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Workshop on Stochastic Differential Equations and Statistical Inference for Markov Processes Day 1: January 19 th, Day 2: January 28 th Lahore University of Management Sciences
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Schedule Day 1 ( Saturday 21 st Jan ): Review of Probability and Markov Chains Day 2 ( Saturday 28 th Jan ): Theory of Stochastic Differential Equations Day 3 ( Saturday 4 th Feb ): Numerical Methods for Stochastic Differential Equations Day 4 ( Saturday 11 th Feb ): Statistical Inference for Markovian Processes
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Today Continuous Time Continuous Space Processes Stochastic Integrals Ito Stochastic Differential Equations Analysis of Ito SDE
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CONTINUOUS TIME CONTINUOUS SPACE PROCESSES
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Mathematical Foundations X(t) is a continuous time continuous space process if The State Space is or or The index set is X(t) has pdf that satisfies X(t) satisfies the Markov Property if
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Transition pdf The transition pdf is given by Process is homogenous if In this case
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Chapman Kolmogorov Equations For a continuous time continuous space process the Chapman Kolmogorov Equations are If The C-K equation in this case become
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From Random Walk to Brownian Motion Let X(t) be a DTMC (governing a random walk) Note that if Then satisfies Provided
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Symmetric Random Walk: ‘Brownian Motion’ In the symmetric case satisfies If the initial data satisfies The pdf of evolves in time as
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Standard Brownian Motion If and the process is called standard Brownian Motion or ‘Weiner Process’ Note over time period – Mean = – Variance = Over the interval [0,T] we have – Mean = – Variance =
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Diffusion Processes A continuous time continuous space Markovian process X(t), having transition probability is a diffusion process if the pdf satisfies – i) – ii) – Iii)
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Equivalent Conditions Equivalently
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Kolmogorov Equations Using the C-K equations and the finiteness conditions we can derive the Backward Kolmogorov Equation For a homogenous process
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The Forward Equation THE FKE (Fokker Planck equation) is given by If the BKE is written as The FKE is given by
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Brownian Motion Revisited The FKE and BKE are the same in this case If X(0)=0, then the pdf is given by
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Weiner Process W(t) CT-CS process is a Weiner Process if W(t) depends continuously on t and the following hold a) b) are independent c)
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Weiner Process is a Diffusion Process Let Then These are the conditions for a diffusion process
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Ito Stochastic Integral Let f(x(t),t) be a function of the Stochastic Process X(t) The Ito Stochastic Integral is defined if The integral is defined as where the limit is in the sense that given means
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Properties of Ito Stochastic Integral Linearity Zero Mean Ito Isometry
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Evaluation of some Ito Integrals Not equal to Riemann Integrals!!!!
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Ito Stochastic Differential Equations A Stochastic Process is said to satisfy an Ito SDE if it is a solution of Riemann Ito
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Existence & Uniqueness Results Stochastic Process X(t) which is a solution of if the following conditions hold Similarity to Lipchitz Conditions!!
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Evolution of the pdf The solution of an Ito SDE is a diffusion process It’s pdf then satisfies the FKE
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Some Ito Stochastic Differential Equations Arithmetic Brownian Motion Geometric Brownian Motion Simple Birth and Death Process
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Ito’s Lemma If X(t) is a solution of and F is a real valued function with continuous partials, then Chain Rule of Ito Calculus!!
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Solving SDE using Ito’s Lemma Geometric Brownian Motion Let Then the solution is Note that
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