发帖
雷达卡
摘要翻译：
提出了一种根据相互因果关系对股票指数进行排序的非参数方法。在指数反映一个国家基本经济的假设下，这种排名表明哪些国家在全球经济的一个被审查的子集中发挥最大的经济影响力。该方法将指数表示为有向图中的节点，其中边的权重是对因果影响的估计，并使用有向信息泛函进行量化。这种方法便于从每个索引中使用相对较少数量的样本。然后根据它们在估计图中的净流量（输入权重的总和减去输出权重的总和）对索引进行排序。分析了来自九个指数（三个来自亚洲、三个来自欧洲和三个来自美国）的每日和逐分钟数据。对每日数据的分析表明，美国指数最具影响力，这与代表较大经济体的指数通常发挥更大影响力的直觉一致。然而，它还表明，如果较小的经济表明了较大的现象，则代表一个小经济的指数可以强烈地影响代表一个大经济的指数。最后，研究表明，虽然区域间的交互作用可以用每日数据来捕捉，但区域内的交互作用需要更频繁的样本。
---
英文标题：
《Ranking Causal Influence of Financial Markets via Directed Information
Graphs》
---
作者：
Theo Diamandis, Yonathan Murin, Andrea Goldsmith
---
最新提交年份：
2018
---
分类信息：
一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
--
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Signal Processing 信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
--
---
英文摘要：
A non-parametric method for ranking stock indices according to their mutual causal influences is presented. Under the assumption that indices reflect the underlying economy of a country, such a ranking indicates which countries exert the most economic influence in an examined subset of the global economy. The proposed method represents the indices as nodes in a directed graph, where the edges' weights are estimates of the pair-wise causal influences, quantified using the directed information functional. This method facilitates using a relatively small number of samples from each index. The indices are then ranked according to their net-flow in the estimated graph (sum of the incoming weights subtracted from the sum of outgoing weights). Daily and minute-by-minute data from nine indices (three from Asia, three from Europe and three from the US) were analyzed. The analysis of daily data indicates that the US indices are the most influential, which is consistent with intuition that the indices representing larger economies usually exert more influence. Yet, it is also shown that an index representing a small economy can strongly influence an index representing a large economy if the smaller economy is indicative of a larger phenomenon. Finally, it is shown that while inter-region interactions can be captured using daily data, intra-region interactions require more frequent samples.
---
PDF链接：
https://arxiv.org/pdf/1801.06896
提升卡
置顶卡
沉默卡
变色卡
抢沙发
千斤顶
显身卡
京公网安备 11010802022788号
