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El. knyga: Introduction to Financial Markets: A Quantitative Approach

(Politecnico di Torino, Torino, Italy)
  • Formatas: EPUB+DRM
  • Išleidimo metai: 22-Feb-2018
  • Leidėjas: John Wiley & Sons Inc
  • Kalba: eng
  • ISBN-13: 9781118594667
Kitos knygos pagal šią temą:
  • Formatas: EPUB+DRM
  • Išleidimo metai: 22-Feb-2018
  • Leidėjas: John Wiley & Sons Inc
  • Kalba: eng
  • ISBN-13: 9781118594667
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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. 

 

Preface xv
About the Companion Website xix
Part I Overview
1 Financial Markets: Functions, Institutions, and Traded Assets
1(66)
1.1 What is the purpose of finance?
2(10)
1.2 Traded assets
12(34)
1.2.1 The balance sheet
15(5)
1.2.2 Assets vs. securities
20(2)
1.2.3 Equity
22(2)
1.2.4 Fixed income
24(3)
1.2.5 FOREX markets
27(2)
1.2.6 Derivatives
29(17)
1.3 Market participants and their roles
46(7)
1.3.1 Commercial vs. investment banks
48(1)
1.3.2 Investment funds and insurance companies
49(2)
1.3.3 Dealers and brokers
51(1)
1.3.4 Hedgers, speculators, and arbitrageurs
51(2)
1.4 Market structure and trading strategies
53(7)
1.4.1 Primary and secondary markets
53(1)
1.4.2 Over-the-counter vs. exchange-traded derivatives
53(1)
1.4.3 Auction mechanisms and the limit order book
53(2)
1.4.4 Buying on margin and leverage
55(3)
1.4.5 Short-selling
58(2)
1.5 Market indexes
60(3)
Problems
63(2)
Further Reading
65(1)
Bibliography
65(2)
2 Basic
Problems in Quantitative Finance
67(1)
2.1 Portfolio optimization
68(12)
2.1.1 Static portfolio optimization: Mean-variance efficiency
70(5)
2.1.2 Dynamic decision-making under uncertainty: A stylized consumption-saving model
75(5)
2.2 Risk measurement and management
80(22)
2.2.1 Sensitivity of asset prices to underlying risk factors
81(3)
2.2.2 Risk measures in a non-normal world: Value-at-risk
84(9)
2.2.3 Risk management: Introductory hedging examples
93(7)
2.2.4 Financial vs. nonfinancial risk factors
100(2)
2.3 The no-arbitrage principle in asset pricing
102(15)
2.3.1 Why do we need asset pricing models?
103(1)
2.3.2 Arbitrage strategies
104(4)
2.3.3 Pricing by no-arbitrage
108(4)
2.3.4 Option pricing in a binomial model
112(4)
2.3.5 The limitations of the no-arbitrage principle
116(1)
2.4 The mathematics of arbitrage
117(12)
2.4.1 Linearity of the pricing functional and law of one price
119(1)
2.4.2 Dominant strategies
120(5)
2.4.3 No-arbitrage principle and risk-neutral measures
125(4)
S2.1 Multiobjective optimization
129(4)
S2.2 Summary of LP duality
133(4)
Problems
137(2)
Further Reading
139(1)
Bibliography
139(4)
Part II Fixed-income assets
3 Elementary Theory of Interest Rates
143(64)
3.1 The time value of money: Shifting money forward in time
146(7)
3.1.1 Simple vs. compounded rates
147(3)
3.1.2 Quoted vs. effective rates: Compounding frequencies
150(3)
3.2 The time value of money: Shifting money backward in time
153(8)
3.2.1 Discount factors and pricing a zero-coupon bond
154(4)
3.2.2 Discount factors vs. interest rates
158(3)
3.3 Nominal vs. real interest rates
161(2)
3.4 The term structure of interest rates
163(2)
3.5 Elementary bond pricing
165(25)
3.5.1 Pricing coupon-bearing bonds
165(3)
3.5.2 From bond prices to term structures, and vice versa
168(3)
