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Stochastic Calculus: A Practical Introduction

文件格式:Pdf 可复制性:可复制 TAG标签: stochastic Calculus Introduction Practical 点击次数: 更新时间:2009-09-28 15:40
介绍

1. Brownian Motion
   1.1 Definition and Construction  1
     1.2  Markov Property, Blumenthal's 0-1 Law  7
    1.3  Stopping Times, Strong Markov Property  18
   1.4 First Formulas  26  i .   '
2. Stochastic Integration
   2.1  Integrands: Predictable Processes  33     :
   2.2  Integtators: Continuous LocalMartingales  37
   2.3  Vaiiance and Covariance Processes 42
    2.4  Integrat,ion w.r.t. Bounded Martingales  52
   2.5  The Kunita-Watanabelnequality 59  :;   ;
    2.6   Integration w.r.t. Local Martingales  63
   2.7  Chaoge ofVatiables,lto's Formula 68
   2.8  Integration w.r.t. Semimartingales  70
   2.9  Associative Law 74
    2~.10 Functions of Several Semi.martingales  76
        Chapter Summary 79
     2.11 Meyer-Tan_aka Formula, Local Time  82
   2.12 Girsanov's Formula 90
3.  Brownian Motion, II
   3.1 Recurrence and IYansience  95
   3.2 0ccupation Times 100
    3.3 Exit Times  105
    3.4  Change ofTime, Levy's Theorem  111
   3.5 Burkholder Davis Gundylnequalities  116
    3.6 Martingales Adapted to Brownian Filtrations  119
4. Partial Drfferential Equations
   A. Parabolic Equations
   4.1 The:EIeat Equation 126
   4.2 Thelnhomogeneous Equation  130
   4.3 The Feynman-Kac Formula  137
   B. Elliptic Equations
   4.4 The Dirichlet Problem  143
   4.5 Poisson's Equation  151
   4.6 The Schrodinger E;quation_  156
   C. Applications to Brownian Motion
    4.7  Exit Distributions for the Bail  164
    4.8  0ccupation Times for the Ball  167
     4.9  Laplace Transforms, Arcsine Law  170
5. Stochastic DiflFerential Equations
  5.1 Examples 177
   5.2 Ito's Approach  183
  5.3 lExtension 190
   5.4 Weak Solutions 196
   5.5  Change ofMeasure  202
   5.6  Change ofTime  207
6. One Dimensional Drffusions
  6.1onstruction_ 211
   6.2 Feller's Test  214
   6.3 Recurrence and thansience  219
   6.4 Green's Functions  222
   6.5 Boundary Behavior  229
    6.6  Applications to lIighet Dimensions  234
7. Diffusions as Markov Processes
   7.1 Semigroups and Generators 245
  7.2 Examples 250
   7.3tansition Probabilities 255
   7.4 lE[anis Chains 258
   7.5 Convergerice Theorems 268
8. Weak Convergence
   8.1 In Metric Spaces 271
    8.2 Prokhorov's Theorems  276
    8.3 The Space C  282
    8.4 Skorohod's Existence Theorem for SDE  285
    8.5 Donsker's Theorem 287
   8.6 The Space D 293
    8.7 Convergence to DHfusions  296
  8.8 Examples 305
 Solutions to Exercises  311
References 335
Index 339

 

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