Managing systemic risk in The Netherlands

What Do We Know About the Effects of Macroprudential Policy?

“…Using data on individual banks' loan books, risk exposures, and interbank linkages including over-the-counter derivatives for the Canadian banking system, Gauthier et al (2012) find that macroprudential capital allocation mechanisms reduce default probabilities of individual banks as well as the probability of a systemic crisis by about 25%, suggesting that macroprudential capital buffers can substantially improve financial stability. Liao et al (2015) find for the Netherlands that macroprudential capital requirements under five systemic risk-allocation frameworks substantially reduce banks' default probabilities, and that capital requirements based on DCoVaR allocation lead to the lowest default probabilities, with the average probability of bank defaults decreasing from 6.49% to 5.39%, a 17% reduction in risk. They also find that macroprudential capital requirements decrease the risk of multiple bank failures significantly, with the probability of three or four bank defaults decreasing from 4.22% to 3.09% based on Shapley value VaR allocation, corresponding to a 26% decrease in the risk of a financial crisis.…”

“…Liao et al . () find for the Netherlands that macroprudential capital requirements under five systemic risk‐allocation frameworks substantially reduce banks’ default probabilities, and that capital requirements based on ΔCoVaR allocation lead to the lowest default probabilities, with the average probability of bank defaults decreasing from 6.49% to 5.39%, a 17% reduction in risk. They also find that macroprudential capital requirements decrease the risk of multiple bank failures significantly, with the probability of three or four bank defaults decreasing from 4.22% to 3.09% based on Shapley value VaR allocation, corresponding to a 26% decrease in the risk of a financial crisis.…”

Market-Implied Systemic Risk and Shadow Capital Adequacy

“…Most institution-level measures of systemic risk have focussed on empirical, bivariate approaches with an implicit or explicit treatment of statistical dependence (using the historical dynamics of market prices) to determine the joint risk or expected losses based on the assumed directionality of systemic risk propagation: (i) for the contribution approach -Conditional Value-at-Risk (CoVaR) 2 (Adrian and Brunnermeier (2016)) and related extensions, such as Component/Incremental VaR (Liao et al (2015)), Co-Risk (Giudici and Parisi (2016)), and copula-based CoVaR (Reboredo and Ugolini (2015); Reboredo and Ugolini (2016); Karimalis and Nomikos (2018), which show the marginal contribution of firms to system-wide losses during times of individual stress, and (ii) for the participation approach -Systemic Expected Shortfall (SES) (Acharya et al (2010)) and related extensions, such as Component Expected Shortfall (CES) (Banelescu and Dumitrescu (2013)) and Systemic Capital Shortfall/SRISK (Brownlees and Engle (2011)), which show the potential losses of individual banks in response to a large drop in overall market capitalisation of the aggregate banking sector during times of stress. 3 , 4 However, only a few models have incorporated a multivariate 5 perspective using historically informed measures of association, such as Multiple Conditional VaR (MCoVaR) and Multiple Conditional Expected Shortfall (MCoES) (Bernardi et al (2017)), and all of them define default based on a pre-specified threshold of negative shocks to market returns rather than a structural definition of default.…”

Macroprudential Regulation for the Chinese Banking Network System with Complete and Random Structures

“…These two scholars mainly studied systemic risk without the macro-prudential regulation for banking systems. Gauthier et al [12] and Liao et al [13] calculated each bank's macro-prudential capital, according to its contribution to systemic risk, but they did not take into account the impact of the network structure. Therefore, the present paper considers the macro-prudential regulation focus on capital requirements for a Chinese banking system with complete and random network structures quantitatively.…”

“…We use the dynamic model to estimate the specific bilateral exposures in the Chinese interbank market with complete and random networks and the dynamic evolution of banks' balance sheet based on the actual data of 16 listed banks during 2010-2015. Then, we construct a quantitative model of macro-prudential regulation for the Chinese banking network system based on Gauthier et al [12] and Liao et al [13], where we try to explore the method of macro-prudential regulation for the Chinese banking network system using four risk allocation mechanisms (Component VaR, Incremental VaR, Shapley value EL, ∆CoVaR), and then make a comparative analysis on the regulation effect under different risk allocation mechanisms in the two network structures, and put forward some regulation suggestions for Chinese-relevant regulatory agencies.…”