标题： 股票市场实现波动率预测：路径依赖视角 显示英文标题

标题： Forecasting realized volatility in the stock market: a path-dependent perspective

摘要： 金融市场中的波动率预测是一个受到学者们广泛关注的话题。本文提出了一种新的波动率预测模型，该模型结合了异质自回归（HAR）模型与路径依赖波动率模型家族（HAR-PD）。该模型利用价格数据的长短期记忆特性来捕捉波动率特征和趋势特征。通过将路径依赖波动率的特征整合到HAR模型家族框架中，我们开发出一组新的波动率预测模型。并且，我们基于经验四分位数阈值提出了一个HAR-REQ模型，相比HAR-REX模型展现出更强的预测能力。随后，使用中国股票市场的数据通过统计测试评估了HAR-PD模型家族的预测性能，并与基本的HAR模型家族进行了比较。实证结果显示，HAR-PD模型家族相较于基础HAR模型家族具有更高的预测准确性。此外，稳健性检验确认了HAR-PD模型家族显著的预测能力。 显示英文摘要

摘要： Volatility forecasting in financial markets is a topic that has received more attention from scholars. In this paper, we propose a new volatility forecasting model that combines the heterogeneous autoregressive (HAR) model with a family of path-dependent volatility models (HAR-PD). The model utilizes the long- and short-term memory properties of price data to capture volatility features and trend features. By integrating the features of path-dependent volatility into the HAR model family framework, we develop a new set of volatility forecasting models. And, we propose a HAR-REQ model based on the empirical quartile as a threshold, which exhibits stronger forecasting ability compared to the HAR-REX model. Subsequently, the predictive performance of the HAR-PD model family is evaluated by statistical tests using data from the Chinese stock market and compared with the basic HAR model family. The empirical results show that the HAR-PD model family has higher forecasting accuracy compared to the underlying HAR model family. In addition, robustness tests confirm the significant predictive power of the HAR-PD model family.