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An Introduction to Financial Markets

Book Description

COVERS THE FUNDAMENTAL TOPICS IN MATHEMATICS, STATISTICS, AND FINANCIAL MANAGEMENT THAT ARE REQUIRED FOR A THOROUGH STUDY OF FINANCIAL MARKETS

This comprehensive yet accessible book introduces students to financial markets and delves into more advanced material at a steady pace while providing motivating examples, poignant remarks, counterexamples, ideological clashes, and intuitive traps throughout. Tempered by real-life cases and actual market structures, An Introduction to Financial Markets: A Quantitative Approach accentuates theory through quantitative modeling whenever and wherever necessary. It focuses on the lessons learned from timely subject matter such as the impact of the recent subprime mortgage storm, the collapse of LTCM, and the harsh criticism on risk management and innovative finance. The book also provides the necessary foundations in stochastic calculus and optimization, alongside financial modeling concepts that are illustrated with relevant and hands-on examples.

An Introduction to Financial Markets: A Quantitative Approach starts with a complete overview of the subject matter. It then moves on to sections covering fixed income assets, equity portfolios, derivatives, and advanced optimization models. This book’s balanced and broad view of the state-of-the-art in financial decision-making helps provide readers with all the background and modeling tools needed to make “honest money” and, in the process, to become a sound professional.

  • Stresses that gut feelings are not always sufficient and that “critical thinking” and real world applications are appropriate when dealing with complex social systems involving multiple players with conflicting incentives
  • Features a related website that contains a solution manual for end-of-chapter problems
  • Written in a modular style for tailored classroom use
  • Bridges a gap for business and engineering students who are familiar with the problems involved, but are less familiar with the methodologies needed to make smart decisions

An Introduction to Financial Markets: A Quantitative Approach offers a balance between the need to illustrate mathematics in action and the need to understand the real life context. It is an ideal text for a first course in financial markets or investments for business, economic, statistics, engi­neering, decision science, and management science students.

PAOLO BRANDIMARTE is Full Professor at the Department of Mathematical Sciences of Politecnico di Torino in Italy, where he teaches Business Analytics and Financial Engineering. He is the author of several publications, including more than ten books on the application of optimization and simulation to diverse areas such as production and supply chain management, telecommunications, and finance.

