دانلود کتاب Optimization Methods in Finance [2nd ed.]

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Content: Machine generated contents note: 1.Overview of Optimization Models --
1.1.Types of Optimization Models --
1.2.Solution to Optimization Problems --
1.3.Financial Optimization Models --
1.4.Notes --
2.Linear Programming: Theory and Algorithms --
2.1.Linear Programming --
2.2.Graphical Interpretation of a Two-Variable Example --
2.3.Numerical Linear Programming Solvers --
2.4.Sensitivity Analysis --
2.5.*Duality --
2.6.*Optimality Conditions --
2.7.*Algorithms for Linear Programming --
2.8.Notes --
2.9.Exercises --
3.Linear Programming Models: Asset-Liability Management --
3.1.Dedication --
3.2.Sensitivity Analysis --
3.3.Immunization --
3.4.Some Practical Details about Bonds --
3.5.Other Cash Flow Problems --
3.6.Exercises --
3.7.Case Study --
4.Linear Programming Models: Arbitrage and Asset Pricing --
4.1.Arbitrage Detection in the Foreign Exchange Market --
4.2.The Fundamental Theorem of Asset Pricing --
4.3.One-Period Binomial Pricing Model --
4.4.Static Arbitrage Bounds Note continued: 4.5.Tax Clientele Effects in Bond Portfolio Management --
4.6.Notes --
4.7.Exercises --
pt. II Single-Period Models --
5.Quadratic Programming: Theory and Algorithms --
5.1.Quadratic Programming --
5.2.Numerical Quadratic Programming Solvers --
5.3.Sensitivity Analysis --
5.4.*Duality and Optimality Conditions --
5.5.*Algorithms --
5.6.Applications to Machine Learning --
5.7.Exercises --
6.Quadratic Programming Models: Mean-Variance Optimization --
6.1.Portfolio Return --
6.2.Markowitz Mean-Variance (Basic Model) --
6.3.Analytical Solutions to Basic Mean-Variance Models --
6.4.More General Mean-Variance Models --
6.5.Portfolio Management Relative to a Benchmark --
6.6.Estimation of Inputs to Mean-Variance Models --
6.7.Performance Analysis --
6.8.Notes --
6.9.Exercises --
6.10.Case Studies --
7.Sensitivity of Mean-Variance Models to Input Estimation --
7.1.Black-Litterman Model --
7.2.Shrinkage Estimation --
7.3.Resampled Efficiency Note continued: 7.4.Robust Optimization --
7.5.Other Diversification Approaches --
7.6.Exercises --
8.Mixed Integer Programming: Theory and Algorithms --
8.1.Mixed Integer Programming --
8.2.Numerical Mixed Integer Programming Solvers --
8.3.Relaxations and Duality --
8.4.Algorithms for Solving Mixed Integer Programs --
8.5.Exercises --
9.Mixed Integer Programming Models: Portfolios with Combinatorial Constraints --
9.1.Combinatorial Auctions --
9.2.The Lockbox Problem --
9.3.Constructing an Index Fund --
9.4.Cardinality Constraints --
9.5.Minimum Position Constraints --
9.6.Risk-Parity Portfolios and Clustering --
9.7.Exercises --
9.8.Case Study --
10.Stochastic Programming: Theory and Algorithms --
10.1.Examples of Stochastic Optimization Models --
10.2.Two-Stage Stochastic Optimization --
10.3.Linear Two-Stage Stochastic Programming --
10.4.Scenario Optimization --
10.5.*The L-Shaped Method --
10.6.Exercises --
11.Stochastic Programming Models: Risk Measures Note continued: 11.1.Risk Measures --
11.2.A Key Property of CVaR --
11.3.Portfolio Optimization with CVaR --
11.4.Notes --
11.5.Exercises --
pt. III Multi-Period Models --
12.Multi-Period Models: Simple Examples --
12.1.The Kelly Criterion --
12.2.Dynamic Portfolio Optimization --
12.3.Execution Costs --
12.4.Exercises --
13.Dynamic Programming: Theory and Algorithms --
13.1.Some Examples --
13.2.Model of a Sequential System (Deterministic Case) --
13.3.Bellman\'s Principle of Optimality --
13.4.Linear-Quadratic Regulator --
13.5.Sequential Decision Problem with Infinite Horizon --
13.6.Linear-Quadratic Regulator with Infinite Horizon --
13.7.Model of Sequential System (Stochastic Case) --
13.8.Notes --
13.9.Exercises --
14.Dynamic Programming Models: Multi-Period Portfolio Optimization --
14.1.Utility of Terminal Wealth --
14.2.Optimal Consumption and Investment --
14.3.Dynamic Trading with Predictable Returns and Transaction Costs Note continued: 14.4.Dynamic Portfolio Optimization with Taxes --
14.5.Exercises --
15.Dynamic Programming Models: the Binomial Pricing Model --
15.1.Binomial Lattice Model --
15.2.Option Pricing --
15.3.Option Pricing in Continuous Time --
15.4.Specifying the Model Parameters --
15.5.Exercises --
16.Multi-Stage Stochastic Programming --
16.1.Multi-Stage Stochastic Programming --
16.2.Scenario Optimization --
16.3.Scenario Generation --
16.4.Exercises --
17.Stochastic Programming Models: Asset-Liability Management --
17.1.Asset-Liability Management --
17.2.The Case of an Insurance Company --
17.3.Option Pricing via Stochastic Programming --
17.4.Synthetic Options --
17.5.Exercises --
pt. IV Other Optimization Techniques --
18.Conic Programming: Theory and Algorithms --
18.1.Conic Programming --
18.2.Numerical Conic Programming Solvers --
18.3.Duality and Optimality Conditions --
18.4.Algorithms --
18.5.Notes --
18.6.Exercises --
19.Robust Optimization Note continued: 19.1.Uncertainty Sets --
19.2.Different Flavors of Robustness --
19.3.Techniques for Solving Robust Optimization Models --
19.4.Some Robust Optimization Models in Finance --
19.5.Notes --
19.6.Exercises --
20.Nonlinear Programming: Theory and Algorithms --
20.1.Nonlinear Programming --
20.2.Numerical Nonlinear Programming Solvers --
20.3.Optimality Conditions --
20.4.Algorithms --
20.5.Estimating a Volatility Surface --
20.6.Exercises --
Appendices --
Appendix Basic Mathematical Facts --
A.1.Matrices and Vectors --
A.2.Convex Sets and Convex Functions --
A.3.Calculus of Variations: the Euler Equation.