当前对资产组合在险价值(VaR)的研究仅限于等间隔抽样数据的建模。本文提出资产组合的非等间隔日内在险价值(Irregularly Spaced Intraday Value at Risk,ISIVaR)研究方法,克服资产组合逐笔交易数据非等间隔且不同步问题,利用逐笔交易...当前对资产组合在险价值(VaR)的研究仅限于等间隔抽样数据的建模。本文提出资产组合的非等间隔日内在险价值(Irregularly Spaced Intraday Value at Risk,ISIVaR)研究方法,克服资产组合逐笔交易数据非等间隔且不同步问题,利用逐笔交易数据所包含的丰富市场微观结构信息对VaR进行估计。该方法基于更新时间方法将非同步的资产组合标值序列同步化;运用Copula理论建立资产组合的非等间隔日内波动率模型,并捕捉资产组合中各资产在截面上的相关关系;最后利用这种截面相关关系,使用蒙特卡洛模拟技术估计出资产组合的ISIVaR。实证部分利用真实的逐笔交易数据验证了上述方法的有效性。展开更多
The metal futures price fluctuation prediction model was constructed based on symbolic high-frequency time series using high-frequency data on the Shanghai Copper Futures Exchange from July 2014 to September 2018,and ...The metal futures price fluctuation prediction model was constructed based on symbolic high-frequency time series using high-frequency data on the Shanghai Copper Futures Exchange from July 2014 to September 2018,and the sample was divided into 194 histogram time series employing symbolic time series.The next cycle was then predicted using the K-NN algorithm and exponential smoothing,respectively.The results show that the trend of the histogram of the copper futures earnings prediction is gentler than that of the actual histogram,the overall situation of the prediction results is better,and the overall fluctuation of the one-week earnings of the copper futures predicted and the actual volatility are largely the same.This shows that the results predicted by the K-NN algorithm are more accurate than those predicted by the exponential smoothing method.Based on the predicted one-week price fluctuations of copper futures,regulators and investors in China’s copper futures market can timely adjust their regulatory policies and investment strategies to control risks.展开更多
利用超高频数据基于不规则时间序列对知情交易概率和交易活跃程度进行系统建模,使用H am ilton状态转移方程和混沌优化算法对超高频知情交易概率进行估计,实证检验中国股票市场上交易持续期间、交易量和知情交易概率之间的日内动态关系...利用超高频数据基于不规则时间序列对知情交易概率和交易活跃程度进行系统建模,使用H am ilton状态转移方程和混沌优化算法对超高频知情交易概率进行估计,实证检验中国股票市场上交易持续期间、交易量和知情交易概率之间的日内动态关系及其相互影响,重点研究交易活跃程度和信息之间的超高频特性。结果表明在超高频水平上久期和交易量之间呈现负相关关系,并且信息冲击会导致交易更活跃。展开更多
文摘当前对资产组合在险价值(VaR)的研究仅限于等间隔抽样数据的建模。本文提出资产组合的非等间隔日内在险价值(Irregularly Spaced Intraday Value at Risk,ISIVaR)研究方法,克服资产组合逐笔交易数据非等间隔且不同步问题,利用逐笔交易数据所包含的丰富市场微观结构信息对VaR进行估计。该方法基于更新时间方法将非同步的资产组合标值序列同步化;运用Copula理论建立资产组合的非等间隔日内波动率模型,并捕捉资产组合中各资产在截面上的相关关系;最后利用这种截面相关关系,使用蒙特卡洛模拟技术估计出资产组合的ISIVaR。实证部分利用真实的逐笔交易数据验证了上述方法的有效性。
基金Projects(71633006,7184207,7184210)supported by the National Natural Science Foundation of ChinaProject(2019CX016)supported by the Annual Innovation-driven Project in Central South University,China。
文摘The metal futures price fluctuation prediction model was constructed based on symbolic high-frequency time series using high-frequency data on the Shanghai Copper Futures Exchange from July 2014 to September 2018,and the sample was divided into 194 histogram time series employing symbolic time series.The next cycle was then predicted using the K-NN algorithm and exponential smoothing,respectively.The results show that the trend of the histogram of the copper futures earnings prediction is gentler than that of the actual histogram,the overall situation of the prediction results is better,and the overall fluctuation of the one-week earnings of the copper futures predicted and the actual volatility are largely the same.This shows that the results predicted by the K-NN algorithm are more accurate than those predicted by the exponential smoothing method.Based on the predicted one-week price fluctuations of copper futures,regulators and investors in China’s copper futures market can timely adjust their regulatory policies and investment strategies to control risks.
文摘利用超高频数据基于不规则时间序列对知情交易概率和交易活跃程度进行系统建模,使用H am ilton状态转移方程和混沌优化算法对超高频知情交易概率进行估计,实证检验中国股票市场上交易持续期间、交易量和知情交易概率之间的日内动态关系及其相互影响,重点研究交易活跃程度和信息之间的超高频特性。结果表明在超高频水平上久期和交易量之间呈现负相关关系,并且信息冲击会导致交易更活跃。
