Quantifying Systemic Risk: Conditional Interval Risk Measures and Their Applications

Risk-consistent conditional systemic risk measures

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.

In this paper, we study the effect of network structure between agents and objects on measures for systemic risk. We model the influence of sharing large exogeneous losses to the financial or (re)insurance market by a bipartite graph. Using Pareto-tailed losses and multivariate regular variation, we obtain asymptotic results for conditional risk measures based on the Value-at-Risk and the Conditional Tail Expectation. These results allow us to assess the influence of an individual institution on the systemic or market risk and vice versa through a collection of conditional risk measures. For large markets, Poisson approximations of the relevant constants are provided. Differences of the conditional risk measures for an underlying homogeneous and inhomogeneous random graph are illustrated by simulations.

The question of measuring and managing systemic risk—especially in view of the recent financial crisis—became more and more important. We study systemic risk by taking the perspective of a financial regulator and considering the axiomatic approach originally introduced in Chen et al. (Manag Sci 59(6):1373–1388, 2013) and extended in Kromer et al. (Math Methods Oper Res 84:323–357, 2016). The aim of this paper is to generalize the static approach in Kromer et al. (2016) and analyze systemic risk measures in a dynamic setting. We work in the framework of Cheridito et al. (Electron J Probab 11:57–106, 2006) who consider risk measures for bounded discrete-time processes. Apart from the possibility to consider the “evolution of financial values”, another important advantage of the dynamic approach is the possibility to incorporate information in the risk measurement and management process. In context of this dynamic setting we also discuss the arising question of time-consistency for our dynamic systemic risk measures.

Systemic risk refers to the risk that the financial system is susceptible to\nfailures due to the characteristics of the system itself. The tremendous cost\nof systemic risk requires the design and implementation of tools for the\nefficient macroprudential regulation of financial institutions. The current\npaper proposes a novel approach to measuring systemic risk.\n Key to our construction is a rigorous derivation of systemic risk measures\nfrom the structure of the underlying system and the objectives of a financial\nregulator. The suggested systemic risk measures express systemic risk in terms\nof capital endowments of the financial firms. Their definition requires two\ningredients: a cash flow or value model that assigns to the capital allocations\nof the entities in the system a relevant stochastic outcome; and an\nacceptability criterion, i.e. a set of random outcomes that are acceptable to a\nregulatory authority. Systemic risk is measured by the set of allocations of\nadditional capital that lead to acceptable outcomes. We explain the conceptual\nframework and the definition of systemic risk measures, provide an algorithm\nfor their computation, and illustrate their application in numerical case\nstudies.\n Many systemic risk measures in the literature can be viewed as the minimal\namount of capital that is needed to make the system acceptable after\naggregating individual risks, hence quantify the costs of a bail-out. In\ncontrast, our approach emphasizes operational systemic risk measures that\ninclude both ex post bailout costs as well as ex ante capital requirements and\nmay be used to prevent systemic crises.\n

Assessing the Risk Relevance of Accounting Variables in Dynamic Market Conditions

The measurement and determination of risk have received considerable attention in recent years. One measure of risk is systematic risk, defined in terms of the covariance of a security's return with the return from the market portfolio. The relationship is often standardized by dividing the covariance by the variance of return from the market portfolio. Hereafter, this measure of standardized systematic risk shall be referred to as beta.

PurposeBefore providing an overview of the conference with the above title and this Special Issue, this paper aims to present a view of the meaning of systemic risk, factors that affect systemic risk and measures of systemic risk. Thereafter, the conference presentations and the papers in this issue are summarized.Design/methodology/approachCharacteristics and measures of systemic risk are reviewed. Conference papers and presentations are summarized.FindingsWhile some aspects of systemic risk of a financial institution can be measured, an important aspect associated with contagion through markets is not easily captured by simple measures.Originality/valueThe conference and the papers in this issue contribute to the policy debate about sources and characteristics of systemic risk.

