Unexpected shortfalls of Expected Shortfall: Extreme default profiles and regulatory arbitrage

Koch-Médina, Pablo; Munari, Cosimo

doi:10.1016/j.jbankfin.2015.11.006

Cited by 39 publications

(17 citation statements)

References 10 publications

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“…VaR is criticised for its lack of sub-additivity and it may create regulatory arbitrage in an insurance group (see Asimit, Badescu, & Tsanakas, 2013a ). A detailed discussion on possible regulatory arbitrages in a CVaR-based regime is provided in Koch-Medina and Munari (2016) . A desirable property for a risk measure is the elicitability , which allows one to compare competitive forecasting methods, a property that VaR does have (see Gneiting, 2011 ).…”

Section: Introductionmentioning

confidence: 99%

Robust and Pareto Optimality of Insurance Contracts

et al. 2017

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a b s t r a c tThe optimal insurance problem represents a fast growing topic that explains the most efficient contract that an insurance player may get. The classical problem investigates the ideal contract under the assumption that the underlying risk distribution is known, i.e. by ignoring the parameter and model risks. Taking these sources of risk into account, the decision-maker aims to identify a robust optimal contract that is not sensitive to the chosen risk distribution. We focus on Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR)-based decisions, but further extensions for other risk measures are easily possible. The Worst-case scenario and Worst-case regret robust models are discussed in this paper, which have been already used in robust optimisation literature related to the investment portfolio problem. Closed-form solutions are obtained for the VaR Worst-case scenario case, while Linear Programming (LP) formulations are provided for all other cases. A caveat of robust optimisation is that the optimal solution may not be unique, and therefore, it may not be economically acceptable, i.e. Pareto optimal. This issue is numerically addressed and simple numerical methods are found for constructing insurance contracts that are Pareto and robust optimal. Our numerical illustrations show weak evidence in favour of our robust solutions for VaR-decisions, while our robust methods are clearly preferred for CVaR-based decisions.

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Section: Introductionmentioning

confidence: 99%

Robust and Pareto Optimality of Insurance Contracts

et al. 2017

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“…However, as stated earlier, banking regulation is commonly a taboo topic, both in academic and in political discussions. Banks are regulated in order to protect shareholders and depositors alike ( Koch-Medina and Munari, 2016 ) but also because banking instability can have significant adverse effects to the economic environment ( Mcilroy, 2008 ). On the other hand, Dungey et al (2018) suggest that crisis contagion is unidirectional from the non-financial towards the financial sector only, which is a novel finding given the banking sector’s generally accepted role as a financier of economic growth.…”

Section: Literature Reviewmentioning

confidence: 99%

Economic stimulus through bank regulation: Government responses to the COVID-19 crisis

Polyzos

Samitas

Kampouris

2021

Journal of International Financial Markets, Institutions and Mo

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In this paper, we estimate the effects of the COVID-19 pandemic on the banking system and the real economy and simulate potential policy responses. We combine machine learning algorithms, namely a Random Regression Forest and a Long Short Term Memory neural network, with an agent-based framework to calculate the expected results of the pandemic, according to different scenarios regarding financial stability. We then simulate government responses to this crisis and find that traditional demand and supply stimuli are outperformed by our suggestion of relaxing bank regulation. We examine two alternatives of our suggested policy and find that they result in optimised outcomes for most variables examined. Our findings have important policy implications as authorities are formulating post-crisis recovery plans amidst budgetary constraints.

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“…However, the discussion about the best risk measure is still an open question and depends on the characteristics to be evaluated. For instance, in a recent work Koch‐Medina and Munari (2016) warn about the use of ES for capital adequacy and portfolio risk measurement since ES does not necessarily guarantee protection of liability holders.…”

Section: Literature Overviewmentioning

confidence: 99%

Backtesting expected shortfall for world stock indexETFswith extreme value theory andGram–Charliermixtures

2020

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This paper analyses risk quantification for three main stock market index exchange‐traded funds in world financial markets. We compare the relative performance of a set of parametric and semi‐nonparametric models in terms of both value‐at‐risk and expected shortfall backtesting techniques. To this end, we explore the result of the jointly elicitability of these two risk measures. We provide a new mixture of Gram–Charlier distributions that have been used in this framework for the first time and derive a close formula for directly computing expected shortfall. This model is compared to the Gaussian (benchmark model), Student's t, generalized Pareto (a case of the extreme value theory) and mixtures of Gram–Charlier distributions. The results show that peaks‐over‐threshold (extreme value theory) and flexible Gram–Charlier approximations are suitable to quantify market risk and mitigate concerns about possible financial instabilities generated by misuse of exchange‐traded funds trading.

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Unexpected shortfalls of Expected Shortfall: Extreme default profiles and regulatory arbitrage

Cited by 39 publications

References 10 publications

Robust and Pareto Optimality of Insurance Contracts

Robust and Pareto Optimality of Insurance Contracts

Economic stimulus through bank regulation: Government responses to the COVID-19 crisis

Backtesting expected shortfall for world stock indexETFswith extreme value theory andGram–Charliermixtures

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