Stress Testing for Cyber Risks : Cyber Risk Insurance Modeling beyond Value-at-Risk (VaR): Risk, Uncertainty, and, Profit for the Cyber Era

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.Updated, revised, summary version of the thesis invited for submission by NAIC as:National Association of Insurance Commissioners (NAIC) Expert Paper: The National Association of Insurance Commissioners (NAIC) is the U.S. standard-setting and regulatory support organization created and governed by the chief insurance regulators from the 50 states, the District of Columbia and five U.S. territories. Updated, revised, summary version of the thesis invited for submission by NAIC as: National Association of Insurance Commissioners Expert Paper:Malhotra, Yogesh, Advancing Cyber Risk Insurance Underwriting Model Risk Management beyond VaR to Pre-Empt and Prevent the Forthcoming Global Cyber Insurance Crisis (June 24, 2017). Available at SSRN: https://ssrn.com/abstract=3081492. Expert Paper prepared and submitted on the request of the National Association of Insurance Commissioners on June 24, 2017