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Mathematical Methods and Quantum Mathematics for Economics and Finance

Mathematical Methods and Quantum Mathematics for Economics and Finance (Hardcover)

Belal Ehsan Baaquie (지은이)
Springer
255,170원

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Mathematical Methods and Quantum Mathematics for Economics and Finance
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· 제목 : Mathematical Methods and Quantum Mathematics for Economics and Finance (Hardcover) 
· 분류 : 외국도서 > 경제경영 > 경제학/경제일반 > 경제학이론
· ISBN : 9789811566103
· 쪽수 : 432쪽
· 출판일 : 2020-08-11

목차

PART I : INTRODUCTION

1 Introduction

1.1 Introduction

1.2 Elementary Algebra

1.2.1 Quadratic polynomial

1.3 Finite Series

1.4 Infinite Series

1.4.1 Cauchy convergence

1.5 Problems

 

2 Functions

2.1 Introduction

2.2 Exponential function

2.3 Demand and supply function

2.4 Option theory payoff

2.5 Interest rates; bonds

2.6 Problems

 

PART II : LINEAR ALGEBRA

3 Simultaneous linear equations

3.1 Introduction

3.2 Two commodities

3.3 Vectors
3.4 Basis vectors
3.4.1 Scalar product
3.5 Linear transformations; matrices

3.6 EN: N-dimensional linear vector space

3.7 Linear transformations of EN

3.8 Problems


4 Matrices

4.1 Introduction

4.2 Matrix multiplication

4.3 Properties of N × N matrices

4.4 System of linear equations

4.5 Determinant: 2 × 2 case

4.6 Inverse of a 2 × 2 matrix

4.7 Outer product; transpose

4.7.1 Transpose

4.8 Eigenvalues and eigenvectors

4.8.1 Spectral decomposition

4.9 Problems

 

5 Square matrices

5.1 Determinant: 3 × 3 case

5.2 Properties of determinants

5.3 N × N determinant

5.3.1 Inverse of a N × N matrix

5.4 Leontief input-output model

5.4.1 Hawkins-Simon condition

5.5 Symmetric matrices

5.6 Symmetric matrix: diagonalization

5.6.1 Functions of a symmetric matrix

5.7 Hermitian matrices

5.8 Diagonalizable matrices

5.8.1 Non-symmetric matrix

5.9 Change of Basis states

5.9.1 Symmetric matrix: change of basis

5.9.2 Hermitian matrix: change of basis

5.10 Problems

 

PART III : CALCULUS

6 Integration

6.1 Introduction

6.2 Sums leading to integrals

6.3 Definite and indefinite integrals

6.4 Applications in economics

6.5 Multiple Integrals

6.5.1 Change of variables

6.6 Gaussian integration

6.6.1 N-dimensional Gaussian integration

6.7 Problems

 

7 Differentiation

7.1 Introduction

7.2 Inverse of Integration

7.3 Rules of differentiation

7.4 Integration by parts

7.5 Taylor expansion

7.6 Minimum and maximum

7.6.1 Maximizing profit

7.7 Integration; change of variable

7.8 Partial derivatives

7.8.1 Chain rule; Jacobian

7.8.2 Polar coordinates; Gaussian integration

7.9 Hessian matrix: critical points

7.10 Constrained optimization: Lagrange multiplier

7.10.1 Interpretation of λc

7.11 Line integral; Exact and inexact differentials

7.12 Problems

 

8 Functional analysis

8.1 Dirac bracket and vector notation

8.2 Continuous basis states

8.3 Dirac delta function

8.4 Basis states for function space

8.5 Operators on function space

8.6 Gaussian kernel

8.7 Fourier Transform

8.8 Taylor expansion

8.9 Gaussian functional integration

8.10 Problems

 

9 Ordinary Differential Equations

9.1 Introduction

9.2 Separable differential equations

9.3 Linear differential equations

9.4 Bernoulli differential equation

9.5 Homegeneous differential equation

9.6 Second order linear differential equations

9.6.1 Single eigenvalue

9.7 Ricatti differential equation

9.8 Inhomogeneous second order differential equations

9.8.1 Green’s function

9.9 System of linear differential equations

9.10 Strum-Louisville theorem; special functions

9.11 Problems

 

PART IV : PROBABILITY THEORY

10 Random variables

10.1 Introduction: Risk

10.1.1 Example

10.2 Key ideas of probability

10.3 Discrete random variables

10.3.1 Bernoulli random variable

10.3.2 Binomial random variable

10.3.3 Poisson random variable

10.4 Continuous random variables

10.4.1 Uniform random variable

10.4.2 Exponential random variable

10.4.3 Normal (Gaussian) random variable

10.5 Problems

 

11 Probability distribution functions

11.0.1 Cumulative density

11.1 Axioms of probability theory

11.2 Joint probability density

11.3 Independent random variables

11.3.1 Law of large numbers

11.4 Correlated random variables

11.5 Marginal probability density

11.6 Conditional expectation value

11.6.1 Discrete random variable

11.6.2 Continuous random variables

11.7 Problems


12 Stochastic processes & Option pricing

12.1 Gaussian white noise

12.1.1 Integrals of White Noise

12.2 Ito Calculus

12.3 Lognormal Stock Price

12.4 Black-Scholes Equation; Hedged Portfolio

12.4.1 Assumptions in the Derivation of Black-Scholes

12.5 Risk-Neutral Martingale Solution of the Black-Scholes Equation

12.6 Black-Scholes-Schrodinger equation

12.7 Linear Langevin Equation

12.7.1 Random Paths

12.8 Problems

 

13 Appendix

13.1 Introduction

13.2 Integers

13.3 Real numbers

13.4 Cantor’s Diagonal Argument

13.5 Higher Order Infinities

13.6 Mathematical Logic

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