A Historical Introduction | p. 1 |
Motivation | p. 1 |
Some Historical Examples | p. 2 |
Brownian Motion | p. 2 |
Langevin's Equation | p. 6 |
The Stock Market | p. 8 |
Statistics of Returns | p. 8 |
Financial Derivatives | p. 9 |
The Black-Scholes Formula | p. 10 |
Heavy Tailed Distributions | p. 10 |
Birth-Death Processes | p. 11 |
Noise in Electronic Systems | p. 14 |
Shot Noise | p. 14 |
Autocorrelation Functions and Spectra | p. 18 |
Fourier Analysis of Fluctuating Functions: Stationary Systems | p. 19 |
Johnson Noise and Nyquist's Theorem | p. 20 |
Probability Concepts | p. 23 |
Events, and Sets of Events | p. 23 |
Probabilities | p. 24 |
Probability Axioms | p. 24 |
The Meaning of P(A) | p. 25 |
The Meaning of the Axioms | p. 25 |
Random Variables | p. 26 |
Joint and Conditional Probabilities: Independence | p. 27 |
Joint Probabilities | p. 27 |
Conditional Probabilities | p. 27 |
Relationship Between Joint Probabilities of Different Orders | p. 28 |
Independence | p. 28 |
Mean Values and Probability Density | p. 29 |
Determination of Probability Density by Means of Arbitrary Functions | p. 30 |
Sets of Probability Zero | p. 30 |
The Interpretation of Mean Values | p. 31 |
Moments, Correlations, and Covariances | p. 32 |
The Law of Large Numbers | p. 32 |
Characteristic Function | p. 33 |
Cumulant Generating Function: Correlation Functions and Cumulants | p. 34 |
Example: Cumulant of Order 4: < | p. 36 |
Significance of Cumulants | p. 36 |
Gaussian and Poissonian Probability Distributions | p. 37 |
The Gaussian Distribution | p. 37 |
Central Limit Theorem | p. 38 |
The Poisson Distribution | p. 39 |
Limits of Sequences of Random Variables | p. 40 |
Almost Certain Limit | p. 40 |
Mean Square Limit (Limit in the Mean) | p. 41 |
Stochastic Limit, or Limit in Probability | p. 41 |
Limit in Distribution | p. 41 |
Relationship Between Limits | p. 41 |
Markov Processes | p. 42 |
Stochastic Processes | p. 42 |
Kinds of Stochastic Process | p. 42 |
Markov Process | p. 43 |
Consistency-the Chapman-Kolmogorov Equation | p. 44 |
Discrete State Spaces | p. 44 |
More General Measures | p. 45 |
Continuity in Stochastic Processes | p. 45 |
Mathematical Definition of a Continuous Markov Process | p. 46 |
Differential Chapman-Kolmogorov Equation | p. 47 |
Derivation of the Differential Chapman-Kolmogorov Equation | p. 48 |
Status of the Differential Chapman-Kolmogorov Equation | p. 51 |
Interpretation of Conditions and Results | p. 51 |
Jump Processes: The Master Equation | p. 51 |
Diffusion Processes-the Fokker-Planck Equation | p. 52 |
Deterministic Processes-Liouville's Equation | p. 54 |
General Processes | p. 55 |
Equations for Time Development in Initial Time-Backward Equations | p. 55 |
Stationary and Homogeneous Markov Processes | p. 56 |
Ergodic Properties | p. 57 |
Homogeneous Processes | p. 60 |
Approach to a Stationary Process | p. 60 |
Autocorrelation Function for Markov Processes | p. 63 |
Examples of Markov Processes | p. 65 |
The Wiener Process | p. 65 |
The Random Walk in One Dimension | p. 68 |
Poisson Process | p. 71 |
The Ornstein-Uhlenbeck Process | p. 72 |
Random Telegraph Process | p. 75 |
The Ito Calculus and Stochastic Differential Equations | p. 77 |
Motivation | p. 77 |
Stochastic Integration | p. 79 |
Definition of the Stochastic Integral | p. 79 |
Ito Stochastic Integral | p. 81 |
Example W(t')dW(t') | p. 81 |
The Stratonovich Integral | p. 82 |
Nonanticipating Functions | p. 82 |
Proof that dW(t)2 = dt and dW(t)2+N = 0 | p. 83 |
Properties of the Ito Stochastic Integral | p. 85 |
Stochastic Differential Equations (SDE) | p. 88 |
Ito Stochastic Differential Equation: Definition | p. 89 |
Dependence on Initial Conditions and Parameters | p. 91 |
