1 / 20

# Application of Optimal Control on Financial Modeling - PowerPoint PPT Presentation

Application of Optimal Control on Financial Modeling. Some Financial Models Introduce an oscillator problem The computational algorithms for the oscillator problem An investment model and the computational results of this model. The asset pricing model of Lucas(1978)

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

## PowerPoint Slideshow about ' Application of Optimal Control on Financial Modeling' - burton-maynard

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

• Some Financial Models

• Introduce an oscillator problem

• The computational algorithms for the oscillator problem

• An investment model and the computational results of this model

The model of Gourieroux and Janiak(2001)

Merton’s model

Some Financial Models

• In this model, the present value of utilities u(ct) is maximized.

• Single asset pays dividends according to stochastic model yt+1 = ryt + et+1

• In this model, the present value of utility function of consumption is maximized, subject to a “ budget constraint” qtCt+atpt = Rt+at+1pt

• The objective to be optimized is an integral of utility of consumption over the planning period, plus a function of wealth at the end time.

Consider a dynamic system described by a second - order differential equation:x1(t) + ßx1(t) + T2x1(t) = T2u(t)the objective is:MIN J =0T |x1(t) - (t)|dt

The computational algorithms differential equation:

• Piecewise-linear transformation

• Non-linear time scale transformation

• Oscillator transformation

• The computational algorithms

Piecewise-linear transformation: differential equation:.The time interval [0,1] is divided as follows:0 = t0 < t1 < … < tj < tj+1 < … < tr = 1.A scale time  takes values: = 0,h,2h,…, jh,(j+1)h,…,rh = 1.The relationship between  and t:t = () = h-1(tj+1-tj)(-h(j-1))+tj

Non-linear time scale transformation differential equation:

• Goh and Teo introduced a change of time scale for a time-optimal control problem while the terminal time T is a variable. Let t = T where  is the new time variable mapping in [0,1], t is again written for , then problem becomes a fixed-time optimal control problem with interval [0,1] and parameter T. This allows T to be computed accurately without being interpolated between subdivision points.

First, the time horizon [0,1] is divided into nb “big subintervals”, with end-points: 0=pt0 < pt1 <…< ptj < pt j+1 < … < ptnb=1

Then each subinterval [ptj, ptj+1] subdivided into ns “small subintervals”

A scale time  is constructed for the computer package, corresponding to the total number of subintervals nn=nb*ns,

Oscillator transformation

Computational algorithms subintervals”, with end-points: 0=pt

• Main program for the oscillator problem

• Minimizing Program

• Input function for second order differential equation

• Input function for the oscillator problem

An application to an optimal control problem subintervals”, with end-points: 0=pt

FDM (financial decision-making) model subintervals”, with end-points: 0=pt

• Problem description

• Analytical Solution

• Computational algorithms for the non-linear optimal control problem

• computing results and conclusion

Problem description subintervals”, with end-points: 0=pt

• The objective of this model is to maximize the present value of the owners’ shares

• Max P(T)eT + T0e-t[1-ur(t)]rE(t)dt subject to:

P(t) = c[{1-ur(t)}rE(t)- P(t)]

E(t) = rE(t)[ur(t)+us(t){1-E/(1-)P(t)}]

u(t) U = u|ur+us k/r <1, ur0, us 0

Analytical solution subintervals”, with end-points: 0=pt

• The solution shows the classical bang-bang control.

• In the case (a) :

• A: ur* = 0, us* = 0

• B: ur* = k/r, us* = 0

• C: ur* = 0, us* = k/r

Computational algorithms subintervals”, with end-points: 0=pt

• Main model program

• The minimization of the calling program

• The state function calculation

• The integral calculation

• End point condition

Computing results subintervals”, with end-points: 0=pt

Conclusion subintervals”, with end-points: 0=pt