Table of Contents

  1. Preface
  2. About the Companion Website
  3. Part One Overview
    1. Chapter One Financial Markets: Functions, Institutions, and Traded Assets
      1. 1.1 What is the purpose of finance?
      2. 1.2 Traded assets
      3. 1.3 Market participants and their roles
      4. 1.4 Market structure and trading strategies
      5. 1.5 Market indexes
      6. Problems
      7. Further reading
      8. Bibliography
      9. Notes
    2. Chapter Two Basic Problems in Quantitative Finance
      1. 2.1 Portfolio optimization
      2. 2.2 Risk measurement and management
      3. 2.3 The no-arbitrage principle in asset pricing
      4. 2.4 The mathematics of arbitrage
      5. S2.1 Multiobjective optimization
      6. S2.2 Summary of LP duality
      7. Problems
      8. Further reading
      9. Bibliography
      10. Notes
  4. Part Two Fixed-income assets
    1. Chapter Three Elementary Theory of Interest Rates
      1. 3.1 The time value of money: Shifting money forward in time
      2. 3.2 The time value of money: Shifting money backward in time
      3. 3.3 Nominal vs. real interest rates
      4. 3.4 The term structure of interest rates
      5. 3.5 Elementary bond pricing
      6. 3.6 A digression: Elementary investment analysis
      7. 3.7 Spot vs. forward interest rates
      8. Problems
      9. Further reading
      10. Bibliography
      11. Notes
    2. Chapter Four Forward Rate Agreements, Interest Rate Futures, and Vanilla Swaps
      1. 4.1 LIBOR and EURIBOR rates
      2. 4.2 Forward rate agreements
      3. 4.3 Eurodollar futures
      4. 4.4 Vanilla interest rate swaps
      5. Problems
      6. Further reading
      7. Bibliography
      8. Notes
    3. Chapter Five Fixed-Income Markets
      1. 5.1 Day count conventions
      2. 5.2 Bond markets
      3. 5.3 Interest rate derivatives
      4. 5.4 The repo market and other money market instruments
      5. 5.5 Securitization
      6. Problems
      7. Further reading
      8. Bibliography
      9. Notes
    4. Chapter Six Interest Rate Risk Management
      1. 6.1 Duration as a first-order sensitivity measure
      2. 6.2 Further interpretations of duration
      3. 6.3 Classical duration-based immunization
      4. 6.4 Immunization by interest rate derivatives
      5. 6.5 A second-order refinement: Convexity
      6. 6.6 Multifactor models in interest rate risk management
      7. Problems
      8. Further reading
      9. Bibliography
      10. Notes
  5. Part Three Equity portfolios
    1. Chapter Seven Decision-Making under Uncertainty: The Static Case
      1. 7.1 Introductory examples
      2. 7.2 Should we just consider expected values of returns and monetary outcomes?
      3. 7.3 A conceptual tool: The utility function
      4. 7.4 Mean-risk models
      5. 7.5 Stochastic dominance
      6. S7.1 Theorem proofs
      7. Problems
      8. Further reading
      9. Bibliography
      10. Notes
    2. Chapter Eight Mean–Variance Efficient Portfolios
      1. 8.1 Risk aversion and capital allocation to risky assets
      2. 8.2 The mean-variance efficient frontier with risky assets
      3. 8.3 Mean–variance efficiency with a risk-free asset: The separation property
      4. 8.4 Maximizing the Sharpe ratio
      5. 8.5 Mean–variance efficiency vs. expected utility
      6. 8.6 Instability in mean–variance portfolio optimization
      7. S8.1 The attainable set for two risky assets is a hyperbola
      8. S8.2 Explicit solution of mean–variance optimization in matrix form
      9. Problems
      10. Further reading
      11. Bibliography
      12. Notes
    3. Chapter Nine Factor Models
      1. 9.1 Statistical issues in mean-variance portfolio optimization
      2. 9.2 The single-index model
      3. 9.3 The Treynor-Black model
      4. 9.4 Multifactor models
      5. 9.5 Factor models in practice
      6. S9.1 Proof of Equation (9.17)
      7. Problems
      8. Further reading
      9. Bibliography
      10. Notes
    4. Chapter Ten Equilibrium Models: CAPM and APT
      1. 10.1 What is an equilibrium model?
      2. 10.2 The capital asset pricing model
      3. 10.3 The Black-Litterman portfolio optimization model
      4. 10.4 Arbitrage pricing theory
      5. 10.5 The behavioral critique
      6. S10.1 Bayesian statistics
      7. Problems
      8. Further reading
      9. Bibliography
      10. Notes
  6. Part Four Derivatives
    1. Chapter Eleven Modeling Dynamic Uncertainty
      1. 11.1 Stochastic processes
      2. 11.2 Stochastic processes in continuous time
      3. 11.3 Stochastic differential equations
      4. 11.4 Stochastic integration and Itô’s lemma
      5. 11.5 Stochastic processes in financial modeling
      6. 11.5.1 Geometric Brownian Motion
      7. 11.5.2 Generalizations
      8. 11.6 Sample path generation
      9. S11.1 Probability spaces, measurability, and information
      10. Problems
      11. Further reading
      12. Bibliography
      13. Notes
    2. Chapter Twelve Forward and Futures Contracts
      1. 12.1 Pricing forward contracts on equity and foreign currencies
      2. 12.2 Forward vs. futures contracts
      3. 12.3 Hedging with linear contracts
      4. Problems
      5. Further reading
      6. Bibliography
      7. Notes
    3. Chapter Thirteen Option Pricing: Complete Markets
      1. 13.1 Option terminology
      2. 13.2 Model-free price restrictions
      3. 13.3 Binomial option pricing
      4. 13.4 A continuous-time model: The Black–Scholes–Merton pricing formula
      5. 13.5 Option price sensitivities: The Greeks
      6. 13.6 The role of volatility
      7. 13.7 Options on assets providing income
      8. 13.8 Portfolio strategies based on options
      9. 13.9 Option pricing by numerical methods
      10. Problems
      11. 13.12 Further reading
      12. Bibliography
      13. Notes
    4. Chapter Fourteen Option Pricing: Incomplete Markets
      1. 14.1 A PDE approach to incomplete markets
      2. 14.2 Pricing by short-rate models
      3. 14.3 A martingale approach to incomplete markets
      4. 14.4 Issues in model calibration
      5. Further reading
      6. Bibliography
      7. Notes
  7. Part Five Advanced optimization models
    1. Chapter Fifteen Optimization Model Building
      1. 15.1 Classification of optimization models
      2. 15.2 Linear programming
      3. 15.3 Quadratic programming
      4. 15.4 Integer programming
      5. 15.5 Conic optimization
      6. 15.6 Stochastic optimization
      7. 15.7 Stochastic dynamic programming
      8. 15.8 Decision rules for multistage SLPs
      9. 15.9 Worst-case robust models
      10. 15.10 Nonlinear programming models in finance
      11. Problems
      12. Further reading
      13. Bibliography
      14. Notes
    2. Chapter Sixteen Optimization Model Solving
      1. 16.1 Local methods for nonlinear programming
      2. 16.2 Global methods for nonlinear programming
      3. 16.3 Linear programming
      4. 16.4 Conic duality and interior-point methods
      5. 16.5 Branch-and-bound methods for integer programming
      6. 16.6 Optimization software
      7. Problems
      8. Further reading
      9. Bibliography
      10. Notes
  8. Index
  9. EULA