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

Macro: 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, 17.8.2000