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Mr. Rui Ding
Stony Brook University

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Research Keywords & Expertise

0 Machine Learning
0 Quantitative Finance
0 Applied Mathematics and Optimization
0 Decision Making under Uncertainty
0 Risk And Financial Management

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Short Biography

I am a Ph.D. student in Applied Mathematics & Statistics at Stony Brook University, NY, concentrating on Quantitative Finance and Operations Research, also broadly interested in Artificial Intelligence and Computational Mathematics. My current research interests are mainly in Stochastic Optimization and Machine Learning. In particular, I am working on topics such as reinforcement learning and sequential decision making, information geometry, and risk sensitive and robust optimization with applications to problems from quantitative finance, operations research, and artificial intelligence.

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Journal article
Published: 03 November 2020 in Journal of Risk and Financial Management
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Systemic risk is the risk that the distress of one or more institutions trigger a collapse of the entire financial system. We extend CoVaR (value-at-risk conditioned on an institution) and CoCVaR (conditional value-at-risk conditioned on an institution) systemic risk contribution measures and propose a new CoCDaR (conditional drawdown-at-risk conditioned on an institution) measure based on drawdowns. This new measure accounts for consecutive negative returns of a security, while CoVaR and CoCVaR combine together negative returns from different time periods. For instance, ten 2% consecutive losses resulting in 20% drawdown will be noticed by CoCDaR, while CoVaR and CoCVaR are not sensitive to relatively small one period losses. The proposed measure provides insights for systemic risks under extreme stresses related to drawdowns. CoCDaR and its multivariate version, mCoCDaR, estimate an impact on big cumulative losses of the entire financial system caused by an individual firm’s distress. It can be used for ranking individual systemic risk contributions of financial institutions (banks). CoCDaR and mCoCDaR are computed with CVaR regression of drawdowns. Moreover, mCoCDaR can be used to estimate drawdowns of a security as a function of some other factors. For instance, we show how to perform fund drawdown style classification depending on drawdowns of indices. Case study results, data, and codes are posted on the web.

ACS Style

Rui Ding; Stan Uryasev. CoCDaR and mCoCDaR: New Approach for Measurement of Systemic Risk Contributions. Journal of Risk and Financial Management 2020, 13, 270 .

AMA Style

Rui Ding, Stan Uryasev. CoCDaR and mCoCDaR: New Approach for Measurement of Systemic Risk Contributions. Journal of Risk and Financial Management. 2020; 13 (11):270.

Chicago/Turabian Style

Rui Ding; Stan Uryasev. 2020. "CoCDaR and mCoCDaR: New Approach for Measurement of Systemic Risk Contributions." Journal of Risk and Financial Management 13, no. 11: 270.