Identification of systemically important banks in India using SRISK

Purpose Systemic risk has been a cause of concern for the bank regulatory authorities worldwide since the global financial crisis. This study aims to identify systemically important banks (SIBs) in India by using SRISK to measure the expected capital shortfall of banks in a systemic event. The sample size comprises a balanced data set of 31 listed Indian commercial banks from 2006 to 2019. Design/methodology/approach In this study, the authors have used SRISK to identify banks that have a maximum contribution to the systemic risk of the Indian banking sector. Leverage, size and long-run marginal expected shortfall (LRMES) are used to compute SRISK. Forward-looking LRMES is computed using the GJR-GARCH-dynamic conditional correlation methodology for early prediction of a bank’s contribution to systemic risk. Findings This study finds that public sector banks are more vulnerable to macroeconomic shocks owing to their capital inadequacy vis-à-vis the private sector banks. This study also emphasizes that size should not be used as a standalone factor to assess the systemic importance of a bank. Originality/value Systemic risk has attracted a lot of research interest; however, it is largely limited to the developed nations. This paper fills an important research gap in banking literature about the identification of SIBs in an emerging economy, India. As SRISK uses both balance sheet and market-based information, it can be used to complement the existing methodology used by the Reserve Bank of India to identify SIBs.

Modelling Systemically Important Banks vis-à-vis the Basel Prudential Guidelines

Our paper investigates Indonesia’s systemically important banks (SIBs) using theoretical approaches—CoVaR, marginal expected shortfall (MES), and SRISK—to compare with the Basel guidelines as benchmark. We use Indonesian banks’ market and supervisory data over the 2008–2019 period. The research aims to seek intertheoretical model interaction and SIB ranking in concordance with the Basel guidelines as applied by a bank supervisor. The findings show that SRISK produced a more consistent ranking compared with CoVaR and MES. CoVaR and MES had higher intermodel correlation converted to 59% similarity in rankings. Further, all theoretical models are in line with the Basel guidelines, where the closest approximation is at 47%. The results indicate that policy makers could use scholarly models as validation tools and help improve supervision decision to identify systemically important institutions.

Modeling System Risk in the South African Insurance Sector: A Dynamic Mixture Copula Approach

In this paper, a dynamic mixture copula model is used to estimate the marginal expected shortfall in the South African insurance sector. We also employ the generalized autoregressive score model (GAS) to capture the dynamic asymmetric dependence between the insurers’ returns and the stock market returns. Furthermore, the paper implements a ranking framework that expresses to what extent individual insurers are systemically important in the South African economy. We use the daily stock return of five South African insurers listed on the Johannesburg Stock Exchange from November 2007 to June 2020. We find that Sanlam and Discovery contribute the most to systemic risk, and Santam turns out to be the least systemically risky insurance company in the South African insurance sector. Our findings defy common belief whereby only banks are systemically risky financial institutions in South Africa and should undergo stricter regulatory measures. However, our results indicate that stricter regulations such as higher capital and loss absorbency requirements should be required for systemically risky insurers in South Africa.

Modeling System Risk in the South African Insurance sector: A Dynamic Mixture Copula Approach

In this study, a dynamic mixture copula is used to estimate the marginal expected shortfall in the South African insurance sector. While other studies assumed nonlinear dependence to be static over time, our model capture time-varying nonlinear dependence between institutions and the market. In order to capture time-varying nonlinear dependence, the generalized autoregressive score (GAS) is used to model the dynamic copula parameters. Furthermore, our study implements a ranking that expresses to what degree individual insurers are systemically important in South Africa. We use daily stock return of five South African insurers listed in the Johannesburg Stock Exchange (JSE) from November 13, 2007 to June 15, 2020. We find that Sanlam and Discovery contribute the most to systemic risk, while Santam is found to be the least contributor to the overall systemic risk in the South African insurance sector. Our findings would be of paramount importance for the South African regulators as they would be informed that not only banks are systemically important, but some insurers also are systemically important financial institutions. Hence, stricter regulation of these institutions in the form of higher capital and loss absorbency requirements could be required based on the individual business activities undertaken by the company.

A Riskmas Carol

Do you believe in Santa Claus (rally)? This study investigates the existence of the ‘Santa Claus rally’ in bank systemic risk. Christmas rally describes a persistent rise in the stock market during the final week of December through the first two trading days in January. In this article, we evaluate this calendar effect, focusing on systemic risk measures for global systemically important banks (GSIBs). First, we estimate the three popular systemic risk measures (DCoVaR, marginal expected shortfall [MES] and SRISK), and then we use an event study approach to analyse the reaction of risk. The results support the existence of Santa Claus. We find that the arrival of Santa Claus has a positive effect on systemic risk, that is, a reduction in bank systemic risk.

Bank regulation and systemic risk: cross country evidence

Using data for banks from 65 countries for the period 2001–2013, we investigate the impact of bank regulation and supervision on individual banks’ systemic risk. Our cross-country empirical findings show that bank activity restriction, initial capital stringency and prompt corrective action are all positively related to systemic risk, measured by Marginal Expected Shortfall. We use the staggered timing of the implementation of Basel II regulation across countries as an exogenous event and use latitude for instrumental variable analysis to alleviate the endogeneity concern. Our results also hold for various robustness tests. We further find that the level of equity banks can alleviate such effect, while bank size is likely to enhance the effect, supporting our conjecture that the impact of bank regulation and supervision on systemic risk is through bank’s capital shortfall. Our results do not argue against bank regulation, but rather focus on the design and implementation of regulation.

A Bayesian Entropy Approach to Sectoral Systemic Risk Modeling

We investigate the dynamics of systemic risk of European companies using an approach that merges paradigmatic risk measures such as Marginal Expected Shortfall, CoVaR, and Delta CoVaR, with a Bayesian entropy estimation method. Our purpose is to bring to light potential spillover effects of the entropy indicator for the systemic risk measures computed on the 24 sectors that compose the STOXX 600 index. Our results show that several sectors have a high proclivity for generating spillovers. In general, the largest influences are delivered by Capital Goods, Banks, Diversified Financials, Insurance, and Real Estate. We also bring detailed evidence on the sectors that are the most pregnable to spillovers and on those that represent the main contributors of spillovers.