Markov Property of the Solution of an Ito SDE | p. 92 |
Change of Variables: Ito's Formula | p. 92 |
Connection Between Fokker-Planck Equation and Stochastic Differential Equation | p. 93 |
Multivariable Systems | p. 95 |
The Stratonovich Stochastic Integral | p. 96 |
Definition of the Stratonovich Stochastic Integral | p. 96 |
Stratonovich Stochastic Differential Equation | p. 96 |
Some Examples and Solutions | p. 99 |
Coefficients without x Dependence | p. 99 |
Multiplicative Linear White Noise Process-Geometric Brownian Motion | p. 100 |
Complex Oscillator with Noisy Frequency | p. 101 |
Ornstein-Uhlenbeck Process | p. 103 |
Conversion from Cartesian to Polar Coordinates | p. 104 |
Multivariate Ornstein-Uhlenbeck Process | p. 105 |
The General Single Variable Linear Equation | p. 108 |
Multivariable Linear Equations | p. 110 |
Time-Dependent Ornstein-Uhlenbeck Process | p. 111 |
The Fokker-Planck Equation | p. 113 |
Probability Current and Boundary Conditions | p. 114 |
Classification of Boundary Conditions | p. 116 |
Boundary Conditions for the Backward Fokker-Planck Equation | p. 116 |
Fokker-Planck Equation in One Dimension | p. 117 |
Boundary Conditions in One Dimension | p. 118 |
Stationary Solutions for Homogeneous Fokker-Planck Equations | p. 120 |
Examples of Stationary Solutions | p. 122 |
Eigenfunction Methods for Homogeneous Processes | p. 124 |
Eigenfunctions for Reflecting Boundaries | p. 124 |
Eigenfunctions for Absorbing Boundaries | p. 126 |
Examples | p. 126 |
First Passage Times for Homogeneous Processes | p. 130 |
Two Absorbing Barriers | p. 130 |
One Absorbing Barrier | p. 132 |
Application-Escape Over a Potential Barrier | p. 133 |
Probability of Exit Through a Particular End of the Interval | p. 135 |
The Fokker-Planck Equation in Several Dimensions | p. 138 |
Change of Variables | p. 138 |
Stationary Solutions of Many Variable Fokker-Planck Equations | p. 140 |
Boundary Conditions | p. 140 |
Potential Conditions | p. 141 |
Detailed Balance | p. 142 |
Definition of Detailed Balance | p. 142 |
Detailed Balance for a Markov Process | p. 143 |
Consequences of Detailed Balance for Stationary Mean, Autocorrelation Function and Spectrum | p. 144 |
Situations in Which Detailed Balance must be Generalised | p. 144 |
Implementation of Detailed Balance in the Differential Chapman-Kolmogorov Equation | p. 145 |
Examples of Detailed Balance in Fokker-Planck Equations | p. 149 |
Kramers' Equation for Brownian Motion in a Potential | p. 149 |
Deterministic Motion | p. 152 |
Detailed Balance in Markovian Physical Systems | p. 153 |
Ornstein-Uhlenbeck Process | p. 153 |
The Onsager Relations | p. 155 |
Significance of the Onsager Relations-Fluctuation-Dissipation Theorem | p. 156 |
Eigenfunction Methods in Many Variables | p. 158 |
Relationship between Forward and Backward Eigenfunctions | p. 159 |
Even Variables Only-Negativity of Eigenvalues | p. 159 |
A Variational Principle | p. 160 |
Conditional Probability | p. 161 |
Autocorrelation Matrix | p. 161 |
Spectrum Matrix | p. 162 |
First Exit Time from a Region (Homogeneous Processes) | p. 163 |
Solutions of Mean Exit Time Problems | p. 164 |
Distribution of Exit Points | p. 166 |
Small Noise Approximations for Diffusion Processes | p. 169 |
Comparison of Small Noise Expansions for Stochastic Differential Equations and Fokker-Planck Equations | p. 169 |
Small Noise Expansions for Stochastic Differential Equations | p. 171 |
Validity of the Expansion | p. 174 |
Stationary Solutions (Homogeneous Processes) | p. 175 |
Mean, Variance, and Time Correlation Function | p. 175 |
Failure of Small Noise Perturbation Theories | p. 176 |
Small Noise Expansion of the Fokker-Planck Equation | p. 178 |
Equations for Moments and Autocorrelation Functions | p. 180 |