The purpose of this paper is to measure systemic risk of US financial institutions during and following the period of the subprime crisis. So, we estimated the systemic risk of a sample composed by 90 US financial institutions during the period from 2 January 2007 to 31 December 2014. We employ the SRISK as a measure of systemic risk. We estimate the systemic risk for each year. Based on the SRISK estimated, we try to present a classification of US financial institutions and we present the decomposition of systemic risk. The empirical results found that the total systemic risk supported by the US financial institutions is very high. In addition, the contribution of each institution in the risk of the financial system in the USA is very important. After the decomposition of systemic risk, we show that the institutions that take on more debt, contribute positively and highly to systemic risk.

PurposeMany studies have shown that from a theoretical and empirical point of view, downside risk-based measures of risk are better than the traditional ones. Despite academic appeal and practical implications, downside risk has not been thoroughly examined in markets outside developed country markets. Using downside beta as a measure of downside risk, this study examines the relationship between downside beta and stock returns in Indian equity market, an emerging market with unique investor, asset and market characteristics.Design/methodology/approachThis is an empirical study done by using ranked portfolio return analysis and regression analysis methodologies.FindingsThe study results show that downside risk, as measured by downside beta, is distinctly priced in the Indian equity market. There is a direct positive relationship between downside beta and contemporaneous realized returns, indicating a premium for downside risk. Downside risk carries a higher weightage than upside potential in the aggregate return of the stock portfolios. Downside beta is a better measure of systematic risk than conventional market beta and downside coskewness.Practical implicationsThe empirical results support the adoption of downside beta in practice and provide a case for replacing traditional beta with downside beta in asset pricing applications, trading and investment strategies, and capital allocation decision-making.Originality/valueThis is one of the first in-depth studies examining downside beta in Indian equity markets using a broad sample of individual stock returns covering a wide time range of 22 years. To the best of our knowledge, this study is the first one to compare downside beta and downside coskewness using individual stock data from the Indian equity market.

In this paper, we introduce two kinds of time consistent properties for set-valued dynamic risk measures for discrete-time processes that are adapted to a given filtration, named time consistency and multi-portfolio time consistency. Equivalent characterizations of multi-portfolio time consistency are deduced for normalized dynamic risk measures. In the normalized case, multi-portfolio time consistency is equivalent to the recursive form for risk measures as well as a decomposition property for the acceptance sets. The relations between time consistency and multi-portfolio time consistency are addressed. We also provide a way to construct multi-portfolio time consistent versions of any dynamic risk measure. Finally, we investigate the relationship about time consistency and multi-portfolio time consistency between risk measures for processes and risk measures for random vectors on some product space.

Optimal Scenario-Dependent Multivariate Shortfall Risk Measure and its Application in Capital Allocation

Since July 2000 PT. Bursa Efek Jakarta (BEJ) and PT. Danareksa Investment Management (DIM) launch the Jakarta Islamic Index (JII) which consist of all emitents with business activity complied to syariah law. Risk of investment, consist of systematic risk and non systematic risk, is also inevitable in securities trading of syariah stock. In investment analysis, non systematic risk is often neglected because of it’s characteristics that can be omitted by diversification, therefore the only risk which needs more attention is the systematic risk. This study specifically analyse the effect of return on equity, current ratio, debt ratio, operating leverage and asset growth variables on beta of syariah stock as the measurement of systematic risk in Bursa Efek Indonesia (BEI) in 2005 – 2008. Samples used in this study consist of companies which consistantly listing on the Jakarta Islamic Index (JII) within the period of the study. The result of five research hypothesis using multiple regression method shows that only asset growth variable has significant effect on beta of syariah stock at 5% significant effect, while return on equity, current ratio, debt ratio and operating leverage variables statistically proven that they do not have any significant effect on beta of syariah stocks.

Bank ownership, financial segments and the measurement of systemic risk: An application of CoVaR

Did the Euro Increase Systemic Risk?