法是基于实现的方差始终如一地测试长马返回可预测性的合理性。为了实现这一目标，我们根据纯粹的跳跃点过程为连续时间日志价格过程提出了一个参数交易级模型。该模型确定回报和在任何级别的聚合中实现的差异，其属性与经验金融文献中的风格化事实相符。在我们的模型下，长期内存参数从交易级漂移到日历时间返回和已实现的方差不变，内源性地导致了平衡的预测回归方程。我们在预测回归中使用幂律聚集提出了一个渐近框架。在此框架内，我们提出了一个假设测试，以实现长范围返回可预测性，该可预测性在渐近尺寸且一致。
We develop and justify methodology to consistently test for long-horizon return predictability based on realized variance. To accomplish this, we propose a parametric transaction-level model for the continuous-time log price process based on a pure jump point process. The model determines the returns and realized variance at any level of aggregation with properties shown to be consistent with the stylized facts in the empirical finance literature. Under our model, the long-memory parameter propagates unchanged from the transaction-level drift to the calendar-time returns and the realized variance, leading endogenously to a balanced predictive regression equation. We propose an asymptotic framework using power-law aggregation in the predictive regression. Within this framework, we propose a hypothesis test for long horizon return predictability which is asymptotically correctly sized and consistent.
