Simulation, 2e
by Pachamanova, Fabozzi, Fabozzi
| ISBN: 9780262049801 | Copyright 2025
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Simulation, Optimization, and Machine Learning for Finance offers a comprehensive introduction to the quantitative tools essential for asset management and corporate finance. This extensively revised and expanded edition builds upon the foundation of the textbook Simulation and Optimization in Finance, integrating the latest advancements in quantitative tools. Designed for undergraduates, graduate students, and professionals seeking to enhance their analytical expertise in finance, the book bridges theory with practical application, making complex financial concepts more accessible.
Beginning with a review of foundational finance principles, the text progresses to advanced topics in simulation, optimization, and machine learning, demonstrating their relevance in financial decision-making. Readers gain hands-on experience developing financial risk models using these techniques, fostering conceptual understanding and practical implementation.Provides a structured introduction to probability, inferential statistics, and data science
- Provides a structured introduction to probability, inferential statistics, and data science
- Explores cutting-edge techniques in simulation modeling, optimization, and machine learning
- Demonstrates real-world asset allocation strategies, advanced portfolio risk measures, and fixed-income portfolio management using quantitative tools
- Covers factor models and stochastic processes in asset pricing
- Integrates capital budgeting and real options analysis, emphasizing the role of uncertainty and quantitative modeling in long-term financial decision-making
- Is suitable for practitioners, students, and self-learners
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| Contents (pg. vii) | |
| Preface (pg. ix) | |
| Acknowledgments (pg. xv) | |
| About the Authors (pg. xvii) | |
| 1. Introduction (pg. 1) | |
| 1.1 Optimization (pg. 2) | |
| 1.2 Simulation (pg. 2) | |
| 1.3 Machine Learning (pg. 3) | |
| 1.4 Organization of the Book (pg. 4) | |
| 1.5 A Closer Look at Each Chapter (pg. 4) | |
| I. Background Topics (pg. 9) | |
| 2. Important Finance Concepts (pg. 11) | |
| 2.1 Basic Theory of Interest (pg. 11) | |
| 2.2 Asset Classes (pg. 14) | |
| 2.3 Basic Trading Terminology (pg. 23) | |
| 2.4 Calculating the Rate of Return (pg. 24) | |
| 2.5 Valuation (pg. 27) | |
| 2.6 Important Concepts in Fixed Income (pg. 31) | |
| Summary (pg. 42) | |
| 3. Random Variables and Probability Distributions (pg. 45) | |
| 3.1 What Is a Probability Distribution? (pg. 45) | |
| 3.2 Bernoulli Probability Distribution and Probability Mass Functions (pg. 46) | |
| 3.3 Binomial Probability Distribution and Discrete Distributions (pg. 46) | |
| 3.4 Normal Distribution and Probability Density Functions (pg. 50) | |
| 3.5 Concept of Cumulative Probability (pg. 53) | |
| 3.6 Describing Distributions (pg. 55) | |
| 3.7 Brief Overview of Some Important Probability Distributions (pg. 60) | |
| 3.8 Dependence between Two Random Variables: Covariance and Correlation (pg. 68) | |
| 3.9 Sums of Random Variables (pg. 71) | |
| 3.10 Joint Probability Distributions and Conditional Probability (pg. 73) | |
| 3.11 Risk Versus Uncertainty (pg. 74) | |
| Summary (pg. 75) | |
| 4. Inferential Statistics (pg. 77) | |
| 4.1 Estimating Population Parameters from Sample Statistics (pg. 78) | |
| 4.2 Hypothesis Testing (pg. 85) | |
| Summary (pg. 87) | |
| II. Fundamentals of Simulation, Optimization, and Machine Learning (pg. 89) | |
| 5. Simulation Modeling (pg. 91) | |
| 5.1 Monte Carlo Simulation: A Simple Example (pg. 91) | |
| 5.2 Why Use Simulation? (pg. 96) | |
| 5.3 Important Questions in Simulation Modeling (pg. 102) | |
| 5.4 Random Number Generation (pg. 105) | |
| Summary (pg. 114) | |
| 6. Optimization Modeling (pg. 117) | |
| 6.1 Optimization Formulations (pg. 117) | |
| 6.2 Important Types of Optimization Problems (pg. 122) | |
