mixed material bibliography New York Mc Graw Hill 2010

300p. : pdf. ; 4.3 MB. 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 : 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 Christina Ray is senior managing director for Market Intelligence at Omnis Inc. She has over 25 years experience in quantitative finance and is the author of The Perpustakaan - Sekolah Tinggi Manajemen PPM Pusat Informasi Manajemen 38648 2012-03-08 00:00:00 2015-12-11 00:00:00 machine generated