Example | p. 182 |
Asymptotic Method for Stationary Distribution | p. 184 |
The White Noise Limit | p. 185 |
White Noise Process as a Limit of Nonwhite Process | p. 185 |
Formulation of the Limit | p. 186 |
Generalisations of the Method | p. 189 |
Brownian Motion and the Smoluchowski Equation | p. 192 |
Systematic Formulation in Terms of Operators and Projectors | p. 194 |
Short-Time Behaviour | p. 195 |
Boundary Conditions | p. 197 |
Evaluation of Higher Order Corrections | p. 198 |
Adiabatic Elimination of Fast Variables: The General Case | p. 202 |
Example: Elimination of Short-Lived Chemical Intermediates | p. 202 |
Adiabatic Elimination in Haken's Model | p. 206 |
Adiabatic Elimination of Fast Variables: A Nonlinear Case | p. 210 |
An Example with Arbitrary Nonlinear Coupling | p. 214 |
Beyond the White Noise Limit | p. 216 |
Specification of the Problem | p. 217 |
Eigenfunctions of L1 | p. 218 |
Projectors | p. 218 |
Bloch's Perturbation Theory | p. 219 |
Formalism for the Perturbation Theory | p. 220 |
Application of Bloch's Perturbation Theory | p. 222 |
Construction of the Conditional Probability | p. 223 |
Stationary Solution Ps(x,p) | p. 226 |
Examples | p. 226 |
Generalisation to a system driven by several Markov Processes | p. 228 |
Computation of Correlation Functions | p. 230 |
Special Results for Ornstein-Uhlenbeck p(t) | p. 232 |
Generalisation to Arbitrary Gaussian Inputs | p. 232 |
The White Noise Limit | p. 233 |
Relation of the White Noise Limit of | p. 233 |
Levy Processes and Financial Applications | p. 235 |
Stochastic Description of Stock Prices | p. 235 |
The Brownian Motion Description of Financial Markets | p. 237 |
Financial Assets | p. 237 |
"Long" and "Short" Positions | p. 238 |
Perfect Liquidity | p. 238 |
The Black-Scholes Formula | p. 238 |
Explicit Solution for the Option Price | p. 240 |
Analysis of the Formula | p. 242 |
The Risk-Neutral Formulation | p. 244 |
Change of Measure and Girsanov's Theorem | p. 245 |
Heavy Tails and Levy Processes | p. 248 |
Levy Processes | p. 248 |
Infinite Divisibility | p. 249 |
The Poisson Process | p. 250 |
The Compound Poisson Process | p. 250 |
Levy Processes with Infinite Intensity | p. 251 |
The Levy-Khinchin Formula | p. 252 |
The Paretian Processes | p. 252 |
Shapes of the Paretian Distributions | p. 255 |
The Events of a Paretian Process | p. 255 |
Stable Processes | p. 257 |
Other Levy processes | p. 258 |
Modelling the Empirical Behaviour of Financial Markets | p. 258 |
Stylised Statistical Facts on Asset Returns | p. 258 |
The Paretian Process Description | p. 259 |
Implications for Realistic Models | p. 259 |
Equivalent Martingale Measure | p. 260 |
Hyperbolic Models | p. 262 |
Choice of Models | p. 262 |
Epilogue-the Crash of 2008 | p. 263 |
Master Equations and Jump Processes | p. 264 |
Birth-Death Master Equations-One Variable | p. 264 |
Stationary Solutions | p. 265 |
Example: Chemical Reaction X A | p. 267 |
A Chemical Bistable System | p. 269 |
Approximation of Master Equations by Fokker-Planck Equations | p. 273 |
Jump Process Approximation of a Diffusion Process | p. 273 |
The Kramers-Moyal Expansion | p. 275 |
Van Kampen's System Size Expansion | p. 276 |
Kurtz's Theorem | p. 280 |
Critical Fluctuations | p. 281 |
Boundary Conditions for Birth-Death Processes | p. 283 |
Mean First Passage Times | p. 284 |
Probability of Absorption | p. 286 |
Comparison with Fokker-Planck Equation | p. 286 |
Birth-Death Systems with Many Variables | p. 287 |
Stationary Solutions when Detailed Balance Holds | p. 288 |
Stationary Solutions Without Detailed Balance (Kirchoff's Solution) | p. 290 |
System Size Expansion and Related Expansions | p. 291 |
Some Examples | p. 291 |
X + A 2X | p. 291 |
