In this paper we explore the properties of the global banking network using cross-border bank lending data for 184 countries over 1978-2009. Specifically, we analyze financial interconnectedness using network metrics of centrality, connectivity, and clustering. We document a relatively unstable global banking network, with structural breaks in network indicators identifying several waves of capital flows. Interconnectedness rankings, especially for borrowers, are relatively volatile over the period. Connectivity tends to fall during and after systemic banking crises and sovereign debt crises. The 2008-09 global financial crisis stands out as an unusually large perturbation to the cross-border banking network.
This paper applies network analysis to assess the extent of systemic vulnerabilities in the ECCU banking system. It includes two sets of illustrative stress tests. First, solvency and liquidity shocks to each individual bank and the impact on other banks in the network through their biltareal net asset exposures. Second, country and region-wide tail shocks to GDP affecting capital and liquidity of all banks in the shocked jurisdictions, followed by the rippling effects through the regional network. The results identify systemic institutions that merit hightened attention by the regulator, as determined by the degree of connectivity with the rest of the system, and the extent to which they are vulnerable to the failure of other banks.
Effective cross-border financial surveillance requires the monitoring of direct and indirect systemic linkages. This paper illustrates how network analysis could make a significant contribution in this regard by simulating different credit and funding shocks to the banking systems of a number of selected countries. After that, we show that the inclusion of risk transfers could modify the risk profile of entire financial systems, and thus an enriched simulation algorithm able to account for risk transfers is proposed. Finally, we discuss how some of the limitations of our simulations are a reflection of existing information and data gaps, and thus view these shortcomings as a call to improve the collection and analysis of data on cross-border financial exposures.
This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
The analysis of interconnectedness and contagion is an important part of the financial stability and risk assessment of a country’s financial system. This paper offers detailed and practical guidance on how to conduct a comprehensive analysis of interconnectedness and contagion for a country’s financial system under various circumstances. We survey current approaches at the IMF for analyzing interconnectedness within the interbank, cross-sector and cross-border dimensions through an overview and examples of the data and methodologies used in the Financial Sector Assessment Program. Finally, this paper offers practical advice on how to interpret results and discusses potential financial stability policy recommendations that can be drawn from this type of in-depth analysis.
This paper presents a novel approach to investigate and model the network of euro area banks’ large exposures within the global banking system. Drawing on a unique dataset, the paper documents the degree of interconnectedness and systemic risk of the euro area banking system based on bilateral linkages. We develop a Contagion Mapping model fully calibrated with bank-level data to study the contagion potential of an exogenous shock via credit and funding risks. We find that tipping points shifting the euro area banking system from a less vulnerable state to a highly vulnerable state are a non-linear function of the combination of network structures and bank-specific characteristics.
This book concerns the use of concepts from statistical physics in the description of financial systems. The authors illustrate the scaling concepts used in probability theory, critical phenomena, and fully developed turbulent fluids. These concepts are then applied to financial time series. The authors also present a stochastic model that displays several of the statistical properties observed in empirical data. Statistical physics concepts such as stochastic dynamics, short- and long-range correlations, self-similarity and scaling permit an understanding of the global behaviour of economic systems without first having to work out a detailed microscopic description of the system. Physicists will find the application of statistical physics concepts to economic systems interesting. Economists and workers in the financial world will find useful the presentation of empirical analysis methods and well-formulated theoretical tools that might help describe systems composed of a huge number of interacting subsystems.
Banking is an increasingly global business, with a complex network of international transactions within multinational groups and with international customers. This book provides a thorough, practical analysis of international taxation issues as they affect the banking industry. Thoroughly explaining banking’s significant benefits and risks and its taxable activities, the book’s broad scope examines such issues as the following: taxation of dividends and branch profits derived from other countries; transfer pricing and branch profit attribution; taxation of global trading activities; tax risk management; provision of services and intangible property within multinational groups; taxation treatment of research and development expenses; availability of tax incentives such as patent box tax regimes; swaps and other derivatives; loan provisions and debt restructuring; financial technology (FinTech); group treasury, interest flows, and thin capitalisation; tax havens and controlled foreign companies; and taxation policy developments and trends. Case studies show how international tax analysis can be applied to specific examples. The Organisation for Economic Co-operation and Development Base Erosion and Profit Shifting (OECD BEPS) measures and how they apply to banking taxation are discussed. The related provisions of the OECD Model Tax Convention are analysed in detail. The banking industry is characterised by rapid change, including increased diversification with new banking products and services, and the increasing significance of activities such as shadow banking outside current regulatory regimes. For all these reasons and more, this book will prove to be an invaluable springboard for problem solving and mastering international taxation issues arising from banking. The book will be welcomed by corporate counsel, banking law practitioners, and all professionals, officials, and academics concerned with finance and its tax ramifications.
Leverage Python for expert-level volatility and variance derivative trading Listed Volatility and Variance Derivatives is a comprehensive treatment of all aspects of these increasingly popular derivatives products, and has the distinction of being both the first to cover European volatility and variance products provided by Eurex and the first to offer Python code for implementing comprehensive quantitative analyses of these financial products. For those who want to get started right away, the book is accompanied by a dedicated Web page and a Github repository that includes all the code from the book for easy replication and use, as well as a hosted version of all the code for immediate execution. Python is fast making inroads into financial modelling and derivatives analytics, and recent developments allow Python to be as fast as pure C++ or C while consisting generally of only 10% of the code lines associated with the compiled languages. This complete guide offers rare insight into the use of Python to undertake complex quantitative analyses of listed volatility and variance derivatives. Learn how to use Python for data and financial analysis, and reproduce stylised facts on volatility and variance markets Gain an understanding of the fundamental techniques of modelling volatility and variance and the model-free replication of variance Familiarise yourself with micro structure elements of the markets for listed volatility and variance derivatives Reproduce all results and graphics with IPython/Jupyter Notebooks and Python codes that accompany the book Listed Volatility and Variance Derivatives is the complete guide to Python-based quantitative analysis of these Eurex derivatives products.
With new innovations in the field, this new edition of Network Theory and Financial Risk has been fully updated and expanded. A hands-on guide to analysing and modelling financial networks, authors Kimmo Soramäki and Samantha Cook provide an in-depth introduction to network theory and examine the general tools for network analysis. [Resumen de editor]
