https://github.com/JuliaStats/TimeModels.jl

Modeling time series in Julia
https://github.com/JuliaStats/TimeModels.jl

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Modeling time series in Julia

• Host: GitHub
• URL: https://github.com/JuliaStats/TimeModels.jl
• Owner: JuliaStats
• License: other
• Created: 2013-03-07T12:52:22.000Z (over 11 years ago)
• Default Branch: master
• Last Pushed: 2020-02-08T17:19:32.000Z (over 4 years ago)
• Last Synced: 2024-06-01T16:14:16.712Z (4 months ago)
• Language: Julia
• Size: 566 KB
• Stars: 57
• Watchers: 22
• Forks: 28
• Open Issues: 18
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
TimeModels.jl

============

[![Build Status](https://travis-ci.org/JuliaStats/TimeModels.jl.svg?branch=master)](https://travis-ci.org/JuliaStats/TimeModels.jl)

[![Coverage Status](https://coveralls.io/repos/JuliaStats/TimeModels.jl/badge.svg?branch=master)](https://coveralls.io/r/JuliaStats/TimeModels.jl?branch=master)

[![TimeModels](http://pkg.julialang.org/badges/TimeModels_0.6.svg)](http://pkg.julialang.org/?pkg=TimeModels&ver=0.6)

## Please note: Currently unmaintained and not guaranteed to work with Julia 0.6!

## A Julia package for modeling time series.

![Kalman Demo](png/kalman.png)

![Experimental acf plot](png/acf_plot.png)

## GARCH model

***

Generalized Autoregressive Conditional Heteroskedastic ([GARCH](http://en.wikipedia.org/wiki/Autoregressive_conditional_heteroskedasticity)) models for Julia.

## What is implemented

* garchFit - estimates parameters of univariate normal GARCH process.

* predict - make prediction using fitted object returned by garchFit

* garchPkgTest - runs package test (compares model parameters with those obtained using R fGarch)

* Jarque-Bera residuals test

* Error analysis

Analysis of model residuals - currently only Jarque-Bera Test implemented.

## What is not ready yet

* More complex GARCH models

* Comprehensive set of residuals tests

* n-step forecasts

## Usage

### garchFit

estimates parameters of univariate normal GARCH process.

#### arguments:

data - data vector

#### returns:

Structure containing details of the GARCH fit with the following fields:

* data - orginal data

* params - vector of model parameters (omega,alpha,beta)

* llh - likelihood

* status - status of the solver

* converged - boolean convergence status, true if constraints are satisfied

* sigma - conditional volatility

* hessian - Hessian matrix

* secoef - standard errors

* tval - t-statistics

### predict

make volatility prediction

#### arguments:

fit - fitted object returned by garchFit

#### returns:

one-step-ahead volatility forecast

## Example

    using GARCH

    using Quandl

    quotes = quandl("YAHOO/INDEX_GSPC")

    ret = diff(log(quotes["Close"]))

    ret = ret - mean(ret)

    garchFit(convert(Vector,ret[end-199:end]))

## References

* T. Bollerslev (1986): Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics 31, 307–327.

* R. F. Engle (1982): Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica 50, 987–1008.