As a result, applications of network theory are becoming ever more present in finance, with network analysis providing answers to questions where traditional analysis methods are weak; and also leading to improved models across wide types of risks. In fact, networks underlie virtually every type of risk, including liquidity, operational, insurance, and credit risk. However, while network approaches are very useful, understanding complex networks is not easy.
Network Theory and Financial Risk is a hands-on guide to analysing and modelling financial networks. Authors Kimmo Soramaki and Samantha Cook provide an in-depth introduction to network theory and examine general tools for network analysis, detailing coverage of four types of widely-occurring financial networks: payment systems, exposure networks, trade networks, and asset correlation networks.
Almost all areas of financial risk have an underlying network that is relevant for measuring this risk. Understanding market risk involves understanding an entire network of assets; understanding liquidity risk involves understanding the network of liquidity flows; and understanding systemic risk necessitates understanding the network of counterparty exposures.
Network Theory and Financial Risk presents detailed examples of financial networks, which the reader can then put to use in practice analysing asset, payment, exposure, and supply chain networks for improved assessment and analysis of risk. Moreover, with an improved knowledge of network theory and the understanding that networks are literally everywhere, the reader may apply the concepts of network theory to new areas within and beyond finance.
Network Theory and Financial Risk is geared primarily towards practitioners, quantitative analysts, data scientists, economists and managers who have some knowledge of network theory and want to put that knowledge to use in analysing real network data. By making network analysis accessible, the book will also be of interest to researchers, professors, and students whose subject area contains network data.
Chapter 2: The Basics: Categorizing, Summarizing, and Generating Networks
Chapter 3: Identifying important nodes? Connectedness and Centrality
Chapter 4: Uncovering Latent Structure: Clustering and Community Detection
Chapter 5: Finding Hidden Links: Projection Networks
Chapter 6: Fast Insights: Visualizing Networks
Chapter 7: Financial Cartography: Network Layouts
Chapter 8: Brass Tacks: Complexity Reduction
Chapter 9: Market Risk: Asset Correlation Networks
Chapter 10: Liquidity and Operational Risk: Payment Networks
Chapter 11: Counterparty and Systemic Risk: Exposure Networks
Chapter 12: Supply Chain Risk: Trade Networks
Dr. Samantha Cook is the Chief Scientist at FNA. Before joining FNA in 2012, she worked as a Quantitative Analyst at Google's Research Group in New York and as a Professor of Statistics at Universitat Pompeu Fabra in Barcelona. She has published in financial networks and statistics as well as psychology, economics, and public health, including work on Google Flu Trends. Sam holds a PhD in Statistics from Harvard University.