3.5.3 What is a risk-free rate, anyway?
171(3)
3.5.4 Yield-to-maturity
174(6)
3.5.5 Interest rate risk
180(8)
3.5.6 Pricing floating rate bonds
188(2)
3.6 A digression: Elementary investment analysis
190(3)
3.6.1 Net present value
191(1)
3.6.2 Internal rate of return
192(1)
3.6.3 Real options
193(1)
3.7 Spot vs. forward interest rates
193(10)
3.7.1 The forward and the spot rate curves
197(1)
3.7.2 Discretely compounded forward rates
197(1)
3.7.3 Forward discount factors
198(1)
3.7.4 The expectation hypothesis
199(3)
3.7.5 A word of caution: Model risk and hidden assumptions
202(1)
S3.1 Proof of Equation (3.42)
203(1)
Problems
203(2)
Further Reading
205(1)
Bibliography
205(2)
4 Forward Rate Agreements, Interest Rate Futures, and Vanilla Swaps
207(22)
4.1 LIBOR and EURIBOR rates
208(1)
4.2 Forward rate agreements
209(7)
4.2.1 A hedging view of forward rates
210(4)
4.2.2 FRAs as bond trades
214(1)
4.2.3 A numerical example
215(1)
4.3 Eurodollar futures
216(4)
4.4 Vanilla interest rate swaps
220(6)
4.4.1 Swap valuation: Approach 1
221(2)
4.4.2 Swap valuation: Approach 2
223(2)
4.4.3 The swap curve and the term structure
225(1)
Problems
226(1)
Further Reading
226(1)
Bibliography
226(3)
5 Fixed-Income Markets
229(18)
5.1 Day count conventions
230(1)
5.2 Bond markets
231(6)
5.2.1 Bond credit ratings
233(1)
5.2.2 Quoting bond prices
233(2)
5.2.3 Bonds with embedded options
235(2)
5.3 Interest rate derivatives
237(2)
5.3.1 Swap markets
237(1)
5.3.2 Bond futures and options
238(1)
5.4 The repo market and other money market instruments
239(1)
5.5 Securitization
240(4)
Problems
244(1)
Further Reading
244(1)
Bibliography
244(3)
6 Interest Rate Risk Management
247(30)
6.1 Duration as a first-order sensitivity measure
248(9)
6.1.1 Duration of fixed-coupon bonds
250(4)
6.1.2 Duration of a floater
254(1)
6.1.3 Dollar duration and interest rate swaps
255(2)
6.2 Further interpretations of duration
257(4)
6.2.1 Duration and investment horizons
258(2)
6.2.2 Duration and yield volatility
260(1)
6.2.3 Duration and quantile-based risk measures
260(1)
6.3 Classical duration-based immunization
261(4)
6.3.1 Cash flow matching
262(1)
6.3.2 Duration matching
263(2)
6.4 Immunization by interest rate derivatives
265(1)
6.4.1 Using interest rate swaps in asset-liability management
266(1)
6.5 A second-order refinement: Convexity
266(3)
6.6 Multifactor models in interest rate risk management
269(2)
Problems
271(1)
Further Reading
272(1)
Bibliography
273(4)
Part III Equity portfolios
7 Decision-Making under Uncertainty: The Static Case
277(42)
7.1 Introductory examples
278(4)
