Extreme Risk Management: Revolutionary Approaches to Evaluating and Measuring Risk


A revolutionary new approach for detecting and managing inherent risk

The unprecedented ...
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Extreme Risk Management: Revolutionary Approaches to Evaluating and Measuring Risk

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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


• 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

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Product Details

  • ISBN-13: 9780071700597
  • Publisher: McGraw-Hill Professional Publishing
  • Publication date: 6/7/2010
  • Edition description: New Edition
  • Edition number: 1
  • Pages: 304
  • Product dimensions: 6.20 (w) x 9.10 (h) x 1.20 (d)

Meet the Author

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

Bond Market and Think Like a Trader, Invest Like a Pro.

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Read an Excerpt




The McGraw-Hill Companies, Inc.

Copyright © 2010The McGraw-Hill Companies, Inc.
All rights reserved.
ISBN: 978-0-07-175956-4



Plausibility versus Probability: Two Worldviews

Over the last three decades or so, sophisticated financial modeling has been almost exclusively statistical in nature. The ready availability of massive amounts of historical market data has fueled the creation of valuation and risk measurement models built on concepts such as association, correlation, and likelihood.

All these models create implicit forecasts, that is, estimates of expected and possible future scenarios for a security or a portfolio of securities. Most often, these forecasts are based on the assumption that the future market behavior is well represented by the past.

However, this stochastic approach implies a worldview that ignores causality in favor of correlation. In this world, it doesn't matter whether gold prices increased because interest rates decreased or vice versa. It also doesn't matter whether the prices of a utility stock and an airline stock are directly related in some fashion or whether, instead, they are both driven by a common dependence on fuel prices. This world is a supremely efficient world as well: all prices reflect new information immediately, and that information is transmitted instantaneously around the globe.

However, intuition belies these notions. Traders and portfolio managers know that events drive prices. Catalysts such as the release of an economic indicator or an earnings report drive prices, and chain reactions precipitated by an important event can take a finite amount of time to propagate.

Such statistical models were often sufficient in the past, when the volume and complexity of derivative instruments were far lower than they are today. But now, the value and risk of popular instruments such as options on credit derivatives and complex asset-backed securities increasingly depend on the modeling of low-probability, high-consequence events. If the models used are not adequate for the task of anticipating such high-consequence events, massive losses and market disruptions can occur. Certainly the financial disruptions that began in 2007–2008 are abundant evidence of such failures.

But as the old saying goes, "Correlation is not causation." The alternative to a statistical model is a causal model that explicitly creates an alternative worldview, one in which cause and effect are modeled in logical or temporal order.

This alternative world is one in which plausibility rather than probability is modeled. The consequences and likelihood of events that have never before occurred but that can be reasonably anticipated (as a consequence of other events) are included in the quantitative models. Such modeling is the forte of the intelligence community and those responsible for national security, who must create metrics and construct solutions for threats that have never before occurred.

Plausibility can be determined from a mixture of expert opinion, hard facts, and historical experience. Although the structure of any causal model may be guided by the insights of human experts, it need not be strictly an expert system. Instead, through a process of causal inference, past history can be used to validate and inform the model. A causal model is not necessarily deterministic; it can allow for uncertainty. Ideally, causal inference facilitates the integration of substantive knowledge with statistical data to refine the former and interpret the latter.

Such causal models are used in other disciplines, most notably epidemiology and decision science. They are little used in finance, with the notable exception of the measurement of operational risk (i.e., the risk of loss due to human error). Causal models are nearly absent from quantitative modeling for purposes of instrument valuation or market and credit risk measurement.

The preference that quantitative analysts have for "frequentist" or probabilistic models over causal models over the last three decades is understandable for a number of reasons.

First, such models are relatively easy to create and implement, using financial theories (such as modern portfolio theory) that are already well accepted and in the public domain.

Also, until recently, neither the mathematical language nor the technical tools that might facilitate the creation of causal models existed. Although the financial community commenced serious quantitative modeling in the 1970s, it wasn't until the mid-1980s that much substantive work was done on causal models, even within the academic community.

Thus, the creation of rigorous theory, methodologies, and a language of causality that might have facilitated such model building did not exist at the time the financial community was choosing its path. Perhaps more important, even if such models had been created, the data required to inform them were usually insufficiently granular, synchronized, and properly organized for use in a causal inference process.

However, now, in the words of Judea Pearl, a leader in this field, "Put simply, causality has been mathematized." At the same time, certain technological innovations have made causal inference practical in the financial arena.

Consider one of the key questions in causal inference: How can one distinguish between mere correlation and cause and effect? When the sun rises and the cock crows, was one of these two events the catalyst for the other, or were they both the consequence of a third event?

One of the best methods of validating causal relationships is via experimentation. We can wake up the cock at 3 a.m. and see if this causes the sun to rise. Or an experiment can be designed to eliminate all variables but one: for example, in medical trials, the effect of the drug on a patient. To produce valid results, such an experiment would probably contain key features used in causal modeling, such as randomization (e.g., patients are randomly selected to receive an experimental drug or a placebo) and elimination of exogenous factors (e.g., variations in age or sex).

Fortunately, in finance, the capital markets are a laboratory that continuously provides us with natural experiments. Thus, rather than using historical market prices in statistical analyses, we can use them in causal inference models. Every day, traders receive information about catalytic events that move markets and are able to observe the synchronous or subsequent effects of those catalysts.

Technological advances now make the observation of these natural experiments both possible and practical. Formerly, end-of-day data were relatively useless for determining causation because so many important events occur during the course of a trading day. Just as in a medical trial, when there are multiple variables, reliable causal inferences are exceedingly difficult to make.

Only in the last few years has commercial software become available that is capable of capturing event data and synchronizing those data with real-time market data of the highest granularity. This synchronized information gives us the means to learn from one controlled experiment at a time, even if the experiments last just seconds.

Although many events occur in the course of a trading day, few of them occur simultaneously, where simultaneous is defined as occurring within the same very small window of time. For example, we might capture the earliest moment at which an earnings report became public or a report on crude oil inventory was released. If we then examine the real-time behavior of stock or oil prices in the seconds to minutes after the release, we can form opinions about how such an event drives prices.

Besides potentially providing better estimates of value and risk, causal models may be more in

Excerpted from EXTREME RISK MANAGEMENT by CHRISTINA RAY. Copyright © 2010 by The McGraw-Hill Companies, Inc.. Excerpted by permission of The McGraw-Hill Companies, Inc..
All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
Excerpts are provided by Dial-A-Book Inc. solely for the personal use of visitors to this web site.

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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

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  • Posted August 16, 2010

    Must read for investment managers

    I've worked with Christina Ray and am familiar with some of her path-breaking work. She is a true expert in financial risk management, at the cutting edge of developing new techniques; not some academic researcher publishing a book for resume development. If your hedge fund/money manager hasn't read and mastered this book, you probably should move your investments elsewhere.

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