High Frequency Financial Time Series Forecasting via Particle Filtering

机译：通过粒子滤波的高频金融时间序列预测

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Of the strong non-Gauss characteristic, the high frequency financial time series could not be analyzed and forecasted by traditional statistics method any more. For inaccurately estimating the realized volatility using the limited high frequency data created by the market operation, a novel forecasting method is proposed: after modeling the realized volatility, the particle filtering technology for non-Gauss non-liner process is adopted to analyze and predict the volatility, hence the intra-day transaction data could be treated. The method is applied in the MSFT intra-day quote forecasting and a perfect result is obtained.

机译：具有很强的非高斯特性，高频金融时间序列已不再能够通过传统的统计方法进行分析和预测。为了利用市场操作所产生的有限的高频数据来不准确地估计实际波动率，提出了一种新颖的预测方法：在对实际波动率建模之后，采用了非高斯非线性过程的粒子滤波技术来分析和预测波动率。波动性，因此可以处理当日交易数据。将该方法应用于MSFT日内报价预测中，得到了理想的结果。

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《International Conference on Information Management, Innovation Management and Industrial Engineering;ICIII 2009》|2009年|62-65|共4页
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Gaoyu Zhang; Qiongfei Li; Qing Luo; Zhizhao Zhou;
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financial time series; forecasting; high frequency; particle filtering; realized volatility;

机译：金融时间序列;预测;高频;粒子滤波;已实现波动;

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1. A Fuzzy Interval Time-Series Energy and Financial Forecasting Model Using Network-Based Multiple Time-Frequency Spaces and the Induced-Ordered Weighted Averaging Aggregation Operation [J] . Liu Gang, Xiao Fuyuan, Lin Chin-Teng, IEEE Transactions on Fuzzy Systems . 2020,第11期

机译：一种模糊间隔时间序列能源和金融预测模型使用网络的多时频空间和诱导有序加权平均聚合操作
2. Using random quasi-Monte-Carlo within particle filters, with application to financial time series [J] . Fearnhead P Journal of computational and graphical statistics: A joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America . 2005,第4期

机译：在粒子滤波器中使用随机的准蒙特卡洛法，并应用于金融时间序列
3. Forecasting of Financial Time Series by a Digital Filter and a Neural-Network [J] . Susumu Saito, Shintaro Kanda 電子情報通信学会技術研究報告. パターン認識·メディア理解. Pattern Recognition and Media Understanding . 2003,第296期

机译：通过数字滤波器和神经网络预测财务时间序列
4. High frequency financial time series forecasting via particle filtering [C] . ZHANG Gaoyu, LI Qiongfei, LUO Qing, International Conference on Information Management, Innovation Management and Industrial Engineering . 2009

机译：高频财务时间序列通过粒子过滤预测
5. Forecasting realized volatility with long memory time series models using high frequency financial data: Estimation, prediction, seasonal adjustment and computation. [D] . Lu, Yi. 2005

机译：使用高频财务数据通过长存储时间序列模型预测已实现的波动：估计，预测，季节性调整和计算。
6. Forecasting Financial Time Series through Causal and Dilated Convolutional Neural Networks [O] . Lukas Börjesson, Martin Singull 2020

机译：通过因果和扩张卷积神经网络预测财务时间序列
7. Forecasting Financial Time Series using Linear Predictive Filters [O] . Li Bin 2013

机译：使用线性预测滤波器预测金融时间序列

High Frequency Financial Time Series Forecasting via Particle Filtering

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