Contributions published at Banking (Stefano Battiston)

We develop a model that captures, at the same time, the temporal dynamics of single-firm credit risk and the contagion across banks via a network of obligations and common assets. In particular, we enrich the continuous-time modelling approach of default by accounting explicitly for the procyclical loop between asset prices and leverage. Contagion can spread well before any default occurs, through the value of the obligations held by counterparties. Moreover, the extent of procyclicality effects depends explicitly on the structure of both the interbank network and the asset bank network. We analyse the model in a simplified scenario of a densely connected core of banks and we carry out a systematic investigation of how procyclicality emerges from the multiplicative interplay of market illiquidity and tightness of capital requirements.

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Building on previous works on business fluctuations, we model the propagation of financial distress in a network of regions, each populated by heterogeneous interacting firms and banks. In order to diversify risk, firm sell goods outside their own region and borrow from banks located there. However, this results in ties across regions which propagate financial distress across regional borders. We investigate how the average level of economic integration affects the probability of both individual and systemic failures. We find that the benefit of greater diversification is eventually offset by the effect of financial acceleration and contagion. In particular, beyond a certain level of integration the economy suffers more frequently from events with larger numbers of simultaneous failures.

Traditional economic theory could not explain, much less predict, the near collapse of the financial system and its long-lasting effects on the global economy. Since the 2008 crisis, there has been increasing interest in using ideas from complexity theory to make sense of economic and financial markets. Concepts, such as tipping points, networks, contagion, feedback, and resilience have entered the financial and regulatory lexicon, but actual use of complexity models and results remains at an early stage. Recent insights and techniques offer potential for better monitoring and management of highly interconnected economic and financial systems and, thus, may help anticipate and manage future crises

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The interconnectedness of the financial system is one of the main factors contributing to systemic risk. The financial crisis has shown how the network of intrafinancial exposures may, in times of systemic distress, amplify initially small shocks. In this work, the authors build on the DebtRank methodology by introducing the notion of a network of leverage and propose a two-round stress test exercise. In the first round, a shock hits banks’ external assets; in the second round, these initial losses reverberate in the network of interbank exposures because of the devaluation of interbank obligations. Losses in the second round result from a multiplicative effect between external and interbank leverage. The authors then apply the stress test to the largest EU banks for the years 2008–2013. They find that second-round losses are at least as large as first-round losses; neglecting these effects could therefore lead to a severe underestimation of systemic risk.

In this Chapter, we describe the phenomenology of multilevel financial networks. Network analysis represents a useful tool for the analysis of financial systems, allowing, in particular, for a better understanding of the mechanics of systemic distress. However, the level of complexity reached by the financial system, coupled with the linkages arising to and from other economic sectors, calls for a more integrated approach that takes into account a whole series of networks. In this Chapter, we therefore describe the financial systems as a nexus of interconnected networks. By reviewing selected theoretical and empirical works and describing two methodological extensions for DebtRank, we show different arguments in favor of the adoption of a broader view of the network approach to finance.

Two changes have ushered in a new era of analyzing the complex and interdependent world surrounding us. One is related to the increased influx of data, furnishing the raw material for this revolution that is now starting to impact economic thinking. The second change is due to a subtler reason: a paradigm shift in the analysis of complex systems.

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We report our preliminary results of application of the Takens algorithm to build a FOREX trade strategy, resulting in a steady long-time gain for a trader. The actual historical rates for pair EUR vs. USD are used. The values of various parameters of the problem including the “stop loss” and “take profit” thresholds are optimized to provide the maximal gain during the training period. Then, these values are employed for trades. We have succeeded to get the steady gain, if the spread is neglected. It proves that the FOREX market is predictable.

Social media and social networks contribute to shape the debate on societal and policy issues, but the dynamics of this process is not well understood. As a case study, we monitor Twitter activity on a wide range of environmental issues. First, we identify influential users and communities by means of a network analysis of the retweets. Second, we carry out a content-based classification of the communities according to the main interests and profile of their most influential users. Third, we perform sentiment analysis of the tweets to identify the leaning of each community towards a set of common topics, including some controversial issues. This novel combination of network, content-based, and sentiment analysis allows for a better characterization of groups and their leanings in complex social networks.

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How, and to what extent, does an interconnected financial system endogenously amplify external shocks? This paper attempts to reconcile some apparently different views emerged after the 2008 crisis regarding the nature and the relevance of contagion in financial networks. We develop a common framework encompassing several network contagion models and show that, regardless of the shock distribution and the network topology, precise ordering relationships on the level of aggregate systemic losses hold among models. We argue that the extent of contagion crucially depends on the amount of information that each model assumes to be available to agents. Under no uncertainty about the network structure and values of external assets, the well-known Eisenberg and Noe (2001) model applies, which delivers the lowest level of contagion. This is due to a property of loss conservation: aggregate losses after contagion are equal to the losses incurred by those institutions initially hit by a shock. This property implies that many contagion analyses rule out by construction any loss amplification, treating de facto an interconnected system as a single aggregate entity, where losses are simply mutualised. Under higher levels of uncertainty, as captured for instance by the DebtRank model, losses become non-conservative and get compounded through the network. This has important policy implications: by reducing the levels of uncertainty in times of distress (e.g. by obtaining specific data on the network) policymakers would be able to move towards more conservative scenarios. Empirically, we compare the magnitude of contagion across models on a sample of the largest European banks during the years 2006- 2016. In particular, we analyse contagion effects as a function of the size of the shock and the type of external assets shocked.

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