| 6.3 Optimization Problem Formulation Examples (pg. 125) | |
| 6.4 Optimization Algorithms (pg. 133) | |
| 6.5 Optimization Duality (pg. 140) | |
| 6.6 Multistage Optimization (pg. 142) | |
| 6.7 Optimization Software (pg. 152) | |
| Summary (pg. 153) | |
| 7. Optimization under Uncertainty (pg. 155) | |
| 7.1 Dynamic Programming (pg. 156) | |
| 7.2 Stochastic Programming (pg. 161) | |
| 7.3 Robust Optimization (pg. 171) | |
| Summary (pg. 177) | |
| 8. Data and Data Science (pg. 179) | |
| 8.1 Types of Data (pg. 179) | |
| 8.2 Data Issues (pg. 182) | |
| 8.3 Dimensionality and the Curse of Dimensionality (pg. 185) | |
| 8.4 Data Measurement Scales (pg. 185) | |
| 8.5 Big Data (pg. 187) | |
| 8.6 Data Visualization (pg. 189) | |
| Summary (pg. 189) | |
| 9. Regression Models (pg. 191) | |
| 9.1 OLS Regression (pg. 192) | |
| 9.2 Steps in Designing a Regression Model (pg. 197) | |
| 9.3 Determining the Number of Explanatory Variables (pg. 199) | |
| 9.4 Diagnostic Tests of Violation of the Error Term (pg. 201) | |
| 9.5 Selecting the Regression Model (pg. 202) | |
| 9.6 Use of Categorical Variables as Explanatory Variables (pg. 202) | |
| 9.7 Multicollinearity and the Selection of Explanatory Variables (pg. 205) | |
| 9.8 Probabilistic Classification Regression Models (pg. 207) | |
| Summary (pg. 211) | |
| 10. Machine Learning (pg. 215) | |
| 10.1 ML Terminology (pg. 215) | |
| 10.2 Categorizing ML Algorithms (pg. 216) | |
| 10.3 Hyperparameter Tuning (pg. 229) | |
| 10.4 Cross-Validation (pg. 234) | |
| 10.5 Challenges of Implementing Machine Learning in Finance (pg. 236) | |
| Summary (pg. 237) | |
| 11. Natural Language Processing (pg. 241) | |
| 11.1 A Brief History of NLP (pg. 241) | |
| 11.2 NLP Techniques (pg. 242) | |
| 11.3 Preprocessing Text Data (pg. 247) | |
| 11.4 Applications of NLP to Asset Management (pg. 251) | |
| 11.5 Using NLP in Financial Applications (pg. 255) | |
| Summary (pg. 255) | |
| III. Applications to Asset Management (pg. 257) | |
| 12. Asset Allocation Models (pg. 259) | |
| 12.1 Asset Allocation and Diversification (pg. 260) | |
| 12.2 The Classical Mean-Variance Optimization Model (pg. 261) | |
| 12.3 Robust Optimization for Mean-Variance Analysis (pg. 272) | |
| 12.4 Risk Parity Model (pg. 274) | |
| 12.5 Using ML Models for Asset Allocation (pg. 277) | |
| Summary (pg. 279) | |
| 13. Advanced Portfolio Risk Measures (pg. 281) | |
| 13.1 Classes of Risk Measures (pg. 281) | |
| 13.2 Value-at-Risk (pg. 285) | |
| 13.3 Conditional Value-at-Risk and the Concept of Coherent Risk Measures (pg. 300) | |
| Summary (pg. 304) | |
| 14. Equity Portfolio Selection in Practice (pg. 307) | |
| 14.1 The Investment Process (pg. 308) | |
| 14.2 Portfolio Constraints Commonly Used in Practice (pg. 315) | |
| 14.3 Benchmark Exposure and Tracking Error Minimization (pg. 321) | |
| 14.4 Incorporating Transaction Costs (pg. 324) | |
| 14.5 Incorporating Taxes (pg. 329) | |
| 14.6 Multiaccount Optimization (pg. 332) | |
| 14.7 Robust Parameter Estimation (pg. 336) | |
| 14.8 Portfolio Resampling (pg. 337) | |
| 14.9 Robust Portfolio Optimization (pg. 339) | |
| Summary (pg. 344) | |
| 15. Fixed-Income Portfolio Management in Practice (pg. 347) | |
| 15.1 Measuring Bond Portfolio Risk (pg. 347) | |
| 15.2 Spectrum of Bond Portfolio Strategies (pg. 352) | |
| 15.3 Bond Benchmarks (pg. 357) | |
| 15.4 Using Quantitative Methods for Portfolio Allocation (pg. 358) | |
| 15.5 Liability-Driven Strategies (pg. 359) | |
| 15.6 Machine Learning Applications in Bond Portfolio Management (pg. 365) | |
| Summary (pg. 373) | |
| IV. Asset Pricing Models (pg. 375) | |
| 16. Factor Models (pg. 377) | |
| 16.1 Identifying Factors (pg. 377) | |
| 16.2 Equity Factors and Market Anomalies (pg. 380) | |
| 16.3 Expected-Return Forecasting Factor Models in Practice (pg. 381) | |
| 16.4 Risk-Forecasting Factor Models (pg. 391) | |
| Summary (pg. 396) | |
| 17. Modeling Asset Price Dynamics (pg. 399) | |
| 17.1 Binomial Trees (pg. 400) | |
