How reliable are systemic risk measures? Model risk estimates of MES and

In recent years, it has become apparent that an isolated microprudential approach to capital adequacy requirements of individual institutions is insufficient. It can increase the homogeneity of the financial system and ultimately the cost to society. For this reason, the focus of the financial and mathematical literature has shifted toward the macroprudential regulation of the financial network as a whole. In particular, systemic risk measures have been discussed as a risk measurement and mitigation tool. In this spirit, we adopt a general approach of multivariate, set-valued risk measures and combine it with the notion of intrinsic risk measures. In order to define the risk of a financial position, intrinsic risk measures utilize only internal capital, which is received when part of the currently held assets are sold, instead of relying on external capital. We translate this methodology into the systemic framework and show that systemic intrinsic risk measures have desirable properties such as the set-valued equivalents of monotonicity and quasi-convexity. Furthermore, for convex acceptance sets we derive a dual representation of the systemic intrinsic risk measure. We apply our methodology to a modified Eisenberg–Noe network of banks and discuss the appeal of this approach from a regulatory perspective, as it does not require to inject external capital into the system. We show evidence that this approach allows to mitigate systemic risk by moving the network toward more stable assets.

The beauty contest between systemic and systematic risk measures: Assessing the empirical performance

At present, the measurement of systemic risk is still a worldwide challenge. The complex network theory provides a new perspective for the study of this problem. Based on the correlation coefficient between the banks calculated using their default probabilities, this paper builds China's banking networks for the periods of 2008-2019, and analyzes systematically the topological structure of the networks, and determine the size of the systemic risk from the perspective of network topology by using the corresponding characteristics of complex network with the feature of systemic financial risk. It is found that the systemic risk of China's banking industry has a declined tendency before 2018, and the main cause is due to the eigenvector centrality and clustering coefficient declined rapidly. However, after 2018, systemic risk showed a litter upward trend, and the increase of clustering coefficient and eigenvector centrality was the main reason for that upward trend. Before 2018, risk transmission was mainly taken place from local banks and joint-equity commercial banks to state-owned banks, which were the main risk bearers. After 2018, risk contagion mainly occurred among local banks, and some local banks role as systemically important ones. Therefore, dissolving the systemic financial risk in China should strengthen the regulation of local banks. In particular, the high-risk leverage operations and excessively innovative business should be strictly supervised so as to prevent the expansion and spread of the negative effects stemmed from maturity mismatch, maturity transformation and credit transformation.

This article presents an analysis of the literature on systemic risk measurement methods. Only the recent global crisis has particularly attracted the attention of researchers on systemic risk measurement. Global challenges such as Big Data, AI, IoF, etc. also have an impact on expanding the systemic risk measurement capabilities. The growing number of publications in the last decade opens the door to deeper insights into the systemic risk measurement features, summarizing the contribution of research and analyse the mainstream research on systemic risk, identify the strengths and weaknesses of the studies. Therefore, the main objective of this study is to provide a framework to address the relevant gaps in the current discussion on systemic risk measurement by conducting a wide search in Scopus database to identify the studies that used different systemic risk measurement in the period from 2009 to January 2018. A meta-analysis of scientific articles is performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method and using network approach presents the main interconnection of the methods used to measure systemic risk. A critical analysis of these articles addresses some important key issues. The results of this review are important: they will help researchers to develop better research methods and models around systemic risk measurement. Based on the results, it has allowed us to identify the key issues in choosing a method to assess systemic risk and to help researchers avoid pitfalls in using these methods.

