《隨機分析基礎》是2009年8月1日世界圖書出版公司出版的圖書
基本介紹
- 中文名：隨機分析基礎
- 別名：elementary stochastic calculus with finance in view
- 作者：(丹麥)麥考斯基
- 出版社：世界圖書出版公司
- 出版時間：2009年8月1日
- 頁數：212 頁
- 開本：24 開
- 裝幀：平裝
- ISBN：9787510005244, 7510005248
內容簡介,目錄,
內容簡介
《隨機分析基礎(英文版)》講述了：Iknewbetter.Atthattime.staftmembersofeconomicsandmathematicsdepartmentsalreadydiscussedtheuseoftheBlackandScholesoptionpricingformula；coursesonstochasticfinancewere0fieredatleadinginstitutionssuchasETHZfirich.ColumbiaandStanford；andthereWasageneralagreementthatnotonlystudentsandstaftmembersofeconomicsandmathematicsde-partments、butalsopractitionersinfinanciaiinstitutionsshouldknowmoreaboutthisnewtopic.SoonIrealizedthatthereWasnotverymuchliteraturewhichcouldbeusedforteachingstochasticcaiculusataratherelementarylevel.Ialnfullyawareofthefactthatacombinationof“elementary”and“stochasticcalculus”isacontradictioniUitselfStochasticcalculusrequiresadvancedmathematicaitechniques；thistheorycannotbefullvunderstoodifonedoesnotknowaboutthebasicsofmeasuretheory，functionalanalysisandthetheoryofstochasticprocessesHowever.Istronglybelievethataninterestedpersonwhoknowsaboutelementaryprobabilitytheoryandwhocanhandletherulesofinte-grationanddifierentiationisabletounderstandthemainideasofstochasticcalculus.ThisissupportedbymyexperiencewhichIgainedincoursesforeconomicsstatisticsandmathematicsstudentsatVUWWellingtonandtheDepartmentofMathematicsinGroningen.IgotthesameimpressionasalecturerofcrashcoursesonstochasticcalculusattheSummerSchOOl.
目錄
Reader Guidelines
1 Preliminaries
1.1 Basic Concepts fl'om Probability Theory
1.1.1 Random Variables
1.1.2 Random Vectors
1.1.3 Independence and Dependence
1.2 Stochastic Processes
1.3 Brownian Motion
1.3.1 Defining Properties
1.3.2 Processes Derived from Brownian Motion
1.3.3 Simulation of Brownian Sample Paths
1.4 Conditional Expectation
1.4.1 Conditional Expectation under Discrete Condition
1.4.2 About a-Fields
1.4.3 The General Conditional Expectation
1.4.4 Rules for the Calculation of Conditional Expectations
1.4.5 The Projection Property of Conditional Expectations
1.5 Martingales
1.5.1 Defining Properties
1.5.2 Examples
1.5.3 The Interpretation of a Martingale as a Fair Game
2 The Stochastic Integral
2.1 The Riemann and Riemann-Stieltjes Integrals
2.1.1 The Ordinary Riemann Integral
2.1.2 The Riemann-Stieltjes Integral
2.2 The Ito Integral
2.2.1 A Motivating Example
2.2.2 The Ito Stochastic Integral for Simple Processes
2.2.3 The General Ito Stochastic Integral
2.3 The Ito Lemma
2.3.1 The Classical Chain Rule of Differentiation
2.3.2 A Simple Version of the Ito Lemma
2.3.3 Extended Versions of the Ito Lemma
2.4 The Stratonovich and Other Integrals
3 Stochastic Differential Equations
3.1 Deterministic Differential Equations
3.2 Ito Stochastic Differential Equations
3.2.1 What is a Stochastic Differential Equation?
3.2.2 Solving Ito Stochastic Differential Equations by the ItoLemma
3.2.3 Solving Ito Differential Equations via Stratonovich Calculus
3.3 The General Linear Differential Equation
3.3.1 Linear Equations with Additive Noise
3.3.2 Homogeneous Equations with Multiplicative Noise
3.3.3 The General Case
3.3.4 The Expectation and Variance Functions of the Solution
3.4 Numerical Solution
3.4.1 The Euler Approximation
3.4.2 The Milstein Approximation
4 Applications of Stochastic Calculus in Finance
4.1 The Black-Scholes Option Pricing Formula
4.1.1 A Short Excursion into Finance
4.1.2 What is an Option?
4.1.3 A Mathematical Formulation of the Option Pricing Problem
4.1.4 The Black and Scholes Formula
4.2 A Useful Technique: Change of Measure
4.2.1 What is a Change of the Underlying Measure?
4.2.2 An Interpretation of the Black-Scholes Formula by Change of Measure
Appendix
A1 Modes of Convergence
A2 Inequalities
A3 Non-Differentiability and Unbounded Variation of Brownian Sample Paths
A4 Proof of the Existence of the General Ito Stochastic Integral
A5 The Radon-Nikodym Theorem
A6 Proof of the Existence and Uniqueness of the Conditional Expectation
Bibliography
Index
List of Abbreviations and Symbols
