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Finance[GaussMarkovProcess] - create new Gauss-Markov short-rate process

Calling Sequence

GaussMarkovProcess(r0, g, h, sigma, t, opts)

Parameters

r0

-

algebraic expression; initial value

g

-

algebraic expression, procedure, or yield term structure; the adjusted mean

h

-

algebraic expression, procedure, or yield term structure; speed of mean reversion

sigma

-

algebraic expression, procedure, or yield term structure; the non-negative volatility

t

-

(optional) name; time variable

opts

-

(optional) equation(s) of the form option = value where option is scheme; specify options for the GaussMarkovProcess command

Description

• 

The GaussMarkovProcess command creates a linear Gauss-Markov stochastic process Xt, which is governed by the stochastic differential equation (SDE)

dXt=htgtXtdt+σtdWt

  

The function gt is selected so that the model fits the initial term structure. The functions ht and σt are volatility parameters that are chosen to fit the market prices of a set of actively traded interest rate options.

• 

This process can be used to model short-term interest rates. This model - introduced by Hull and White (1990) - contains many popular term structure models as special cases.

 

Vasicek model:

gt, ht and σt are constant.

 

 

Ho-Lee model:

ht=0 and σt is constant.

 

 

Hull-White model:

ht and σt are constant.

 

• 

The parameter r0 defines the initial value of the underlying stochastic process.

• 

The parameter g is the adjusted mean. It can be either an algebraic expression, a procedure, or a yield term structure. The parameters h and sigma are the volatility parameters. In general, g, h, and sigma can be any algebraic expressions. However, if the process is to be simulated, these parameters must be assigned numeric values.

Examples

withFinance:

V:=GaussMarkovProcess0.5,0.2,1.0,0.3:

PathPlotVt,t=0..30,timesteps=100,replications=5,color=red..blue,thickness=3,axes=BOXED,gridlines=true

T:=2.0,5.0,10.0,30.0

T:=2.0,5.0,10.0,30.0

(1)

R:=0.04976,0.04970,0.05064,0.05143

R:=0.04976,0.04970,0.05064,0.05143

(2)

g:=CurveFitting:-SplineT,R,t,degree=1

g:=&lcub;0.049800000000.00002000000000tt<5.00.04876000000&plus;0.0001880000000tt<10.00.05024500000&plus;0.00003950000000totherwise

(3)

R:=GaussMarkovProcess0.1&comma;g&comma;1.0&comma;0.3&comma;t

R:=_X0

(4)

PathPlotRt&comma;t&equals;0..30&comma;timesteps&equals;100&comma;replications&equals;5&comma;color&equals;red..blue&comma;thickness&equals;3&comma;axes&equals;BOXED&comma;gridlines&equals;true

rates:=0.02&comma;0.01&comma;0.04&comma;0.06&comma;0.07&colon;

times:=0.0&comma;0.5&comma;1.0&comma;1.5&comma;2.0&colon;

R:=ZeroCurvetimes&comma;rates

R:=moduleend module

(5)

X:=GaussMarkovProcess0.02&comma;R&comma;1.0&comma;0.1

X:=_X1

(6)

PathPlotXt&comma;t&equals;0..2&comma;timesteps&equals;100&comma;replications&equals;10&comma;axes&equals;BOXED&comma;gridlines&equals;true

rates:=0.02&comma;0.01&comma;0.04&comma;0.06&comma;0.07&colon;

times:=0.0&comma;0.5&comma;1.0&comma;1.5&comma;2.0&colon;

R:=ZeroCurvetimes&comma;rates

R:=moduleend module

(7)

Y:=GaussMarkovProcess0.02&comma;R&comma;1.0&comma;0.1&comma;t&comma;scheme&equals;unbiased

Y:=_X2

(8)

PathPlotYt&comma;t&equals;0..2&comma;timesteps&equals;100&comma;replications&equals;10&comma;axes&equals;BOXED&comma;gridlines&equals;true

ExpectedValueY1&comma;replications&equals;104

value&equals;0.08445415108&comma;standarderror&equals;0.001785016918

(9)

SampleValuesY1&comma;replications&equals;104

1 .. 10000 ArrayData Type: float8Storage: rectangularOrder: C_order

(10)

Note that the overhead of computing the transition densities does not depend on the number of replications.

timeSamplePathYt&comma;t&equals;0..2&comma;timesteps&equals;100&comma;replications&equals;10

1.493

(11)

timeSamplePathYt&comma;t&equals;0..2&comma;timesteps&equals;100&comma;replications&equals;104

1.466

(12)

See Also

Finance[BlackScholesProcess], Finance[BrownianMotion], Finance[Diffusion], Finance[Drift], Finance[ExpectedValue], Finance[GeometricBrownianMotion], Finance[ItoProcess], Finance[OrnsteinUhlenbeckProcess], Finance[SamplePath], Finance[SampleValues], Finance[SquareRootDiffusion], Finance[StochasticProcesses], Finance[WienerProcess]

References

  

Brigo, D., Mercurio, F., Interest Rate Models: Theory and Practice. New York: Springer-Verlag, 2001.

  

Glasserman, P., Monte Carlo Methods in Financial Engineering. New York: Springer-Verlag, 2004.

  

Ho, T.S.Y, and Loo, S.-B., Term Structure Movements and Pricing Interest Rate Contingent Claims, Journal of Finance, 41 (1986), pp. 1011-29.

  

Hull, J., Options, Futures, and Other Derivatives, 5th. edition. Upper Saddle River, New Jersey: Prentice Hall, 2003.

  

Hull, J., and White, A., Pricing Interest Rate Derivative Securities, Review of Financial Studies, 3 (1990), pp. 573-92.

  

Vasicek, O.A., An Equilibrium Characterization of the Term Structure, Journal of Financial Economics, 5 (1977), pp. 177-88.


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