Tehran Institute for Advanced Studies (TeIAS)

/ Information Theory for Financial Network Analysis __ Ali Habibnia


Information Theory for Financial Network Analysis

July 11, 2021
(20 Tir, 1400)



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July 10, 2021

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Dr. Ali Habibnia

Assistant Professor of Economics at Virginia Tech


Measures from information theory, such as Shannon entropy, have been used in a variety of economic and financial applications. In this work, we develop a data-driven econometric framework that promotes an understanding of the relationship between financial institutes using directed information graphs (DIGs). DIG is an information-theoretical generalization of Granger-causality which is suitable for capturing the interconnections between a set of high-dimensional time series with linear or nonlinear dynamics. We discuss how the structure of financial systems can be represented as a complex network, where the nodes are financial institutes and the connections are formed utilizing a connectivity measure. A weighted directed network can then be formed to identify the level of systemic risk in the financial sector. We also show (theoretically and through the simulation) how the proposed framework improves the measurement of systemic risk and explain its link to several well-known econometric models such as vector autoregression, switching models, and others.



Ali Habibnia is an assistant professor in the Department of Economics and the Computational Modeling and Data Analytics, College of Science, Virginia Tech. His research focuses on the intersection of statistical machine learning and big data econometrics, with a particular interest in the high-dimensional nonlinear time-series analysis and their applications in macroeconomic/financial forecasting and estimation of economic and financial networks. He received his Ph.D. from the London School of Economics and Political Science, Department of Statistics. He also works as a consultant for financial firms and governmental organizations for all businesses related to pattern recognition and knowledge discovery, especially computer modelling and forecasting. He has been in the industry for a few years, working as a trader and portfolio strategist.