Multi-Asset Risk Modeling - 1st Edition - ISBN: 9780124016903, 9780124016941

Multi-Asset Risk Modeling

1st Edition

Techniques for a Global Economy in an Electronic and Algorithmic Trading Era

Authors: Morton Glantz Robert Kissell
Hardcover ISBN: 9780124016903
eBook ISBN: 9780124016941
Imprint: Academic Press
Published Date: 16th December 2013
Page Count: 544
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Multi-Asset Risk Modeling describes, in a single volume, the latest and most advanced risk modeling techniques for equities, debt, fixed income, futures and derivatives, commodities, and foreign exchange, as well as advanced algorithmic and electronic risk management. Beginning with the fundamentals of risk mathematics and quantitative risk analysis, the book moves on to discuss the laws in standard models that contributed to the 2008 financial crisis and talks about current and future banking regulation. Importantly, it also explores algorithmic trading, which currently receives sparse attention in the literature. By giving coherent recommendations about which statistical models to use for which asset class, this book makes a real contribution to the sciences of portfolio management and risk management.

Key Features

  • Covers all asset classes
  • Provides mathematical theoretical explanations of risk as well as practical examples with empirical data
  • Includes sections on equity risk modeling, futures and derivatives, credit markets, foreign exchange, and commodities


Undergraduate and graduate students, professors, and professionals working with financial risk management techniques who want reference information about theoretical models and applications.

Table of Contents



About The Authors


Chapter 1. Introduction to Multi-Asset Risk Modeling—Lessons from the Debt Crisis

Types of Risk

Faulted Risk Models

Financial Models Breaking Down in the Equity Markets

Risk Models Breaking Down


Chapter 2. A Primer on Risk Mathematics


Regression Analysis

Regression Analysis Statistics

Unbiased Estimators

Matrix Algebra Techniques

Estimate Parameters

Linear Regression: Graphic Example

Log-Linear Regression Model

Log-Transformation: Graphic Example

Non-Linear Regression Model

Probability Models

Probability Distributions

Extreme Value Functions

Descriptive Statistics

Probability Distribution Functions

Continuous Distribution Functions

Extreme Value Functions

Discrete Distributions



Chapter 3. A Primer on Quantitative Risk Analysis

A Brief History of Risk: What exactly is Risk?

The Basics of Risk

The Nature of Risk and Return

Uncertainty Versus Risk

Risk Simulation Applications

Exercise 1: Basic Simulation Model

Exercise 2: Correlation Effects Model


Chapter 4. Price Volatility


What is Volatility?

Volatility Measures


Market Observations: Empirical Findings

Forecasting Stock Volatility



Chapter 5. Factor Models


Data Limitations

False Relationships

Degrees of Freedom

Factor Models

Types of Factor Models



Chapter 6. Equity Derivatives


Option Contracts

Alternative Option Pricing Models

Futures Contracts

Forwards Contract

Swaps Contracts




Chapter 7. Foreign Exchange Market and Interest Rates


How Much Does the FX Market Trade?

Foreign Exchange Markets

Exchange Rate Determinates

Spot Market

FX Quoting Conventions

Bid-Ask Spreads


Triangular Arbitrage

Purchasing Power Parity (PPP)

Law of One Price

Balance of Payments Model

Asset Market Model

Interest Rate Parity

Interest Arbitrage

Uncovered Interest Arbitrage

Covered Interest Arbitrage

Interest Rates

Time Value of Money

Market Observations and Analysis




Chapter 8. Algorithmic Trading Risk


Market Environment

Recent Growth in Algorithmic Trading

Classifications of Algorithms

Types of Algorithms

Algorithmic Trading Trends

Trading Venue Classification

Types of Orders

Algorithmic Decision-Making Process

High-Frequency Trading

The New Equity Exchange Environment

Trading Cost Equations

Trading Risk Components

Volume Forecasting Techniques

Daily Volumes

Trading Risk: Covariance Matrix

Estimation Error



Chapter 9. Risk-Hedging Techniques



Hedge Ratio

Dollar Hedge Value

Optimal Hedge Ratio

CAPM Dollar Value Hedging Technique




Chapter 10. Rating Credit Risk: Current Practices, Model Design, and Applications

External Ratings

Internal Ratings

Modeling Corporate Credit Risk

Specialized Lending Risk Models

Appendix. Corporate Risk Rating: Obligor and Facility Grade Requisites


Chapter 11. A Basic Credit Default Swap Model

Determining Probability of Default from Market Spreads

Probability of Default and Recoveries

Default and Survival Probabilities

Present Value of the CDS Premiums

Present Value of a Default Payment

Calculating the CDS Spread Premium

Other Considerations


Chapter 12. Multi-Asset Corporate Restructurings and Valuations

Building Blocks of Valuation

Stochastic Analysis of Multi-Asset Restructuring: A Banker’s Perspective

Appendix A. Banker’s Guide: Valuation Appraisal of Business Clients


Chapter 13. Extreme Value Theory and Application to Market Shocks for Stress Testing and Extreme Value at Risk

Value at Risk and Systemic Shocks

Extreme Value Theory and Application to Market Shocks for Stress Testing and Extreme Value at Risk

Technical Details

Economic Capital and Value at Risk Illustrations

Efficient Portfolio Allocation and Economic Capital VaR



Chapter 14. Ensuring Sustainability of an Institution as a Going Concern: An Approach to Dealing with Black Swan or Tail Risk