| 17.2 Arithmetic Random Walks (pg. 402) | |
| 17.3 Geometric Random Walks (pg. 405) | |
| 17.4 Mean Reversion (pg. 411) | |
| 17.5 Advanced Random Walk Models (pg. 417) | |
| 17.6 Stochastic Processes (pg. 421) | |
| Summary (pg. 425) | |
| V. Financial Derivatives and Mortgage-Backed Securities (pg. 427) | |
| 18. Introduction to Derivatives (pg. 429) | |
| 18.1 Basic Types of Derivatives (pg. 430) | |
| 18.2 Important Concepts for Derivative Pricing and Use (pg. 437) | |
| 18.3 Pricing Forwards and Futures (pg. 441) | |
| 18.4 Pricing Options (pg. 443) | |
| 18.5 Pricing Swaps (pg. 463) | |
| Summary (pg. 464) | |
| 19. Pricing Derivatives with Simulation (pg. 467) | |
| 19.1 Computing Option Prices with Crude Monte Carlo Simulation (pg. 467) | |
| 19.2 Variance Reduction Techniques (pg. 471) | |
| 19.3 Quasirandom Number Sequences (pg. 482) | |
| 19.4 More Simulation Application Examples (pg. 487) | |
| Summary (pg. 499) | |
| 20. Using Derivatives in Portfolio Management (pg. 501) | |
| 20.1 Using Derivatives in Equity Portfolio Management (pg. 501) | |
| 20.2 Using Derivatives in Bond Portfolio Management (pg. 505) | |
| 20.3 Using Futures to Implement an Asset Allocation Decision (pg. 509) | |
| 20.4 Measuring Portfolio Risk When the Portfolio Contains Derivatives (pg. 510) | |
| Summary (pg. 516) | |
| 21. Structuring and Pricing Residential Mortgage-Backed Securities (pg. 519) | |
| 21.1 Types of Asset-Backed Securities (pg. 519) | |
| 21.2 Mortgage-Backed Securities: Important Terminology (pg. 520) | |
| 21.3 Types of RMBS Structures (pg. 524) | |
| 21.4 Pricing RMBS by Simulation (pg. 538) | |
| 21.5 Using Simulation to Estimate Sensitivity of RMBS Prices to Different Factors (pg. 545) | |
| 21.6 Structuring RMBS Deals Using Dynamic Programming (pg. 547) | |
| 21.7 Machine Learning and the Analysis of RMBS (pg. 548) | |
| Summary (pg. 550) | |
| VI. Capital Budgeting and Real Options (pg. 553) | |
| 22. Capital Budgeting under Uncertainty (pg. 555) | |
| 22.1 Classifying Investment Projects (pg. 556) | |
| 22.2 Investment Decisions and Wealth Maximization (pg. 558) | |
| 22.3 Evaluating Project Risk (pg. 571) | |
| 22.4 Case Study (pg. 577) | |
| 22.5 Managing Portfolios of Projects (pg. 593) | |
| Summary (pg. 593) | |
| 23. Applications of Real Options to Capital Budgeting (pg. 595) | |
| 23.1 Types of Real Options (pg. 596) | |
| 23.2 Real Options and Financial Options (pg. 598) | |
| 23.3 New View of NPV (pg. 599) | |
| 23.4 Option to Expand (pg. 601) | |
| 23.5 Option to Abandon (pg. 603) | |
| 23.6 More Real Options Examples (pg. 604) | |
| 23.7 Estimation of Inputs for Real Option Valuation Models (pg. 611) | |
| Summary (pg. 614) | |
| References (pg. 615) | |
| Index (pg. 633) | |
Dessislava A. Pachamanova
Dessislava A. Pachamanova is Professor and Zwerling Family Endowed Term Chair at Babson College and Research Affiliate at the Massachusetts Institute of Technology. She is coauthor of Robust Portfolio Optimization and Management and Portfolio Construction and Analytics.Frank J. Fabozzi
Frank J. Fabozzi is Professor of Practice at Johns Hopkins Carey Business School. He has held positions at EDHEC Business School, Yale, Princeton, MIT, NYU, and Carnegie Mellon. He is the author of Entrepreneurial Finance and Accounting for High-Tech Companies and Introduction to Fixed-Income Analysis and Portfolio Management, and coauthor of Bond Markets, Analysis, and Strategies, Tenth Edition and Foundations of Global Financial Markets and Institutions, all published by the MIT Press. His forthcoming coauthored books to be published by MIT Press are The Economics of FinTech and Simulation, Optimization, and Machine Learning for Finance.Francesco A. Fabozzi
Francesco A. Fabozzi is Managing Editor of the Journal of Financial Data Science, coauthor of two books on asset management, and a doctoral student in data science at Stevens Institute of Technology.| Instructors Only | |
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