4.83333

4.8 | 7 ratings Rate this file 79 Downloads (last 30 days) File Size: 534 KB File ID: #32882
image thumbnail

ARMAX-GARCH-K Toolbox (Estimation, Forecasting, Simulation and Value-at-Risk Applications)

by

 

13 Sep 2011 (Updated )

ARMAX-GARCH-K Toolbox

| Watch this File

File Information
Description

ARMAX-GARCH-K Toolbox (Estimation, Forecasting, Simulation and Value-at-Risk Applications)

Firstly, it allows the estimation, forecasting and simulation of the family of ARMAX-GARCH of any order of AR, MA, ARCH and GARCH terms of the GARCH, GJR-GARCH, EGARCH, NARCH (Nonlinear ARCH), NGARCH (Nonlinear GARCH), AGARCH (Asymmetric GARCH), APGARCH (Asymmetric Power GARCH), and NAGARCH (Nonlinear Asymmetric GARCH) with the Gaussian, Student-t, Generalized Error, Modified Cauchy, Hansen's Skew-t, Logistic, Laplace, Rayleigh, Centered Cauchy, Extreme Value Distribution Type 1, Generalized Exponential and Gram and Charlier expansion series with constant higher moments.

Secondly, the toolbox allows the estimation, forecasting and simulation of the Autoregressive Conditional Kurtosis Model proposed by Brooks, et al (2005).

Thirdly, the toolbox allows the evaluation of volatility forecasts using a number of loss functions and the estimation of Value-at-Risk for a given confidence level and horizon period.

Finally, a number of examples are presented to illustrate the application of this toolbox in Market Risk and Financial Risk Management.

The main functions are:
1. garch.m & garchk.m which estimates the ARMAX-GARCH-K family of models.

2. garchfind.m, which finds the combination of models and distributions that better fits the data based on a set of criteria (i.e. largest log likelihood value and the smallest AIC and BIC criteria).

3. garchsim.m & garchksim.m, which simulates returns, conditional variances and kurtosis.

4.garchfor.m & garchfor2.m - garchkfor.m & garchkfor2.m, which estimates mean, volatility and kurtosis forecasts given the model, distribution, and number of forecasts.

5. garchvar.m & garchvar2.m - garchkvar.m & garchkvar2.m, which estimates Value-at-Risk for a given confidence level and horizon period for both long and short positions.

6. garchvolfor.m, which is an application in Volatility Forecasting & Value-at-Risk. It allows the comparison of volatility and Value-at-Risk estimates for a data vector and for a variety of GARCH models and distributions and at different forecast periods as well as sort the results according to only a sub-set of forecast periods.

Notes:
1. With the help of the VFLF and VaRLR functions a number of volatility loss functions and the VaR unconditional, independence, conditional and regulatory tests are also estimated. The volatility loss functions are the following: MSE; MAD; MLAE; HMSE; HMAE; MAE; MAPE; R2LOG; QLIKE; SR. The VaR back-testing tests are: percentage of failures, TUFF; Likelihood Ratio Unconditional Coverage, Independence Coverage, and Conditional Coverage; Basel II Accord, Basel. For more information which tests are included please refer the VFLF and VaRLR functions.

2. For further information regarding the full functionality and a set of examples of the ARMAX-GARCH-K Toolbox please refer to the readme files.

3. Additional files for garchvar.m and garchvolfor.m can be found in:
http://www.mathworks.com/matlabcentral/fileexchange/29051-distributions
http://www.mathworks.com/matlabcentral/fileexchange/33414-volatility-loss-functions-and-var-conditional-indepedence-and-regulatory-backtests

I would like to thank you for your comments and your suggestions regarding additional features that should be included.

Please feel free to contact me with comments, suggestions, or bugfixes.

Required Products Optimization Toolbox
MATLAB release MATLAB 7.11 (R2010b)
Tags for This File   Please login to tag files.
Please login to add a comment or rating.
Comments and Ratings (11)
14 Apr 2014 Abdelrazzaq

I finally found what I want. Honestly, we cannot thank you enough. I just suggest to add:
1-the Range-GARCH (RGARCH) based models that use high, low, open, close and volume time series data.
2-Bench mark-based tests such as Diebold-Mariano.

15 May 2013 Da w

I am using matlab 2013a. It is not working in matlab 2013a. while using function 'garch', it gets 'The constructor for class 'garch' must return only one output value.' Please fix it, Thanks!!

15 May 2013 Da w  
07 Dec 2012 Angela

Hello,
I am trying to use the GJR-GARCH specification with exogenous variables in variance equations. I found in garch.m, line_316, the column sequence of constraints matrix A (first: leverage, then, exogeneous y) is not the same as that of the starting values (0.5 for leverage, then 0 for y) and ublb. I guess they should match? Sorry if I am wrong. Could you please advice. Thanks very much.

31 Aug 2012 paula roma

Hello,
I am trying to use garchfind to find the best model to my data but when I put arma (1,9) with garch(1,1) the program gives this output:

Error in ==> garchfind at 41
if nargin == 0

??? Output argument "LLF" (and maybe others) not assigned during call to

Error in ==> aEntrada_de_dados_garch at 179
[parameters, stderrors, LLF, ht, resids, summary] = garchfind(ret, models, distributions,
1, 9, 1, 1, options);

Could you help me?

Thank you a lot

16 Aug 2012 Amol

I am using Matlab R2010a. How can I install this toolbox? Please help.

16 Jun 2012 Iva Mihaylova  
23 Oct 2011 ted p teng

In garchsim.m, if distribution is set to 'GED', the 'gevrnd' requires inputting 'k', 'sigma' and 'mu'. Any suggestions on how the parameters could be determined... maybe with 'gevfit'?

23 Oct 2011 ted p teng

This is a BIG contribution to the FEX community. The examples provided is really helpful! Thank you!

30 Sep 2011 Alexandros Gabrielsen

Thanks for your comments Lurion. Currently, the toolbox estimates only univariate processes.

27 Sep 2011 Lurion De Mello

A lot of hard work here!
Does this do multivariate version of the supported models?
Thanks a lot

Updates
25 Oct 2011

introduced garchvar and garchvolfor

26 Oct 2011

more examples in the readme files are added

07 Dec 2011

additions:
1. distributions: Centered-Cauchy, Logistic, Laplace, Rayleigh, Extreme Value Distribution Type 1 & Generalized Exponential
2. Estimation, forecasting & simulation of the GARCH-K model.
3. Updates in the readme files

Contact us