7.2 Should we just consider expected values of returns and monetary outcomes?
282(6)
7.2.1 Formalizing static decision-making under uncertainty
283(1)
7.2.2 The flaw of averages
284(4)
7.3 A conceptual tool: The utility function
288(11)
7.3.1 A few standard utility functions
293(4)
7.3.2 Limitations of utility functions
297(2)
7.4 Mean-risk models
299(11)
7.4.1 Coherent risk measures
300(2)
7.4.2 Standard deviation and variance as risk measures
302(1)
7.4.3 Quantile-based risk measures: VOR and CV@R
303(6)
7.4.4 Formulation of mean-risk models
309(1)
7.5 Stochastic dominance
310(4)
S7.1 Theorem proofs
314(1)
S7.1.1 Proof of Theorem 7.2
314(1)
S7.1.2 Proof of Theorem 7.4
315(1)
Problems
315(2)
Further Reading
317(1)
Bibliography
317(2)
8 Mean-Variance Efficient Portfolios
319(32)
8.1 Risk aversion and capital allocation to risky assets
320(5)
8.1.1 The role of risk aversion
324(1)
8.2 The mean-variance efficient frontier with risky assets
325(7)
8.2.1 Diversification and portfolio risk
325(1)
8.2.2 The efficient frontier in the case of two risky assets
326(3)
8.2.3 The efficient frontier in the case of n risky assets
329(3)
8.3 Mean-variance efficiency with a risk-free asset: The separation property
332(5)
8.4 Maximizing the Sharpe ratio
337(4)
8.4.1 Technical issues in Sharpe ratio maximization
340(1)
8.5 Mean-variance efficiency vs. expected utility
341(2)
8.6 Instability in mean-variance portfolio optimization
343(2)
S8.1 The attainable set for two risky assets is a hyperbola
345(1)
S8.2 Explicit solution of mean-variance optimization in matrix form
346(2)
Problems
348(1)
Further Reading
349(1)
Bibliography
349(2)
9 Factor Models
351(22)
9.1 Statistical issues in mean-variance portfolio optimization
352(1)
9.2 The single-index model
353(5)
9.2.1 Estimating a factor model
354(2)
9.2.2 Portfolio optimization within the single-index model
356(2)
9.3 The Treynor-Black model
358(7)
9.3.1 A top-down/bottom-up optimization procedure
362(3)
9.4 Multifactor models
365(2)
9.5 Factor models in practice
367(1)
S9.1 Proof of Equation (9.17)
368(1)
Problems
369(2)
Further Reading
371(1)
Bibliography
371(2)
10 Equilibrium Models: CAPM and APT
373(44)
10.1 What is an equilibrium model?
374(1)
10.2 The capital asset pricing model
375(6)
10.2.1 Proof of the CAPM formula
377(1)
10.2.2 Interpreting CAPM
378(2)
10.2.3 CAPM as a pricing formula and its practical relevance
380(1)
10.3 The Black-Litterman portfolio optimization model
381(7)
10.3.1 Black-Litterman model: The role of CAPM and Bayesian Statistics
382(4)
10.3.2 Black-Litterman model: A numerical example
386(2)
10.4 Arbitrage pricing theory
388(10)
10.4.1 The intuition
389(2)
10.4.2 A not-so-rigorous proof of APT
391(1)
10.4.3 APT for Well-Diversified Portfolios
392(1)
10.4.4 APT for Individual Assets
393(1)
10.4.5 Interpreting and using APT
394(4)
10.5 The behavioral critique
398(6)
10.5.1 The efficient market hypothesis
400(1)
10.5.2 The psychology of choice by agents with limited rationality
400(1)
10.5.3 Prospect theory: The aversion to sure loss
401(3)
S10.1 Bayesian statistics
404(7)
S10.1.1 Bayesian estimation
405(2)
S10.1.2 Bayesian learning in coin flipping
407(1)
S10.1.3 The expected value of a normal distribution
408(3)
Problems
411(2)
Further Reading
413(1)
Bibliography
413(4)
Part IV Derivatives
11 Modeling Dynamic Uncertainty
417(64)
11.1 Stochastic processes
420(18)
11.1.1 Introductory examples
422(6)
11.1.2 Marginals do not tell the whole story
428(2)