In the realm of portfolio management, the focus lies on constructing a well-diversified portfolio to mitigate unsystematic risk, allowing for the identification and measurement of systematic risk e.g. through uni-factor models, such as CAPM, and multi-factor models, such as APT. This approach is rooted in the belief that, with a sufficiently diversified portfolio, unsystematic risk in theory can be eliminated, making the remaining systematic risk more apparent. While diversification is the means to diversify the unsystematic risk in a portfolio management problem, pooling strategies, with a limited strategy of just expanding the pool members, necessitate a distinct approach to systematic risk. In such scenarios, the challenge lies in disentangling the impact of systematic factors from idiosyncratic influences within a pool. This paper explores the methodologies and considerations unique to pooling situations, shedding light on the complexities involved in identifying and quantifying systematic risk in a pool. In our effort to assess the concept of systematic risk in a pool, we adopt an approach that identifies the defining characteristics of systematic risks, which remain invariant regardless of the number of losses or any manipulations within a finite set of losses. To explore these principles, we find a framework of risk management on sequences in Banach lattices to be particularly suitable. In establishing these principles, we introduce the notion of “systematic compatibility”, signifying invariance to variations in finite changes within a sequence of losses. Consequently, we observe that while systematic risk often possesses an implicit representation in the risk space, it exhibits an explicit representation in the bi-dual space. Moreover, we introduce systematic compatible risk measures and establish their dual characterization. We demonstrate that risk measurement can naturally be represented as a split into a summation of systematic and unsystematic components. In practical applications, we employ these measures to address risk management problems, with a specific emphasis on risk pooling scenarios. In revisiting the traditional “principle of insurance” (POI), we propose an extension called the “principle of pooling” (POP). By showing that the principle of pooling holds if and only if the systematic risk is secure, we investigate this novel concept.

This dissertation provides a study on systemic risk in financial markets; it is laid out as follows. Chapter 1 provides a survey of the quantitative measure of systemic risk in the economics and finance literature. In Chapter 2 examine, using conditional VaR (CoVaR), the systemic risk generated by major Spanish financial institutions in the recent global financial crisis and the European sovereign debt crisis as a systemic risk measure. CoVaR was quantified using quantile regression, multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) and copula approaches. We also describe a novel copula-based approach to computing the CoVaR value, given that copula are flexible modellers of joint distribution and are particularly useful for characterizing the tail behaviour that provides such crucial information for the CoVaR. We found significant increases in systemic risk around the time of the recent global financial crisis and, to a lesser extent, around the time of the European debt crisis. Our evidence also shows that the quantile regression approach was unable to reflect the dynamics of, and sudden changes in, systemic risk. These results have implications for capital regulation in financial institutions and on how systemic risk should be measured. In chapter 3 we study systemic risk in European sovereign debt markets before and after the onset of the Greek debt crisis, taking, as a systemic risk measure, the CoVaR as characterized and computed using copulas. We found sovereign debt markets to be coupled before the debt crisis and systemic risk to be similar for all countries. With the onset of the Greek crisis, debt markets decoupled and the systemic risk of the countries in crisis (excepting Spain) decreased whereas that of the non-crisis countries increased slightly. The systemic risk of the Greek debt market increased for other countries in crisis, especially for Portugal (where systemic risk tripled after the onset of the crisis) and decreased for non-crisis countries. In Chapter 4 we investigated - using the CoVaR measure as characterized and computed using copulas and vine copulas - systemic sovereign debt distress in European domestic financial systems and the systemic risk of a potentially distressed Greek debt market for other European financial systems countries before and after the onset of the recent financial and debt crises. We found that, before the debt crisis, sovereign debt had a positive systemic risk on European domestic financial systems. However, with the onset of the Greek crisis, the systemic impact of sovereign debt increased for countries in crisis (Greece, Italy and Portugal) whereas it remained stable or reduced for non-crisis countries. Regarding the systemic impact of sovereign Greek debt distress, our evidence indicates that negative impacts were limited to a small set of countries (Belgium, Italy, the Netherlands and Portugal).

