A revolutionary new approach for detecting and managing inherent risk

The unprecedented turmoil in the financial markets turned the field of quantitative finance on its head and generated severe criticism of the statistical models used to manage risk and predict “black swan” events. Something very important had been lost when statistical representations replaced expert knowledge and statistics substituted for causation.

Extreme Risk Management brings causation into the equation. The use of causal models in risk management, securities valuation, and portfolio management provides a real and much-needed alternative to the stochastic models used so far. Providing an alternative tool for risk modeling and scenario-building in stress-testing, this game-changing book uses causal models that help you:



Evaluate risk with extraordinary accuracy
Predict devastating worst-case scenarios
Enhance transparency
Facilitate better decision making

TABLE OF CONTENTS



Plausibility vs. Probability: Alternative World Views
The Evolution of Modern Analytics
Risk Management Metrics and Models
The Future as Forecast: Assumptions Implicit in Stochastic Risk Measurement Models
An Alternative Path to Actionable Intelligence
Solutions: Moving Toward a Connectivist Approach
An Introduction to Causality: Theory, Models, and Inference
Risk Inference Networks: Estimating Vulnerability, Consequences, and Likelihood
Securities Valuation, Risk Measurement, and Portfolio Management Using Causal Models
Risk Fusion and Super Models: A Framework for Enterprise Risk Management
Inferring Causality from Historical Market Behavior
Sensemaking for Warnings: Reverse-Engineering Market Intelligence
The United States as Enterprise: Implications for National Policy and Security