Stochastic Difierential Equations

Bernt ksendalStochastic Differential EquationsAn Introduction with ApplicationsFifth Edition, Corrected PrintingSpringer-Verlag Heidelberg New YorkSpringer-VerlagBerlin Heidelberg NewYorkLondon Paris TokyoHong Kong BarcelonaBudapestTo My FamilyEva, Elise, Anders and Karina2 The front cover shows four sample pathsXt( 1), Xt( 2), Xt( 3) andXt( 4)of a geometric Brownian motionXt( ), of the solution of a (1-dimensional) Stochastic differential equation of the formdXtdt= (r+ Wt)Xtt 0 ;X0=xwherex, rand are constants andWt=Wt( ) is white noise. This process isoften used to model exponential growth under uncertainty . See Chapters 5,10, 11 and figure is a computer simulation for the casex=r= 1, = mean value ofXt,E[Xt] = exp(t), is also drawn. Courtesy of Jan Ub e,Stord/Haugesund have not succeeded in answering all our answers we have found only serve to raise a whole setof new questions. In some ways we feel we are as confusedas ever, but we believe we are confused on a higher leveland about more important outside the mathematics reading room,Troms UniversityPreface to Corrected Printing, Fifth EditionThe main corrections and improvements in this corrected printing are fromChaper 12.

10, 11 and 12. The flgure is a ... lem in terms of stochastic difierential equations, and we apply the results of Chapters VII and VIII to show that the problem can be reduced to solving the (deterministic) Hamilton-Jacobi-Bellman equation. As an illustration we solve a problem about optimal portfolio selection.

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  Equations, Difierential, Stochastic, Stochastic difierential equations

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