chap5 - Chapter 5 Stochastic Differential Equations We...

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Unformatted text preview: Chapter 5 Stochastic Differential Equations We would like to introduce stochastic ODEs without going first through the machinery of stochastic integrals. 5.1 Ito Integrals and Ito Differential Equations Let us start with a review of the invariance principle. Let { n } n N be a sequence of i.i.d. random variables such that E n = 0, E 2 n = 1. Define X N t by X N t n = n i =1 i N , t n = n N , n N (5.1) and piecewise linear interpolation, then the invariance principle asserts that X N d W (5.2) in distribution. Alternatively, we can define { X N t n } N n =1 by the recursion relation X N t n +1 = X N t n + t n +1 , X N = 0 , (5.3) where t = N- 1 . We can think of (5.3) as a forward Euler scheme for solving the differential equation dX t = dW t (5.4) Obviously both (5.3) and (5.4) are a bit unusual. In (5.3), the multiplier in front of n +1 is t instead of the usual t . In (5.4) we write dX t = dW t instead of X t = W t . This is because W t is not a standard stochastic process, but rather a generalized stochastic process as in generalized functions. In fact one can define W t as a Gaussian process with mean zero and covariance K ( s,t ) = ( t- s ) . 41 42 CHAPTER 5. STOCHASTIC DIFFERENTIAL EQUATIONS This is a very important process called the Gaussian white noise. But its sample path are not the standard functions, but rather distributions, see [5]. We can now combine (5.4) with standard ordinary differential equation and study dX t = b ( X t ,t ) dt + dW t . (5.5) One can think of this as the distributional limit of the forward Euler scheme X N t n +1 = X N t n + tb ( X t n ,t n ) + t n +1 (5.6) where as before { n } n =1 is a sequence of i.i.d. random variables such that E n = 0, E 2 n = 1. In fact if we denote by X N t the process obtained using (5.6) and the piecewise linear interpolation, then Theorem 5.1.1. There exists a stochastic process X t such that E | X N t- X t | C t (5.7) for t [0 , 1] , where C is independent of t = 1 /N , and vextendsingle vextendsingle vextendsingle E g [ X N [0 , 1] ]- E g [ X [0 , 1] ] vextendsingle vextendsingle vextendsingle C t (5.8) for any continuous functional g on C [0 , 1] , where C may depend on g but not on t . More generally, we can consider SDEs of the type dX t = b ( X t ,W [0 ,t ] ,t ) dt + ( X t ,W [0 ,t ] ,t ) dW t (5.9) where B and sigma are functions of X t and t , and functional of W [0 ,t ] . Notice that they are nonanticipative functional, i.e. the Wiener process up to time t only enters the right hand-sidde of (5.9). (5.9) is defined it as the limit of the forward Euler scheme X n +1 = X n + tb ( X n ,W m n ,t n ) + t ( X n ,W m n ,t n ) n +1 . (5.10) where, for simplicity we have used the slightly abusive notation X N t n = X n . We can also write this as X n +1 = X n + tb ( X n ,W m n ,t n ) + ( X n ,W m n ,t n )(...
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chap5 - Chapter 5 Stochastic Differential Equations We...

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