An Analysis of Stability of Inter-bank Loan Network: A Simulated Network Approach

Research in the domain of Financial Contagion has come to the forefront in recent years. There has been a significant focus on this field since the recession of 2008. In this paper we take a look at simulation based modelling to stress test the stability of inter-bank loan networks of different structures. We look to analyze the effect of various parameters on the stability of these networks. We first simulate networks which are Homogeneous in nature. We then simulate a Heterogeneous (tiered) network. The model also introduces an endogenous loaning mechanism to imitate a more realistic inter bank loan market. We run simulations on these networks to gain a better understanding of the propagation of losses through the network. After studying the results of these simulations we come up with some interesting new insights about how parameters like connectivity and size of the network, effect a tiered intra-bank financial network. One of our key findings is that higher inter-tier connectivity is good for the stability of big banks but not so much for banks of smaller size

Evaluating the role of risk networks on risk identification, classification and emergence

Modern society heavily relies on strongly connected, socio-technical systems. As a result, distinct risks threatening the operation of individual systems can no longer be treated in isolation. Consequently, risk experts are actively seeking for ways to relax the risk independence assumption that undermines typical risk management models. Prominent work has advocated the use of risk networks as a way forward. Yet, the inevitable biases introduced during the generation of these survey-based risk networks limit our ability to examine their topology, and in turn challenge the utility of the very notion of a risk network. To alleviate these concerns, we proposed an alternative methodology for generating weighted risk networks. We subsequently applied this methodology to an empirical dataset of financial data. This paper reports our findings on the study of the topology of the resulting risk network. We observed a modular topology, and reasoned on its use as a robust risk classification framework. Using these modules, we highlight a tendency of specialization during the risk identification process, with some firms being solely focused on a subset of the available risk classes. Finally, we considered the independent and systemic impact of some risks and attributed possible mismatches to their emerging nature.Comment: 21 pages, 7 Figures, 4 tables, To appear in Journal of Network Theory in Financ

Estudio del contagio en Redes Financieras

The purpose of this work focuses on the study of the "Contagion Effect in Financial Networks". The doctrines of this theory concentrate on determining how a financial network is established and how the assets and liabilities between participants are defined. The studies presented by Gai and Kapadia (2009) and Acemoglu et al. (2014) determine that the amount of loans and credits issued by any financial corporation should not exceed the amount of assets that it has. If this were to happen, all banks would be exposed to a waterfall effect. Contrarily, the contagion effect entirely depends on the size of the network. That said, I proceed to analyze in greater depth different models, several implications of systematic risk and their consequences.La finalidad del siguiente trabajo se basa en el estudio del “Contagio en Redes Financieras”. La teoría se enfoca en determinar cómo está establecida una red y cómo están definidos los activos y pasivos interbancarios entre cada banco. Los estudios presentados por Gai y Kapadia (2009) y Acemoglu et al. (2014), definen que la cantidad de préstamos que emite un banco no debe sobrepasarse de los activos que este posee. Si llegara a suceder esto, todos los bancos estarían expuestos a una cascada de defaults. Por otro lado, el contagio depende mucho del tamaño de la red. Mediante los diferentes modelos de estructuración financiera se analizará a mayor profundidad varias implicaciones del riesgo sistemático y sus consecuencias

Exploring the optimal utilization of locational banking statistics data by a national central bank : the South African perspective

Project Work presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementThe financial crisis that emerged in 2008 highlighted the importance of tracking global vulnerabilities through joint analysis of data covering many financial institutions. Locational banking statistics (LBS) were designed to provide comprehensive and consistent data on the banking systems’ funding and lending patterns (BIS, 2014) . The main purpose of the data is to provide information on the role of banks and financial centers in the intermediation of international capital flows. Apart from operational activities, procedures and systems to compile sound cross-border banking system data, there is a need to improve the understanding of the analysis techniques and research outcomes pertaining to this data and specifically how these elements feed into the broader macroeconomic framework, the financial stability regime, and ultimately into policy advice. This study is conducted within a positivist paradigm and investigates the key analytical uses of the LBS data from a national central bank perspective whilst utilising a quantitative approach to develop a suite of analysis mainly through the use of exploratory data analysis (EDA) techniques

The Econometrics of Bayesian Graphical Models: A Review With Financial Application

Recent advances in empirical finance has shown that the adoption of network theory is critical to understand contagion and systemic vulnerabilities. While interdependencies among financial markets have been widely examined, only few studies review networks, however, they do not focus on the econometrics aspects. This paper presents a state-of-the-art review on the interface between statistics and econometrics in the inference and application of Bayesian graphical models. We specifically highlight the connections and possible applications of network models in financial econometrics, in the context of systemic risk

The Econometrics of Bayesian Graphical Models: A Review With Financial Application

Recent advances in empirical finance has shown that the adoption of network theory is critical to understand contagion and systemic vulnerabilities. While interdependencies among financial markets have been widely examined, only few studies review networks, however, they do not focus on the econometrics aspects. This paper presents a state-of-the-art review on the interface between statistics and econometrics in the inference and application of Bayesian graphical models. We specifically highlight the connections and possible applications of network models in financial econometrics, in the context of systemic risk