Sustainability Management is Critical to Weather a Crisis

Tail Risk and Sustainability Management Need Explicit Focus

Measurement is a Prerequisite to Effective Management

Effective Tail Risk Management

PML-Based Sustainability Management has Large Rewards

Sustaining a Going-Concern Through Tail-Risk Management




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About the Author

Morton Glantz

Professor Morton Glantz serves as a financial consultant, educator, and adviser to a broad spectrum of professionals, including corporate financial executives, government ministers, privatization managers, investment and commercial bankers, public accounting firms, members of merger and acquisition teams, strategic planning executives, management consultants, attorneys, and representatives of foreign governments and international banks. Professor Morton Glantz is a principal of Real Consulting and Real Options Valuation, firms specializing in risk consulting, training, certification, and advanced analytical software in the areas of risk quantification, analysis, and management solutions.

As a JP Morgan Chase (heritage bank) senior banker, Professor Glantz built a progressive career path specializing in credit analysis and credit risk management, risk grading systems, valuation models, and professional training. He was instrumental in the reorganization and development of the credit analysis module of the Bank’s Management Training Program-Finance, which at the time was recognized as one of the foremost training programs in the banking industry.

Professor Glantz is on the (adjunct) finance faculty of the Fordham Graduate School of Business. He has appeared in the Harvard University International Directory of Business and Management Scholars and Research, and has earned Fordham University Deans Award for Faculty Excellence on three occasions. He is a Board Member of the International Standards Board, International Institute of Professional Education and Research (IIPER). The IIPER is a global institute with partners and offices around the world, including the United States, Switzerland, Hong Kong, Mexico, Portugal, Singapore, Nigeria, and Malaysia. Professor Glantz is widely published in financial journals and has authored 8 books.

Affiliations and Expertise

Lecturer in Finance & Business Economics, Fordham Graduate School of Business, New York, NY, USA

Robert Kissell

Dr. Robert Kissell is the president and founder of Kissell Research Group. He has over twenty years of experience specializing in economics, finance, math & statistics, risk, and sports modeling.

Dr. Kissell is author of the leading industry books, “The Science of Algorithmic Trading & Portfolio Management,” (Elsevier, 2013), “Multi-Asset Risk Modeling” (Elsevier, 2014), and “Optimal Trading Strategies,” (AMACOM, 2003). He has published numerous research papers on trading, electronic algorithms, risk management, and best execution. His paper, “Dynamic Pre-Trade Models: Beyond the Black Box,” (2011) won Institutional Investor’s prestigious paper of the year award.

Dr. Kissell is an adjunct faculty member of the Gabelli School of Business at Fordham University and is an associate editor of the Journal of Trading and the Journal of Index Investing. He has previously been an instructor at Cornell University in their graduate Financial Engineering program.

Dr. Kissell has worked with numerous Investment Banks throughout his career including UBS Securities where he was Executive Director of Execution Strategies and Portfolio Analysis, and at JPMorgan where he was Executive Director and Head of Quantitative Trading Strategies. He was previously at Citigroup/Smith Barney where he was Vice President of Quantitative Research, and at Instinet where he was Director of Trading Research. He began his career as an Economic Consultant at R.J. Rudden Associates specializing in energy, pricing, risk, and optimization.

During his college years, Dr. Kissell was a member of the Stony Brook Soccer Team and was Co-Captain in his Junior and Senior years. It was during this time as a student athlete where he began applying math and statistics to sports modeling problems. Many of the techniques discussed in “Optimal Sports Math, Statistics, and Fantasy” were developed during his time at Stony Brook, and advanced thereafter. Thus, making this book the byproduct of decades of successful research.

Dr. Kissell has a Ph.D. in Economics from Fordham University, an MS in Applied Mathematics from Hofstra University, an MS in Business Management from Stony Brook University, and a BS in Applied Mathematics & Statistics from Stony Brook University.

Affiliations and Expertise

Robert Kissell, PhD, is President of Kissell Research Group, a global financial and economic consulting firm specializing in quantitative modeling, statistical analysis, and algorithmic trading. He is also currently an adjunct faculty member of the Gabelli School of Business at Fordham University, and has held several senior leadership positions with prominent bulge bracket Investment Banks.


"…explains advanced risk-modeling techniques for equities, debt, fixed income, futures and derivatives, commodities, and foreign exchange, as well as advanced algorithmic and electronic risk management….focuses on the application of proper volatility and factor models, optimization techniques, and the evaluation of traditional and nontraditional sources of risk.", March 2014

"The financial crisis has shown that measurement and control of financial risks is a crucial task for a financial institution that cannot be delegated to a few specialists in the quant department. This very readable book provides a good introduction to many hot issues in financial risk management at a level accessible to the non-specialist."--Ruediger Frey, Wirtschaftsuniversität Wien

"Multi-Asset Risk Modeling presents a comprehensive overview and summary of methods employed in finance. The statistical methods based on real-world examples provide a practical introduction for students, and the book is a valuable source for financial engineering and risk management tools as well."--Alois Pichler, Universität Wien

"The text offers an up-to-date and practical coverage of a wide range of topics in risk modeling and risk management, representing a good source for both students and practitioners."--Giorgio Fazio, Università degli Studi di Palermo