X Y A | p. 292 |
Prey-Predator System | p. 292 |
Generating Function Equations | p. 297 |
The Poisson Representation | p. 301 |
Formulation of the Poisson Representation | p. 301 |
Kinds of Poisson Representations | p. 305 |
Real Poisson Representations | p. 305 |
Complex Poisson Representations | p. 306 |
The Positive Poisson Representation | p. 309 |
Time Correlation Functions | p. 313 |
Interpretation in Terms of Statistical Mechanics | p. 314 |
Linearised Results | p. 318 |
Trimolecular Reaction | p. 318 |
Fokker-Planck Equation for Trimolecular Reaction | p. 319 |
Third-Order Noise | p. 323 |
Example of the Use of Third-Order Noise | p. 324 |
Simulations Using the Positive Poisson representation | p. 326 |
Analytic Treatment via the Deterministic Equation | p. 326 |
Full Stochastic Case | p. 329 |
Testing the Validity of Positive Poisson Simulations | p. 331 |
Application of the Poisson Representation to Population Dynamics | p. 332 |
The Logistic Model | p. 332 |
Poisson Representation Stochastic Differential Equation | p. 333 |
Environmental Noise | p. 333 |
Extinction | p. 334 |
Spatially Distributed Systems | p. 336 |
Background | p. 336 |
Functional Fokker-Planck Equations | p. 338 |
Multivariate Master Equation Description | p. 339 |
Continuum Form of Diffusion Master Equation | p. 341 |
Combining Reactions and Diffusion | p. 345 |
Poisson Representation Methods | p. 346 |
Spatial and Temporal Correlation Structures | p. 347 |
Reaction X Y | p. 347 |
Reactions B + X C, A + X 2X | p. 350 |
A Nonlinear Model with a Second-Order Phase Transition | p. 355 |
Connection Between Local and Global Descriptions | p. 359 |
Explicit Adiabatic Elimination of Inhomogeneous Modes | p. 360 |
Phase-Space Master Equation | p. 362 |
Treatment of Flow | p. 362 |
Flow as a Birth-Death Process | p. 363 |
Inclusion of Collisions-the Boltzmann Master Equation | p. 366 |
Collisions and Flow Together | p. 369 |
Bistability, Metastability, and Escape Problems | p. 372 |
Diffusion in a Double-Well Potential (One Variable) | p. 372 |
Behaviour for D = 0 | p. 373 |
Behaviour if D is Very Small | p. 373 |
Exit Time | p. 375 |
Splitting Probability | p. 375 |
Decay from an Unstable State | p. 377 |
Equilibration of Populations in Each Well | p. 378 |
Kramers' Method | p. 378 |
Example: Reversible Denaturation of Chymotrypsinogen | p. 382 |
Bistability with Birth-Death Master Equations (One Variable) | p. 384 |
Bistability in Multivariable Systems | p. 386 |
Distribution of Exit Points | p. 387 |
Asymptotic Analysis of Mean Exit Time | p. 391 |
Kramers' Method in Several Dimensions | p. 392 |
Example: Brownian Motion in a Double Potential | p. 394 |
Simulation of Stochastic Differential Equations | p. 401 |
The One Variable Taylor Expansion | p. 402 |
Euler Methods | p. 402 |
Higher Orders | p. 402 |
Multiple Stochastic Integrals | p. 403 |
The Euler Algorithm | p. 404 |
Milstein Algorithm | p. 406 |
The Meaning of Weak and Strong Convergence | p. 407 |
Stability | p. 407 |
Consistency | p. 410 |
Implicit and Semi-implicit Algorithms | p. 410 |
Vector Stochastic Differential Equations | p. 411 |
Formulae and Notation | p. 412 |
Multiple Stochastic Integrals | p. 412 |
The Vector Euler Algorithm | p. 414 |
The Vector Milstein Algorithm | p. 414 |
The Strong Vector Semi-implicit Algorithm | p. 415 |
The Weak Vector Semi-implicit Algorithm | p. 415 |
Higher Order Algorithms | p. 416 |
Stochastic Partial Differential Equations | p. 417 |
Fourier Transform Methods | p. 418 |
The Interaction Picture Method | p. 419 |
Software Resources | p. 420 |
References | p. 421 |
Bibliography | p. 429 |
Author Index | p. 434 |
Symbol Index | p. 435 |
Subject Index | p. 439 |
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