Extreme Risk Management: Revolutionary Approaches to Evaluating and Measuring Risk
Series: PROFESSIONAL FINANCE & INVESTM;
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42 436 Ft
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Product details:
- Publisher McGraw Hill
- Date of Publication 16 July 2010
- ISBN 9780071700597
- Binding Hardback
- No. of pages304 pages
- Size 236x162x23 mm
- Weight 576 g
- Language English 0
Categories
Long description:
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
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 More
Table of Contents:
Introduction: A Profound Transformation in Risk Management
1. Plausibility vs. Probability: Alternative World Views
2. The Evolution of Modern Analytics
3. Risk Management Metrics and Models
4. The Future as Forecast: Assumptions Implicit in Stochastic Risk Measurement Models
5. An Alternative Path to Actionable Intelligence
6. Solutions: Moving Toward a Connectivist Approach
7. An Introduction to Causality: Theory, Models, and Inference
8. Risk Inference Networks: Estimating Vulnerability, Consequences, and Likelihood
9. Securities Valuation, Risk Measurement, and Portfolio Management Using Causal Models
10. Risk Fusion and Super Models: A Framework for Enterprise Risk Management
11. Inferring Causality from Historical Market Behavior
12. Sensemaking for Warnings: Reverse-Engineering Market Intelligence
13. The United States as Enterprise: Implications for National Policy and Security
