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Quantifying the effects of long-term news on stock markets on the basis of the multikernel Hawkes process

Science China(Information Sciences) 2021年第9期64卷 44-56页

作者： Xiao DING Jihao SHI Junwen DUAN Bing QIN Ting LIU Research Center for Social Computing and Information Retrieval

Recent studies have revealed that long-term financial news can affect stock markets. However,previous research mainly focuses on modeling the short-term effects of financial news and suffers from the weak ability of quantifying the time-decaying influence of financial news. To fill this gap, this study introduces the Hawkes process to estimate the time-decaying influence of long-term financial news. However, the performance of the conventional Hawkes process is sensitive to the choice of kernel functions. Hence, we propose a novel multikernel-powered Hawkes process framework, which uses multiple kernels to model different time-decaying rates, thus alleviating the instability of our proposed Hawkes process based prediction model.Experimental results show that the proposed framework yields state-of-the-art stock market prediction accuracies on 515 listed companies and gains more profits in market trading simulation compared with baseline methods. News-based stock prediction can complement studies on price-volume-based stock prediction.

关键词： Hawkes process multikernel function stock market prediction long-term news effects

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Derivative Failure of Compressor Station Analysis Based on Hawkes process

Derivative Failure of Compressor Station Analysis Based on H...

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第三十九届中国控制会议

作者： Lu-ning Zhang Xin Zuo Jian-wei Liu Department of Automation China University of PetroleumBeijing Campus (CUP) Department of Automation China University of PetroleumBeijing Campus Beijing (CUP)

In the circumstance of practical production, derivative failures is an important part of system failures in the compressor station systems. In order to prevent the occurrence of subsequent faults, we would like to analyze the trigger pattern between the failures. However, the existing methods rarely consider the time-stamp of the failures and find the rule how the trigger patterns change over time. Therefore, we intend to analyze the characteristics of the failure trigger pattern over time. After learning the law of trigger patterns of the derivative failures, which change with time-lapse, we can take precautionary measures to reduce the occurrence of derivative failures, which will bring economic and security benefits. Thus, in this paper, we utilize the Hawkes process to model and analyze the cause and trigger pattern. We label the sequences of fault events on nearly 90 compressor stations and use the sequences of failure events to derive failure cause analysis. We devise a non-parametric Hawkes process model and introduce two sparse regularizers to select the salient non-parametric basis function and circumvent the over-fitting.In the experiment, our proposed model outperforms two Hawkes process and two prior Poisson process baselines with intensities that follow the exponential distribution or Weibull distribution. We conduct a detailed analysis of the self-excitation modes and the other-excitation modes of the derivative faults, and based on the stable and unstable trigger pattern of the failures, we put forward some suggestions for the actual production process of the compressor stations.

关键词： Hawkes process impact function derivative failure trigger pattern compressor station

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Topology tracking of dynamic UAV wireless networks

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Chinese Journal of Aeronautics 2022年第11期35卷 322-335页

作者： Yehui SONG Guoru DING Jiachen SUN Jinghua LI Yitao XU College of Communications Engineering Army Engineering University of PLANanjing 210007China

Wireless network is the communication foundation that supports the intelligentization of Unmanned Aerial Vehicle(UAV) swarm. The topology of UAV communication network is the key to understanding and analyzing the behavior of UAV swarm, thus supporting the further prediction of UAV operations. However, the UAV swarm network topology varies over time due to the high mobility and diversified mission requirements of UAVs. Therefore, it is important but challenging to research dynamic topology inference for tracking the topology changes of the UAV network,especially in non-cooperative manner. In this paper, we study the problem of inferring UAV swarm network topology based on external observations, and propose a dynamic topology inference method. First, we establish a sensing framework for acquiring the communication behavior of the target network over time. Then, we expand the multi-dimensional dynamic Hawkes process to model the communication event sequence in a dynamic wireless network. Finally, combining the sliding time window mechanism, the maximum weighted likelihood estimation is applied to inferring the network topology. Extensive simulation results demonstrate the effectiveness of the proposed method.

关键词： Dynamic topology inference Event detection Hawkes process UAV swarm Wireless networks

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Rough Heston Models with Variable Vol-of-Vol and Option Pricing

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Annals of Applied Mathematics 2023年第2期39卷 206-238页

作者： Hui Liang Jingtang Ma Zhengguang Shi School of Science Harbin Institute of TechnologyShenzhenGuangdong 518055China School of Mathematics Southwestern University of Finance and EconomicsChengduSichuan 611130China School of Mathematical Sciences Sichuan Normal UniversityChengduSichuan 610066China

In this paper,a rough Heston model with variable volatility of volatility(vol-of-vol)is derived by modifying the generalized nonlinear Hawkes process and extending the scaling techniques.Then the nonlinear fractional Ric-cati equation for the characteristic function of the asset log-price is derived.The existence,uniqueness and regularity of the solution to the nonlinear fractional Riccati equation are proved and the equation is solved by the Adams methods.Finally the Fourier-cosine methods are combined with the Adams methods to price the options.

关键词： Rough Heston model option pricing Hawkes process fractional differential equations Fourier-cosine methods

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