In this short paper we briefly introduce some of the works presented during the conference “Network models and stress testing for financial stability” which was held in Mexico City hosted by Banco de Mexico on September 26-27, 2017. The papers ranged from new applications of network models for financial stability analysis to stress testing at central banks and common assets contagion. The novelty and originality of most of the works make this special issue a good read for the specialists in the Financial Stability field, including academics as well as practitioners and obviously financial authorities. We hope that you find this short introduction to the special issue and the special issue itself, interesting and useful for your everyday work.

Network models, stress testing methods and early warning systems are attracting growing interest both among scholars and practitioners. In this short paper, we illustrate some of the insights they have to offer both in terms of new fundamental scientific understanding of the emergence systemic risk and in terms of concrete applications to the policy areas of financial stability and macro-prudential policy. Finally, we discuss new research pathways to address the challenging questions still open, including multiplex networks, big financial data, and climate-finance.

How financial investors may react to policy events related to sustainability and climate change mitigation in particular, is a key question with implications for sustainable finance and financial stability. We address this question by carrying out a multi-period difference-in-difference approach on a confidential database of securities holdings of the European Central Bank, and we provide evidence of several effects related to the Paris Agreement. In aggregate, investors reduced their participation in the equities of high-carbon firms in response to the agreement, and the trend reverted after the US’s announcement of withdrawal from the agreement. However, the reaction varies across categories and geographies of the securities holders, their ownership size, and the emissions of owned firms. In particular, transition risk has been taken up by less regulated financial institutions and the BRIC countries. Our results highlight that the redirection of global financial flows towards climate action requires clear and unanimous signals from the global community of policy makers.

In this cover paper, we introduce a Special Issue (SI) published after the fourth edition of a series of financial stability conferences organized by Bank of Mexico, CEMLA, Bank of Canada, Zurich University and the Journal of Financial Stability in November 2021. Before providing our perspective on why the research papers included into the SI are of great relevance, we give a brief and personal overview of recent directions in financial stability research in general, esp., related to topics accentuated by the COVID-19 pandemic or post-pandemic economic and financial conditions and their complexity. Papers published in the SI cover four topics of research in the financial stability field, featuring some outstanding and innovative projects presented during the conference. The first topic is on interconnectedness and shock transmission in the financial system, diving deep into asset fire sales, interconnectedness of various segments of the financial system, in addition to banks, on the optimality of systemic risk capital buffers, and on how risks are priced in the interbank market network. The second one touches upon climate change risks looking at investors’ reactions to international climate policy developments, in particular on the Paris Agreement front and how to jointly model physical and transition risk in the banking system, including the important concept of double materiality. The third topic is represented by projects focused on policy analysis for systemic risk mitigation, specifically dealing with macroprudential policy instruments and crisis mitigation policies. Finally, research papers in the last topic on big data and market data focus on the innovative ways to explore the growing body of data sources, such as data collected by regulators, including credit register data, supervisory data and market data on financial transactions, to better understand sources and implications of systemic risk.

Firms’ green technology investments are increasingly important for competitive positioning, yet their impact on long-run performance remains unclear. Using electricity capacity data for global utility firms, we construct a unique structural measure of technological greenness based on renewable versus fossil fuel physical infrastructure. We find that adopting sustainable technologies is followed by a long-run improvement in corporate fundamentals that is only partially reflected in stock prices, yielding superior returns over a multi-year horizon. These results partly reflect higher revenue growth in liberalized retail markets rather than investor sentiment, revealing a consumer-based mechanism that likely extends to competitive downstream sectors.

We analyze the effects on financial stability of the interplay between climate transition risk and market conditions, such as recovery rate and asset price volatility. To this end, we extend the framework of the climate stress-test of the financial system by including an ex-ante network valuation of financial assets which accounts for asset price volatility as well as for endogenous recovery rate on interbank assets. Moreover, we also consider the dynamics of indirect contagion of banks and investment funds, which are key players in the low carbon transition, via exposures to the same asset classes. We derive some analytical results and we apply the model to a unique supervisory dataset in a range of climate policy scenarios and market conditions. In the event of a disorderly low-carbon transition, stronger market conditions allow to reach more ambitious climate policies at the same level of financial risk.

