Policy Lessons from Systemic Risk Modeling and Measurement

2017 ◽  
pp. 239-273
Author(s):  
Arjen Siegmann
Keyword(s):  
Systemic Risk ◽  
Risk Modeling

scholarly journals Systemic Risk Modeling

2020 ◽  
Vol 20 (54) ◽  
Author(s):  
Raphael Espinoza ◽  
Miguel Segoviano ◽  
Ji Yan
Keyword(s):  
Theoretical Model ◽  
Empirical Model ◽  
Systemic Risk ◽  
Empirical Models ◽  
Equity Returns ◽  
Counterparty Risk ◽  
Risk Modeling ◽  
Cross Sectional ◽  
Fire Sales ◽  
Multivariate Density

We propose a framework to link empirical models of systemic risk to theoretical network/ general equilibrium models used to understand the channels of transmission of systemic risk. The theoretical model allows for systemic risk due to interbank counterparty risk, common asset exposures/fire sales, and a “Minsky" cycle of optimism. The empirical model uses stock market and CDS spreads data to estimate a multivariate density of equity returns and to compute the expected equity return for each bank, conditional on a bad macro-outcome. Theses “cross-sectional" moments are used to re-calibrate the theoretical model and estimate the importance of the Minsky cycle of optimism in driving systemic risk.

scholarly journals Measuring Systemic Risk in the Polish Banking System by Means of the Risk-Based Balance Sheets Method

2012 ◽  
Vol 12 (2) ◽  
pp. 7-18 ◽  
Author(s):  
Renata Karkowska
Keyword(s):  
Systemic Risk ◽  
Global Financial Crisis ◽  
Balance Sheet ◽  
Banking System ◽  
Risk Modeling ◽  
Management Tools ◽  
Balance Sheets ◽  
New Directions ◽  
The Global Financial Crisis ◽  
New Framework

Abstract The complex connections, spillovers and feedbacks of the global financial crisis remind how important it is to improve the analysis of risk modeling. This article introduces a new framework for mitigating systemic risk by using a risk-adjusted balance sheet approach. In this regard, the analysis of individual banks in Poland shows potential risk which could threaten all the financial system. Traditional banking models do not adequately measure risk position of financial institutions and cannot be used to understand risk within and between balance sheets in the financial sector. A fundamental subject is that accounting balance sheets do not indicate risk exposures, which are forward-looking. The paper concludes new directions for measuring systemic risk by using Merton’s model. It shows how risk management tools can be applied in new ways to measure and analyze systemic risk in the Polish banking system.

scholarly journals Systemic Risk Modeling with Lévy Copulas

2021 ◽  
Vol 14 (6) ◽  
pp. 251
Author(s):  
Yuhao Liu ◽  
Petar M. Djurić ◽  
Young Shin Kim ◽  
Svetlozar T. Rachev ◽  
James Glimm
Keyword(s):  
At Risk ◽  
Systemic Risk ◽  
Value At Risk ◽  
Joint Distribution ◽  
Risk Measure ◽  
Tail Dependence ◽  
Risk Modeling ◽  
Jones Index ◽  
Out Of Sample ◽  
Copula Approach

We investigate a systemic risk measure known as CoVaR that represents the value-at-risk (VaR) of a financial system conditional on an institution being under distress. For characterizing and estimating CoVaR, we use the copula approach and introduce the normal tempered stable (NTS) copula based on the Lévy process. We also propose a novel backtesting method for CoVaR by a joint distribution correction. We test the proposed NTS model on the daily S&P 500 index and Dow Jones index with in-sample and out-of-sample tests. The results show that the NTS copula outperforms traditional copulas in the accuracy of both tail dependence and marginal processes modeling.

scholarly journals Systemic Risk Modeling: How Theory Can Meet Statistics

2020 ◽  
Author(s):  
Raphael A. Espinoza ◽  
Miguel Segoviano ◽  
Ji Yan
Keyword(s):  
Systemic Risk ◽  
Risk Modeling

scholarly journals A Bayesian Entropy Approach to Sectoral Systemic Risk Modeling

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1371
Author(s):  
Radu Lupu ◽  
Adrian Cantemir Călin ◽  
Cristina Georgiana Zeldea ◽  
Iulia Lupu
Keyword(s):  
Real Estate ◽  
Systemic Risk ◽  
Risk Measures ◽  
Estimation Method ◽  
Spillover Effects ◽  
Expected Shortfall ◽  
Risk Modeling ◽  
Capital Goods ◽  
Entropy Estimation ◽  
Marginal Expected Shortfall

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.

Sign in / Sign up

Export Citation Format

Share Document