Purpose The paper presents a new method that quantifies environmental risk in systemic risk measurement based on the exposure approach using an existing E-score as the source of information about bank exposure to environmental risks. Our method allows us to base the impact of environmental risk exposure on individual characteristics of banks and their systemic risk levels. Design/methodology/approach We extract the environmental factor (E-factor) from each bank's environmental score (part of the ESG score) and augment systemic risk measurement with it. We apply econometric systemic risk models to quantify systemic risk, and for each, we add the E-factor using a conditional sensitivity function. We demonstrate our method empirically on two systemic risk models: CoVaR and SRISK, using a sample of 20 systemically important European banks from 12 European countries between 2007 and 2023. Findings Our method captures a bigger impact of the environmental risk factor in periods of instability. Moreover, the E-factor records higher impacts on more fragile banks. This observation holds equally for banks from developed and emerging countries, regardless of whether they are global or local systemically important financial institutions. With the E-CoVaR and E-SRISK rankings constructed, we illustrate the contrasts between Western Europe and the CEE region. Higher environmental risk is quantified for the latter, with Russian, Romanian and Polish banks at the bottom of the environmental risk exposure ranking. Research limitations/implications The presented risk quantification methods are universal in the technical sense and applicable to other systemic risk measures and other environmental scores, while the ranking methods may be of value for the regulators as they allow them to identify the banks that are most prone to losses based on their systemic-risk-based environmental exposure. Practical implications Regulators and financial institutions can leverage the proposed ranking methods to identify environmentally vulnerable banks, encouraging them to implement more targeted interventions to mitigate climate-related financial risks. Enhanced monitoring of weak links and exposures within the banking sector can help regulators anticipate systemic disruptions and require banks to strengthen buffers against climate-induced shocks. Social implications Over the long term, this research could influence regulatory frameworks by encouraging the integration of climate risk considerations into financial stability assessments, ultimately reducing spillover effects and systemic crises that produce significant environmental and social costs. Originality/value The paper addresses a research gap by proposing a novel method of environmental risk measurement and its application to, inter alia, the CEE region.

This paper improves on the DebtRank model by incorporating a capital buffer. We use data from China’s banking industry to compare the systemic risk measured by the original, improved and differential DebtRank models. Our results show that the relationship between the systemic risk and initial shock varies over the three models. When the initial shock is small, the systemic risk measured by the improved Debt- Rank is the smallest, followed by the original DebtRank, while the systemic risk measured by the differential DebtRank is the largest. When the initial shock exceeds a certain threshold, the systemic risk measured by the original DebtRank is the smallest, followed by the improved DebtRank, while the systemic risk measured by the differential DebtRank is the largest. This shows that, when the risk shock is small, the existence of a capital buffer inhibits risk contagion and thus reduces systemic risk. However, as the risk shock increases, the role of the capital buffer in reducing risk contagion reduces. The improved model measures systemic risk more accurately than both the original and differential DebtRank models.

Systemic risk refers to the risk that the financial system is susceptible to failures due to the characteristics of the system itself. The tremendous cost of systemic risk requires the design and implementation of tools for the efficient macroprudential regulation of financial institutions. The current paper proposes a novel approach to measuring systemic risk. Key to our construction is a rigorous derivation of systemic risk measures from the structure of the underlying system and the objectives of a financial regulator. The suggested systemic risk measures express systemic risk in terms of capital endowments of the financial firms. Their definition requires two ingredients: a cash flow or value model that assigns to the capital allocations of the entities in the system a relevant stochastic outcome; and an acceptability criterion, i.e. a set of random outcomes that are acceptable to a regulatory authority. Systemic risk is measured by the set of allocations of additional capital that lead to acceptable outcomes. We explain the conceptual framework and the definition of systemic risk measures, provide an algorithm for their computation, and illustrate their application in numerical case studies. Many systemic risk measures in the literature can be viewed as the minimal amount of capital that is needed to make the system acceptable after aggregating individual risks, hence quantify the costs of a bail-out. In contrast, our approach emphasizes operational systemic risk measures that include both ex post bailout costs as well as ex ante capital requirements and may be used to prevent systemic crises.

Aim. The presented study aims to analyze existing approaches and develop recommendations for the numerical assessment of the magnitude of risk associated with the implementation of investment projects.Tasks. The authors describe traditional approaches to systematic risk assessment; systematize the practice of determining the beta coefficient as a risk measure of investment projects; analyze alternative approaches to assessing discount rate and systematic risk; develop recommendations for leveling the systematic risk measure.Methods. This study uses general scientific methods of analysis and synthesis, induction and deduction, comparison and description, as well as special methods of financial mathematics and economic-mathematical modeling.Results. Determining the investment attractiveness of a business or project involves forecasting and discounting future cash flows. Obvious risks are accounted for by adjusting cash flows, and most other risks and uncertainties are reflected in the discount rate. One of the specific components of the discount rate is a measure of systematic risk – the beta coefficient. The authors consider the traditional approach to assessing the cost of capital and systematic risk, alternative approaches that have methodological advantages, but are often not applicable in practice due to additional difficulties in calculations, and most importantly – in comparison between projects/ companies/results for different settlement dates. The study also provides a critical analysis of publicly available analytical data through the example of A. Damodaran’s information and analytical resource and highlights problems in the use of statistics for the valuation of projects and companies in the long term due to the significant volatility of the beta coefficient.Conclusions. In the absence of other reliable sources, analysts should be more careful about the values of published analytical materials, in some cases independently rechecking the data of publicly available analytics using the recommendations presented in this study.

