Journal of Network Theory in Finance最新文献

Technical indicator selection and trading signal forecasting: varying input window length and forecast horizon for the Pakistan Stock Exchange 技术指标选择与交易信号预测:巴基斯坦证券交易所的不同输入窗口长度和预测范围

Journal of Network Theory in Finance Pub Date : 2022-01-01 DOI: 10.21314/jntf.2021.005

B. Bashir, Faheem Aslam

引用次数: 0

Fractional differencing: (in)stability of spectral structure and risk measures of financial networks 分数差分：金融网络谱结构的稳定性和风险度量

Journal of Network Theory in Finance Pub Date : 2021-09-24 DOI: 10.21314/jntf.2021.002

A. Chakrabarti, A. Chakrabarti

{"title":"Fractional differencing: (in)stability of spectral structure and risk measures of financial networks","authors":"A. Chakrabarti, A. Chakrabarti","doi":"10.21314/jntf.2021.002","DOIUrl":"https://doi.org/10.21314/jntf.2021.002","url":null,"abstract":"Computation of spectral structure and risk measures from networks of multivariate financial time series data has been at the forefront of the statistical finance literature for a long time. A standard mode of analysis is to consider log returns from the equity price data, which is akin to taking first difference ($d = 1$) of the log of the price data. Sometimes authors have considered simple growth rates as well. Either way, the idea is to get rid of the nonstationarity induced by the {it unit root} of the data generating process. However, it has also been noted in the literature that often the individual time series might have a root which is more or less than unity in magnitude. Thus first differencing leads to under-differencing in many cases and over differencing in others. In this paper, we study how correcting for the order of differencing leads to altered filtering and risk computation on inferred networks. In summary, our results are: (a) the filtering method with extreme information loss like minimum spanning tree as well as filtering with moderate information loss like triangulated maximally filtered graph are very susceptible to such d-corrections, (b) the spectral structure of the correlation matrix is quite stable although the d-corrected market mode almost always dominates the uncorrected (d = 1) market mode indicating under-estimation in the standard analysis, and (c) the PageRank-based risk measure constructed from Granger-causal networks shows an inverted U-shape evolution in the relationship between d-corrected and uncorrected return data over the period of analysis 1972-2018 for historical data of NASDAQ.","PeriodicalId":41885,"journal":{"name":"Journal of Network Theory in Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41904578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A block-structured model for banking networks across multiple countries 横跨多个国家的银行网络的块结构模型

Journal of Network Theory in Finance Pub Date : 2021-01-01 DOI: 10.21314/jntf.2021.003

Janina Engel, A. Pagano, M. Scherer

引用次数: 0

In Search of Lost Edges: A Case Study on Reconstructing FInancial Networks 寻找失去的边缘:重建金融网络的案例研究

Journal of Network Theory in Finance Pub Date : 2019-09-03 DOI: 10.21314/jntf.2019.058

Michael Lebacher, Samantha Cook, N. Klein, G. Kauermann

{"title":"In Search of Lost Edges: A Case Study on Reconstructing FInancial Networks","authors":"Michael Lebacher, Samantha Cook, N. Klein, G. Kauermann","doi":"10.21314/jntf.2019.058","DOIUrl":"https://doi.org/10.21314/jntf.2019.058","url":null,"abstract":"To capture the systemic complexity of international financial systems, network data is an important prerequisite. However, dyadic data is often not available, raising the need for methods that allow for reconstructing networks based on limited information. In this paper, we are reviewing different methods that are designed for the estimation of matrices from their marginals and potentially exogenous information. This includes a general discussion of the available methodology that provides edge probabilities as well as models that are focussed on the reconstruction of edge values. Besides summarizing the advantages, shortfalls and computational issues of the approaches, we put them into a competitive comparison using the SWIFT (Society for Worldwide Interbank Financial Telecommunication) MT 103 payment messages network (MT 103: Single Customer Credit Transfer). This network is not only economically meaningful but also fully observed which allows for an extensive competitive horse race of methods. The comparison concerning the binary reconstruction is divided into an evaluation of the edge probabilities and the quality of the reconstructed degree structures. Furthermore, the accuracy of the predicted edge values is investigated. To test the methods on different topologies, the application is split into two parts. The first part considers the full MT 103 network, being an illustration for the reconstruction of large, sparse financial networks. The second part is concerned with reconstructing a subset of the full network, representing a dense medium-sized network. Regarding substantial outcomes, it can be found that no method is superior in every respect and that the preferred model choice highly depends on the goal of the analysis, the presumed network structure and the availability of exogenous information.","PeriodicalId":41885,"journal":{"name":"Journal of Network Theory in Finance","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72407136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

Credit rating analysis based on the network of trading information 基于网络交易信息的信用评级分析

Journal of Network Theory in Finance Pub Date : 2019-07-11 DOI: 10.21314/JNTF.2019.050

Ximei Wang, Boualem Djehiche, Xiaoming Hu

引用次数: 2

Default cascades and systemic risk on different interbank network topologies 不同银行间网络拓扑结构的违约级联和系统性风险

Journal of Network Theory in Finance Pub Date : 2019-07-05 DOI: 10.21314/JNTF.2019.049

N. Scholtes

引用次数: 0

Network Sensitivity of Systemic Risk 系统性风险的网络敏感性

Journal of Network Theory in Finance Pub Date : 2018-05-11 DOI: 10.21314/jntf.2019.056

Amanah Ramadiah, Domenico Di Gangi, D. L. Sardo, Valentina Macchiati, M. Pham, F. Pinotti, M. Wilinski, P. Barucca, G. Cimini

引用次数: 11