Climate change has been recently recognised as a new source of risk for the financial system. Over the last years, several central banks and financial supervisors have recommended investors and financial institutions to assess their exposure to climate-related financial risks. Central banks and financial supervisors have also started to design scenarios for climate stress tests - to- assess how vulnerable the financial system is to climate change. Nevertheless, the financial community falls short of methodologies that allow the successful analysis of the risks that climate change poses to financial stability. Indeed, the characteristics of climate risks (i.e., deep uncertainty, non-linearity and endogeneity) challenge traditional approaches to macroeconomic and financial risk analysis. Embedding climate change in macroeconomic and financial analysis using innovative perspectives is fundamental for a comprehensive understanding of the macrofinancial relevance of climate change. This Special Issue is devoted to the relation between climate risks and financial stability and represents the first comprehensive attempt to fill methodological gaps in this area and to shed light on the financial implications of climate change. It includes original contributions that use a range of methodologies – such as network modelling, dynamic evolutionary macroeconomic modelling and financial econometrics – to analyse climate-related financial risks and the implications of financial policies and instruments aiming at the low-carbon transition. The research insights of these contributions can inform the decisions of central banks and financial supervisors about the integration of climate change considerations into their policies and financial risk assessment.

Financial networks have shown to be important in understanding systemic events in credit markets. In this paper, we investigate how the structure of those networks can affect the capacity of regulators to assess the level of systemic risk. We introduce a model to compute the individual and systemic probability of default in a system of banks connected in a generic network of credit contracts and exposed to external shocks with a generic correlation structure. Even in the presence of complete knowledge, we identify conditions on the network for the emergence of multiple equilibria. Multiple equilibria give rise to uncertainty in the determination of the default probability. We show how this uncertainty can affect the estimation of systemic risk in terms of expected losses. We further quantify the effects of cyclicality, leverage, volatility and correlations. Our results are relevant to the current policy discussions on new regulatory framework to deal with systemic events of distress as well as on the desirable level of regulatory data disclosure.

We develop a framework to analyse the credit default swap (CDS) market as a network of risk transfers among counterparties. From a theoretical perspective, we introduce the notion of flow-of-risk and provide sufficient conditions for a bow-tie network architecture to endogenously emerge as a result of intermediation. This architecture shows three distinct sets of counterparties: (i) Ultimate Risk Sellers (URS), (ii) Dealers (indirectly connected to each other), (iii) Ultimate Risk Buyers (URB). We show that the probability of widespread distress due to counterparty risk is higher in a bow-tie architecture than in more fragmented network structures. Empirically, we analyse a unique global dataset of bilateral CDS exposures on major sovereign and financial reference entities in 2011–2014. We find the presence of a bow-tie network architecture consistently across both reference entities and time, and that the flow-of-risk originates from a large number of URSs (e.g. hedge funds) and ends up in a few leading URBs, most of which are non-banks (in particular asset managers). Finally, the analysis of the CDS portfolio composition of the URBs shows a high level of concentration: in particular, the top URBs often show large exposures to potentially correlated reference entities.

We explore the dynamics of default cascades in a network of credit interlink-ages in which each agent is at the same time a borrower and a lender. When some counterparties of an agent default, the loss she experiences amounts to her total exposure to those counterparties. A possible conjecture in this context is that individual risk diversification across more numerous counterparties should make also systemic defaults less likely. We show that this view is not always true. In particular, the diversification of credit risk across many borrowers has ambiguous effects on systemic risk in the presence of mechanisms of loss amplifications such as in the presence of potential runs among the short-term lenders of the agents in the network.