We derive several popular systemic risk measures in a common framework and show that they can be expressed as transformations of market risk measures (e.g., beta). We also derive conditions under which the different measures lead to similar rankings of systemically important financial institutions (SIFIs). In an empirical analysis of US financial institutions, we show that (1) different systemic risk measures identify different SIFIs and that (2) firm rankings based on systemic risk estimates mirror rankings obtained by sorting firms on market risk or liabilities. One-factor linear models explain most of the variability of the systemic risk estimates, which indicates that systemic risk measures fall short in capturing the multiple facets of systemic risk.

The recent financial crisis has brought the issue of banking system ‘stress tests’ to the fore. This paper describes recent progress in the area of systemic risk modelling and measurement and discusses how the results of such analyses are helping shape the practical framework for macroprudential policy and bank stress testing. It also considers how liquidity regulations on banks, such as the core funding ratio implemented by the Reserve Bank of New Zealand, can affect the probability and potential impact of shocks to the financial system.

In view of the recent financial crisis systemic risk has become a very important research object. It is of significant importance to understand what can be done from a regulatory point of view to make the financial system more resilient to global crises. Systemic risk measures can provide more insight on this aspect. The study of systemic risk measures should support central banks and financial regulators with information that allows for better decision making and better risk man- agement. For this reason this paper studies systemic risk measures on locally convex-solid Riesz spaces. In our work we extend the axiomatic approach to systemic risk, as introduced in Chen et al. (2013), in different directions. One direction is the introduction of systemic risk measures that do not have to be positively homogeneous. The other direction is that we allow for a general measurable space whereas in Chen et al. (2013) only a finite probability space is considered. This extends the scope of possible loss distributions of the components of a financial system to a great extent and introduces more flexibility for the choice of suitable systemic risk measures.

In view of the recent financial crisis systemic risk has become a very important research object. It is of significant importance to understand what can be done from a regulatory point of view to make the financial system more resilient to global crises. Systemic risk measures can provide more insight on this aspect. The study of systemic risk measures should support central banks and financial regulators with information that allows for better decision making and better risk management. For this reason this paper studies systemic risk measures on locally convex-solid Riesz spaces. In our work we extend the axiomatic approach to systemic risk, as introduced in Chen et al. (Manag Sci 59(6):1373–1388, 2013), in different directions. One direction is the introduction of systemic risk measures that do not have to be positively homogeneous. The other direction is that we allow for a general measurable space whereas in Chen et al. (2013) only a finite probability space is considered. This extends the scope of possible loss distributions of the components of a financial system to a great extent and introduces more flexibility for the choice of suitable systemic risk measures.

This paper studies the exposure and contribution of financial institutions to systemic risks in financial markets. We employ three popular indicators of a financial institution’s exposure to systemic risks: the systemic risk index (SRISK) and marginal expected shortfall (MES) of Brownlees and Engle (Volatility, correlation and tails for systemic risk measurement, Social Science Research Network, Rochester, NY, 2012) and the conditional Value-at-Risk (CoVaR) of Adrian and Brunnermeier (2011). We use a primary database of Taiwan financial institutions for our empirical study. A panel contains data of stock market returns and balance sheets of 31 Taiwan financial institutions for 2005–2014. We focus on systemic risk analysis so as to understand the dynamics of volatility, interdependency, and risk during the recent financial crisis. We then report the time series dynamics and cross sectional rankings of these systemic risk measures. The main results indicate that although these three measures differ in their definition of the contributions to systemic risk, all are quite similar in identifying systemically important financial institutions (SIFIs). Moreover, we find empirical evidence that systemic risk contributions are closely related to certain institution characteristic factors. The results of the Granger causality tests prove that a systemic risk measure is a great alternative tool for monitoring early warning signals of distress in the real economy.