When the observed price process is the true underlying price process plus microstructure noise, it is known that realized volatility (RV) estimates will be overwhelmed by the noise when the sampling frequency approach...When the observed price process is the true underlying price process plus microstructure noise, it is known that realized volatility (RV) estimates will be overwhelmed by the noise when the sampling frequency approaches infinity. Therefore, it may be optimal to sample less frequently, and averaging the less frequently sampled subsamples can improve estimation for quadratic variation. In this paper, we extend this idea to forecasting daily realized volatility. While subsample averaging has been proposed and used in estimating RV, this paper is the first that uses subsample averaging for forecasting RV. The subsample averaging method we examine incorporates the high frequency data in different levels of systematic sampling. It first pools the high frequency data into several subsamples, then generates forecasts from each subsample, and then combines these forecasts. We find that in daily S&P 500 return realized volatility forecasts, subsample averaging generates better forecasts than those using only one subsample.展开更多
基于高频数据视角,文章提出时变参数(TV)Realized HAR GARCH混合预测模型,同时考虑资产波动率的聚集性、长记忆以及由测量误差引起的参数衰减偏差效应.进一步,为充分利用价格信息并提升估计效率,本文基于日内“OHLC”数据构建赋权修正...基于高频数据视角,文章提出时变参数(TV)Realized HAR GARCH混合预测模型,同时考虑资产波动率的聚集性、长记忆以及由测量误差引起的参数衰减偏差效应.进一步,为充分利用价格信息并提升估计效率,本文基于日内“OHLC”数据构建赋权修正已实现信息波动率(WRIV),并将其用于驱动条件方差的动态变化.在偏t分布假设下,以沪深300指数为样本探究中国股票市场的波动性规律,并在实证中评估所提模型在收益率拟合、波动率预测以及风险度量上的能力.结果显示:中国股票市场的收益波动存在明显的异质性与长记忆特征,TV-Realized HAR GARCH能够充分捕捉指数波动率的动态变化,在样本内拟合效果和样本外波动率与风险预测准确性上均能显现出优势,且WRIV测度的引入能显著提升模型的预测精度,凸显出日内高频数据信息的充分利用对于波动率刻画与风险测度的重要性,综合而言,TV-Realized HAR GARCH(WRIV)模型具有最优的整体实证表现.展开更多
Increasing attention has been focused on the analysis of the realized volatil- ity, which can be treated as a proxy for the true volatility. In this paper, we study the potential use of the realized volatility as a pr...Increasing attention has been focused on the analysis of the realized volatil- ity, which can be treated as a proxy for the true volatility. In this paper, we study the potential use of the realized volatility as a proxy in a stochastic volatility model estimation. We estimate the leveraged stochastic volatility model using the realized volatility computed from five popular methods across six sampling-frequency transaction data （from 1-min to 60- min） based on the trust region method. Availability of the realized volatility allows us to estimate the model parameters via the MLE and thus avoids computational challenge in the high dimensional integration. Six stock indices are considered in the empirical investigation. We discover some consistent findings and interesting patterns from the empirical results. In general, the significant leverage effect is consistently detected at each sampling frequency and the volatility persistence becomes weaker at the lower sampling frequency.展开更多
论述证券市场“已实现”波动率的理论,利用深圳成分指数和上海综合指数的5 m in高频数据,对沪深两市的波动周期做了实证分析,通过Fourier谱分析,比较了沪深两市的波动周期,揭示了我国股市的周期波动性特征.目前,我国股票市场的周期性研...论述证券市场“已实现”波动率的理论,利用深圳成分指数和上海综合指数的5 m in高频数据,对沪深两市的波动周期做了实证分析,通过Fourier谱分析,比较了沪深两市的波动周期,揭示了我国股市的周期波动性特征.目前,我国股票市场的周期性研究多集中在市场指数和收益率的低频数据周期分析,本文的特点是利用高频数据对波动率这一重要参数的周期性进行分析.展开更多
文摘When the observed price process is the true underlying price process plus microstructure noise, it is known that realized volatility (RV) estimates will be overwhelmed by the noise when the sampling frequency approaches infinity. Therefore, it may be optimal to sample less frequently, and averaging the less frequently sampled subsamples can improve estimation for quadratic variation. In this paper, we extend this idea to forecasting daily realized volatility. While subsample averaging has been proposed and used in estimating RV, this paper is the first that uses subsample averaging for forecasting RV. The subsample averaging method we examine incorporates the high frequency data in different levels of systematic sampling. It first pools the high frequency data into several subsamples, then generates forecasts from each subsample, and then combines these forecasts. We find that in daily S&P 500 return realized volatility forecasts, subsample averaging generates better forecasts than those using only one subsample.
文摘基于高频数据视角,文章提出时变参数(TV)Realized HAR GARCH混合预测模型,同时考虑资产波动率的聚集性、长记忆以及由测量误差引起的参数衰减偏差效应.进一步,为充分利用价格信息并提升估计效率,本文基于日内“OHLC”数据构建赋权修正已实现信息波动率(WRIV),并将其用于驱动条件方差的动态变化.在偏t分布假设下,以沪深300指数为样本探究中国股票市场的波动性规律,并在实证中评估所提模型在收益率拟合、波动率预测以及风险度量上的能力.结果显示:中国股票市场的收益波动存在明显的异质性与长记忆特征,TV-Realized HAR GARCH能够充分捕捉指数波动率的动态变化,在样本内拟合效果和样本外波动率与风险预测准确性上均能显现出优势,且WRIV测度的引入能显著提升模型的预测精度,凸显出日内高频数据信息的充分利用对于波动率刻画与风险测度的重要性,综合而言,TV-Realized HAR GARCH(WRIV)模型具有最优的整体实证表现.
文摘Increasing attention has been focused on the analysis of the realized volatil- ity, which can be treated as a proxy for the true volatility. In this paper, we study the potential use of the realized volatility as a proxy in a stochastic volatility model estimation. We estimate the leveraged stochastic volatility model using the realized volatility computed from five popular methods across six sampling-frequency transaction data （from 1-min to 60- min） based on the trust region method. Availability of the realized volatility allows us to estimate the model parameters via the MLE and thus avoids computational challenge in the high dimensional integration. Six stock indices are considered in the empirical investigation. We discover some consistent findings and interesting patterns from the empirical results. In general, the significant leverage effect is consistently detected at each sampling frequency and the volatility persistence becomes weaker at the lower sampling frequency.
文摘论述证券市场“已实现”波动率的理论,利用深圳成分指数和上海综合指数的5 m in高频数据,对沪深两市的波动周期做了实证分析,通过Fourier谱分析,比较了沪深两市的波动周期,揭示了我国股市的周期波动性特征.目前,我国股票市场的周期性研究多集中在市场指数和收益率的低频数据周期分析,本文的特点是利用高频数据对波动率这一重要参数的周期性进行分析.