11.1.3 Modeling information: Filtration generated by a stochastic process
430(3)
11.1.4 Markov processes
433(3)
11.1.5 Martingales
436(2)
11.2 Stochastic processes in continuous time
438(3)
11.2.1 A fundamental building block: Standard Wiener process
438(2)
11.2.2 A generalization: Levy processes
440(1)
11.3 Stochastic differential equations
441(6)
11.3.1 A deterministic differential equation: The bank account process
442(1)
11.3.2 The generalized Wiener process
443(2)
11.3.3 Geometric Brownian motion and Ito processes
445(2)
11.4 Stochastic integration and Ito's lemma
447(10)
11.4.1 A digression: Riemann and Riemann-Stieltjes integrals
447(1)
11.4.2 Stochastic integral in the sense of Ito
448(5)
11.4.3 Ito's lemma
453(4)
11.5 Stochastic processes in financial modeling
457(5)
11.5.1 Geometric Brownian motion
457(3)
11.5.2 Generalizations
460(2)
11.6 Sample path generation
462(6)
11.6.1 Monte Carlo sampling
463(2)
11.6.2 Scenario trees
465(3)
S11.1 Probability spaces, measurability, and information
468(8)
Problems
476(2)
Further Reading
478(1)
Bibliography
478(3)
12 Forward and Futures Contracts
481(24)
12.1 Pricing forward contracts on equity and foreign currencies
482(8)
12.1.1 The spot-forward parity theorem
482(3)
12.1.2 The spot-forward parity theorem with dividend income
485(2)
12.1.3 Forward contracts on currencies
487(2)
12.1.4 Forward contracts on commodities or energy: Contango and backwardation
489(1)
12.2 Forward vs. futures contracts
490(3)
12.3 Hedging with linear contracts
493(8)
12.3.1 Quantity-based hedging
493(1)
12.3.2 Basis risk and minimum variance hedging
494(2)
12.3.3 Hedging with index futures
496(3)
12.3.4 Tailing the hedge
499(2)
Problems
501(1)
Further Reading
502(1)
Bibliography
502(3)
13 Option Pricing: Complete Markets
505(74)
13.1 Option terminology
506(4)
13.1.1 Vanilla options
507(1)
13.1.2 Exotic options
508(2)
13.2 Model-free price restrictions
510(9)
13.2.1 Bounds on call option prices
511(3)
13.2.2 Bounds on put option prices: Early exercise and continuation regions
514(3)
13.2.3 Parity relationships
517(2)
13.3 Binomial option pricing
519(11)
13.3.1 A hedging argument
520(3)
13.3.2 Lattice calibration
523(1)
13.3.3 Generalization to multiple steps
524(3)
13.3.4 Binomial pricing of American-style options
527(3)
13.4 A continuous-time model: The Black-Scholes-Merton pricing formula
530(15)
13.4.1 The delta-hedging view
532(7)
13.4.2 The risk-neutral view: Feynman-Kac representation theorem
539(4)
13.4.3 Interpreting the factors in the BSM formula
543(2)
13.5 Option price sensitivities: The Greeks
545(8)
13.5.1 Delta and gamma
546(4)
13.5.2 Theta
550(1)
13.5.3 Relationship between delta, gamma, and theta
551(1)
13.5.4 Vega
552(1)
13.6 The role of volatility
553(3)
13.6.1 The implied volatility surface
553(2)
13.6.2 The impact of volatility on barrier options
555(1)
13.7 Options on assets providing income
556(6)
13.7.1 Index options
557(1)
13.7.2 Currency options
558(1)
13.7.3 Futures options
559(1)
13.7.4 The mechanics of futures options
559(1)
13.7.5 A binomial view of futures options
560(2)
13.7.6 A risk-neutral view of futures options
562(1)
13.8 Portfolio strategies based on options
562(7)
13.8.1 Portfolio insurance and the Black Monday of 1987
563(1)
13.8.2 Volatility trading
564(2)
13.8.3 Dynamic vs. Static hedging
566(3)
13.9 Option pricing by numerical methods
569(1)
Problems
570(5)
Further Reading
575(1)
Bibliography
576(3)
14 Option Pricing: Incomplete Markets
579(38)
