Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Library: finance
See also: stocksim stockestsim

Quantlet: stockest
Description: stockest is estimating from a given dataset of a random process parameters for the following models: assuming a Wiener Process (model 1), assuming a compounded Poisson Jump Process mixed with a Wiener Process (model 2)

Usage: dat=stockest(data)
Input:
data n x 1 vector , data of a random process
Output:
mue scalar , increasing rate of return in model 1
sigma scalar , volatility of the returns in model 1
lambda scalar , number of jumps in model 2
mue2 scalar , increasing rate of return in the diffusion part of model 2
sigma2 scalar , volatility of the returns in the diffusion part of model 2
jump scalar , volatility for the height of jumps in model 2

Note:

Example:

library("finance")

data=read("motorola")

data=data[,2]

dat=stockest(data)

dat

Result:



Contents of dat.mue

[1,]   7.0066 

Contents of dat.sigma

[1,]   44.191 

Contents of dat.lambda

[1,]        4 

Contents of dat.mue2

[1,]   3.2302 

Contents of dat.sigma2

[1,]   38.819 

Contents of dat.jump

[1,]     10.9 


Library: finance
See also: stocksim stockestsim

Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Author: Härdle, Kleinow 990511
(C) MD*TECH Method and Data Technologies, 21.9.2000