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Multi-Asset Risk Modeling
Techniques for a Global Economy in an Electronic and Algorithmic Trading Era
- 1st Edition - December 3, 2013
- Authors: Morton Glantz, Robert Kissell
- Language: English
- Hardback ISBN:9 7 8 - 0 - 1 2 - 4 0 1 6 9 0 - 3
- eBook ISBN:9 7 8 - 0 - 1 2 - 4 0 1 6 9 4 - 1
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, commod… Read more
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Request a sales quoteMulti-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.
- 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.
Dedication
Preface
About The Authors
Acknowledgements
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
References
Chapter 2. A Primer on Risk Mathematics
Introduction
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
Endnotes
References
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
Reference
Chapter 4. Price Volatility
Introduction
What is Volatility?
Volatility Measures
Definitions
Market Observations: Empirical Findings
Forecasting Stock Volatility
Conclusions
References
Chapter 5. Factor Models
Introduction
Data Limitations
False Relationships
Degrees of Freedom
Factor Models
Types of Factor Models
Conclusion
References
Chapter 6. Equity Derivatives
Introduction
Option Contracts
Alternative Option Pricing Models
Futures Contracts
Forwards Contract
Swaps Contracts
Conclusions
Endnotes
References
Chapter 7. Foreign Exchange Market and Interest Rates
Introduction
How Much Does the FX Market Trade?
Foreign Exchange Markets
Exchange Rate Determinates
Spot Market
FX Quoting Conventions
Bid-Ask Spreads
Arbitrage
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
Conclusion
Endnotes
References
Chapter 8. Algorithmic Trading Risk
Introduction
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
Conclusion
References
Chapter 9. Risk-Hedging Techniques
Introduction
Definitions
Hedge Ratio
Dollar Hedge Value
Optimal Hedge Ratio
CAPM Dollar Value Hedging Technique
Examples
Conclusions
References
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
References
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
References
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
References
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
Conclusion
References
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
References
Index
- No. of pages: 544
- Language: English
- Edition: 1
- Published: December 3, 2013
- Imprint: Academic Press
- Hardback ISBN: 9780124016903
- eBook ISBN: 9780124016941
MG
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, USARK
Robert Kissell
Robert Kissell, Ph.D., 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 a professor at Molloy College in the School of Business and an adjunct professor at the Gabelli School of Business at Fordham University. He has held several senior leadership positions with prominent bulge bracket investment banks including UBS Securities where he was Executive Director of Execution Strategies and Portfolio Analysis, and at JP Morgan 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. Dr. Kissell has written several books and published dozens of journal articles on Algorithmic Trading, Risk, and Finance. He is a coauthor of the CFA Level III reading titled “Trade Strategy and Execution,” CFA Institute 2019.”
Affiliations and expertise
President, Kissell Research Group; Professor, Molloy College; Adjunct Professor, Fordham UniversityRead Multi-Asset Risk Modeling on ScienceDirect