On global stability of financial networks

DasGupta, Bhaskar; Kaligounder, Lakshmi

doi:10.1093/comnet/cnu004

Cited by 13 publications

(15 citation statements)

References 37 publications

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“…Incorporating the effect of interdependence into the risk management process has attracted much recent interest (Battiston, Caldarelli, D'Errico, & Gurciullo, 2016;Battiston, Puliga, Kaushik, Tasca, & Caldarelli, 2012;DasGupta & Kaligounder, 2014;Helbing, 2013;Roukny, Bersini, Pirotte, Caldarelli, & Battiston, 2013;Szymanski, Lin, Asztalos, & Sreenivasan, 2015) partly due to the 2007 -2008 global financial crisis and the way in which traditional risk models, also grounded on the assumption of risk independence, failed to foresee it (Battiston, Caldarelli, et al, 2016;Battiston, Farmer, et al, 2016;Besley & Hennessy, 2009;Schweitzer, Fagiolo, Sornette, Vega-Redondo, & White, 2009).…”

Section: Introductionmentioning

confidence: 99%

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

Ellinas¹,

Allan²,

Coombe³

2017

JNTF

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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. Keywords risk network, network topology, horizon scanning, emerging risk, risk management Key messages The authors introduce a novel methodology to generate risk networks, which relaxes some assumptions of past related work, including prominent work by the World Economic Forum. The risk network can serve as a robust risk classification framework, free from externallyimposed artificial constructs (e.g. risk classification based on regulatory requirements). 2 By applying it to a (re)insurance dataset, the authors use the resulting risk network to evaluate the 'horizon scanning' capacity of every included firm. The risk network uncovers a general mismatch between independent and systemic impact of risks, with authors arguing that this mismatch can be used to classify certain risks as 'emerging'.

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Section: Introductionmentioning

confidence: 99%

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

Ellinas¹,

Allan²,

Coombe³

2017

JNTF

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“…El modelo propone explicar el efecto de contagio combinado dos fases: en la primera de ellas (el choque inicial) se determina la probabilidad de incumplimiento o de quiebra de un banco utilizando el modelo de Merton-Credit-Monitor, que se utiliza como detonador o choque inicial y que determina el número inicial de nodos o bancos afectados. En la segunda fase, a partir del modelo de contagio referido en varios artículos previos, principalmente el de Dasgupta y Kaligounder (2014), se hace una simulación del contagio a los restantes bancos utilizando fundamentalmente el modelo de Erdös-Rényi, donde poco a poco y de acuerdo a una tasa propuesta se va esparciendo el contagio y se va reduciendo el capital de las instituciones afectadas provocando la quiebra o la entrada en default de algunos bancos. El modelo también requiere otros parámetros, como son la proporción de activos externos/activos interbancarios, la proporción capital/activos, el tipo de choque idiosincrático o correlacionado, el grado de los nodos y la distribución homogénea o heterogénea de la red.…”

Section: Introductionunclassified

“…El trabajo comienza con una revisión de los principales trabajos de redes aleatorias, pasando desde el modelo de Erdös-Rényi (1960) y las aportaciones fundamentales de Newman (2003) a la teoría de grafos y redes aleatorias, así como sus trabajos en epidemiología SIR (Newman, 2002). Posteriormente se hace referencia, entre otros, a los trabajos más relacionados con redes aleatorias, como los de Nier y Gai (2010), y los aplicados al caso de México, hasta llegar al artículo de Dasgupta y Kaligounder (2014).…”

Section: Introductionunclassified

Análisis de contagio en el sistema financiero mexicano combinando el modelo de Merton y redes aleatorias

Juárez

2017

Contaduría y Administración

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En el contexto de la regulación y de los acuerdos de Basilea resalta la importancia de controlar el riesgo sistémico y los efectos de contagio. El presente trabajo propone un modelo que consiste de dos fases (considerando el modelo de Merton en la primera parte y un modelo Erdös-Rényi de redes aleatorias en la segunda) para analizar el contagio en el sistema bancario mexicano. Al final se concluye que los bancos pueden ser contagiados a partir de un default inicial y caer en incumplimiento o bien no verse afectados por el evento. Lo anterior dependerá de la volatilidad y del crecimiento de los pasivos y de los parámetros propios de las redes, como son la severidad de choque, el número de nodos afectados inicialmente, la estructura de la red (homogénea o heterogénea), la proporción (activos externos/activos) interbancarios y la proporción capital/activos. Los resultados muestran consistencia con la política de regulación que se lleva a cabo en México para evitar un efecto de contagio en sistema financiero.

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“…Many researchers have shown that combining financial and macroeconomic variables to estimate large vector autoregressive (VAR) models produces better forecasts than standard approaches (see, Banbura et al, 2010;Carriero et al, 2013;Giannone et al, 2005;Koop, 2013;Stock and Watson, 2012). Many others, using datasets of a large number of financial institutions, have shown that the financial system has become highly interconnected and thus, can be represented as a network where linkages among agents sharing common structures play a fundamental role in contagion and the spread of systemic risk (see, Billio et al, 2012;DasGupta and Kaligounder, 2014;Diebold and Yilmaz, 2014;Hautsch et al, 2014;Huang et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Sparse Graphical Vector Autoregression: A Bayesian Approach

2014

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In high-dimensional vector autoregressive (VAR) models, it is natural to have large number of predictors relative to the number of observations, and a lack of efficiency in estimation and forecasting. In this context, model selection is a difficult issue and standard procedures may often be inefficient. In this paper we aim to provide a solution to these problems. We introduce sparsity on the structure of temporal dependence of a graphical VAR and develop an efficient model selection approach. We follow a Bayesian approach and introduce prior restrictions to control the maximal number of explanatory variables for VAR models. We discuss the joint inference of the temporal dependence, the maximum lag order and the parameters of the model, and provide an efficient Markov chain Monte Carlo procedure. The efficiency of the proposed approach is showed on simulated experiments and real data to model and forecast selected US macroeconomic variables with many predictors.

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On global stability of financial networks

Cited by 13 publications

References 37 publications

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

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

Análisis de contagio en el sistema financiero mexicano combinando el modelo de Merton y redes aleatorias

Sparse Graphical Vector Autoregression: A Bayesian Approach

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