14.1 A PDE approach to incomplete markets
581(7)
14.1.1 Pricing a zero-coupon bond in a driftless world
584(4)
14.2 Pricing by short-rate models
588(7)
14.2.1 The Vasicek short-rate model
589(5)
14.2.2 The Cox-Ingersoll-Ross short-rate model
594(1)
14.3 A martingale approach to incomplete markets
595(8)
14.3.1 An informal approach to martingale equivalent measures
598(2)
14.3.2 Choice of numeraire: The bank account
600(1)
14.3.3 Choice of numeraire: The zero-coupon bond
601(1)
14.3.4 Pricing options with stochastic interest rates: Black's model
602(1)
14.3.5 Extensions
603(9)
14.4 Issues in model calibration
603(1)
14.4.1 Bias-variance tradeoff and regularized least-squares
604(5)
14.4.2 Financial model calibration
609(3)
Further Reading
612(1)
Bibliography
612(5)
Part V Advanced optimization models
15 Optimization Model Building
617(82)
15.1 Classification of optimization models
618(7)
15.2 Linear programming
625(3)
15.2.1 Cash flow matching
627(1)
15.3 Quadratic programming
628(4)
15.3.1 Maximizing the Sharpe ratio
629(2)
15.3.2 Quadratically constrained quadratic programming
631(1)
15.4 Integer programming
632(10)
15.4.1 A MIQP model to minimize TEV under a cardinality constraint
634(2)
15.4.2 Good MILP model building: The role of tight model formulations
636(6)
15.5 Conic optimization
642(13)
15.5.1 Convex cones
644(6)
15.5.2 Second-order cone programming
650(3)
15.5.3 Semidefinite programming
653(2)
15.6 Stochastic optimization
655(20)
15.6.1 Chance-constrained LP models
656(1)
15.6.2 Two-stage stochastic linear programming with recourse
657(6)
15.6.3 Multistage stochastic linear programming with recourse
663(7)
15.6.4 Scenario generation and stability in stochastic programming
670(5)
15.7 Stochastic dynamic programming
675(7)
15.7.1 The dynamic programming principle
676(3)
15.7.2 Solving Bellman's equation: The three curses of dimensionality
679(1)
15.7.3 Application to pricing options with early exercise features
680(2)
15.8 Decision rules for multistage SLPs
682(4)
15.9 Worst-case robust models
686(5)
15.9.1 Uncertain LPs: Polyhedral uncertainty
689(1)
15.9.2 Uncertain LPs: Ellipsoidal uncertainty
690(1)
15.10 Nonlinear programming models in finance
691(2)
15.10.1 Fixed-mix asset allocation
692(1)
Problems
693(2)
Further Reading
695(1)
Bibliography
696(3)
16 Optimization Model Solving
699(42)
16.1 Local methods for nonlinear programming
700(15)
16.1.1 Unconstrained nonlinear programming
700(3)
16.1.2 Penalty function methods
703(4)
16.1.3 Lagrange multipliers and constraint qualification conditions
707(6)
16.1.4 Duality theory
713(2)
16.2 Global methods for nonlinear programming
715(4)
16.2.1 Genetic algorithms
716(1)
16.2.2 Particle swarm optimization
717(2)
16.3 Linear programming
719(9)
16.3.1 The simplex method
720(3)
16.3.2 Duality in linear programming
723(3)
16.3.3 Interior-point methods: Primal-dual barrier method for LP
726(2)
16.4 Conic duality and interior-point methods
728(4)
16.4.1 Conic duality
728(3)
16.4.2 Interior-point methods for SOCP and SDP
731(1)
16.5 Branch-and-bound methods for integer programming
732(4)
16.5.1 A matheuristic approach: Fix-and-relax
735(1)
16.6 Optimization software
736(3)
16.6.1 Solvers
737(1)
16.6.2 Interfacing through imperative programming languages
738(1)
16.6.3 Interfacing through non-imperative algebraic languages
738(1)
16.6.4 Additional interfaces
739(1)
Problems
739(1)
Further Reading
740(1)
Bibliography 741(2)
Index 743
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.