189 Pages Posted: 23 Jan 2015 Last revised: 25 Jan 2017
Date Written: January 19, 2015
To avert the impending global Cyber-Finance Insurance Crisis based upon large-scale commercial reliance upon quantitative models with inherent model risks, tail risks, and systemic risks in current form, this post-doctoral thesis makes the following key contributions: Develops the first known Cyber-Finance-Trust™ framework for Cyber insurance modeling; Develops the first known model risk management framework for Cyber insurance modeling; Develops first known analysis of significant and extreme model risks, tail risks, and, systemic risk; Develops multi-method empirical study of VaR and Bayesian inference for containing model risks; Analyzes Markov Chain Monte Carlo for enabling Bayesian inference to minimize model risk; Develops Cyber insurance portfolio framework to minimize model risks, tail risks, systemic risks; Develops framework for Knightian uncertainty management beyond model risk management.
Keywords: Quantitative Analytics, Quantitative Finance, Model Risk Management, Cyber Risk Modeling, Cyber Insurance, Trust, VaR, Value at Risk, Expected Shortfall, ETL, CVaR, Cornish-Fisher, EVT, Bayesian Inference, Markov Chain Monte Carlo, Gibbs Sampling, Metropolis-Hastings Algorithm, Knightian Uncertainty
JEL Classification: D8, D81, D82, D89, G2, G20, G22, F3, F30, F4, F40, P4, P40, C00, C1, C10, C11, C4, C40, C5, C50, C51
Suggested Citation: Suggested Citation
Malhotra, Yogesh, Stress Testing for Cyber Risks: Cyber Risk Insurance Modeling beyond Value-at-Risk (VaR): Risk, Uncertainty, and, Profit for the Cyber Era (January 19, 2015). Available at SSRN: https://ssrn.com/abstract=2553547