This page contains some of the Matlab
code I've written during the course of my research.
If you find any mistakes or bugs in the code please let me know.

This code is being released under a
BSD license, which means that you can do pretty much
what ever you want with it, including make money by
selling it.

Most of my functions use code
contained in the econometrics
toolbox for Matlab by James P. LeSage. This toolbox contains many useful programs for
econometricians. It must be installed before my code will work.

This toolbox contains many useful
functions relating to estimating and simulating both univariate
and multivariate GARCH models. Some of my programs call some of Kevin's
functions, so this also needs to be installed. The GARCH toolbox can be found
here.

This **zip file** contains 5
functions: the pdf, cdf, log-likelihood, inverse cdf and a function to generate random draws from the skewed
*t* distribution.

Hansen, Bruce E., 1994, Autoregressive
Conditional Density Estimation, *International Economic
Review*, 35(3), 705-730.

This Matlab m-file (saved as a “txt” file)
returns an estimate of the optimal block size for bootstrap methods (stationary
or circular) of estimating the asymptotic variance of the mean of dependent
data, given a sample from a univariate time series.

R code for this procedure, written by Jeff Racine and Chris Parmeter is available here, and a PDF help file is here. Alternatively, if you install R and the “np” package you will have access to the function “b.star”.

__Bug
fixes and updates:__

*26aug03: updated with suggestions from Kevin Sheppard.*

*16dec07: major
revision: estimate of optimal block length for the stationary bootstrap
corrected using recent paper by Nordman (Annals of
Statistics, forthcoming). Details in a
“correction” paper available here.*

*9dec08: fixed small bug in choice of
“KN”, thanks to Jeff
Racine for spotting this.*

*28oct09: Here is “mlag.m” (saved as a
“txt” file) which is called in my code. (This function is part
of the **econometrics toolbox for Matlab by James P. LeSage*.)

Politis,
Dimitris N., and White, Halbert, 2004, Automatic
Block-Length Selection for the Dependent Bootstrap, *Econometric
Reviews*, 23(1), 53-70.

Politis, Dimitris N., White, Halbert,
and Patton,
Andrew J., 2009, Correction:
Automatic Block-Length Selection for the Dependent Bootstrap,* Econometric
Reviews*, 28(4), 372-375.

This **zip file** contains code to compute the exceedence correlations used by Longin
and Solnik (2001) and Ang and
Chen (2002). Code for the test that the exceedence
correlation plot is symmetric recently proposed by Hong, Tu
and Zhou (2003) is also included. *
*Longin, Francois, and Solnik, Bruno, 2001, Extreme Correlation of
International Equity Markets,

Ang, Andrew, and Chen, Joseph, 2002, Asymmetric Correlations of Equity Portfolios,

Hong, Yongmiao, Tu, Jun, and Zhou, Guofu, 2007, Asymmetries in Stock Returns: Statistical Tests and Economic Evaluation,

This zip file contains Matlab code to replicate the empirical section in Patton (2011), on volatility
forecast comparison using imperfect volatility proxies. The main program is
called "robust_example_code.m", which
generates the figures and does the calculations. The data used in the paper is
included. The function "robust_loss_1.m" is a function for the
parametric family of "robust" loss functions proposed in the paper.
Some helper functions are also included.

**This zip file**
contains a collection of Matlab functions that I
wrote for my research on copulas for financial time
series (Patton 2006a, Patton 2006b, Patton 2004, Granger et al. 2006, Patton 2007). Some simple example
code is given in "copula_example_code.m". A
table of contents is given in "contents.xls". Briefly, the toolbox
contains CDFs, PDFs, log-likelihoods and random number generators for many common
bivariate copulas, including the Clayton, Gumbel,
Normal, Student's t, Frank, Plackett and symmetrised Joe-Clayton (SJC) copulas. Basic code for
time-varying Normal, Gumbel and SJC copulas is
included. Some helper functions are also included. If you find any bugs in this
code please let me know: andrew.patton@economics.ox.ac.uk.

__Bug fixes:
__22aug06: Fixed two small bugs in clayton_pdf.m
and tCopula_cdf.m

5feb07: Fixed small
bug in copula_example_code.m

**This zip file**
contains Matlab code to replicate the empirical
results presented in Patton
and Timmermann (2010), on tests of monotonicity in asset returns. The main
program is called "sorts_example_code_100.m", which loads the data,
computes the t-test and MR test statistics and p-values, and returns the tables
and the figure. The data used in the paper is included, as are some helper
functions. Also included is a file made for Matlab
beginners, “MR_test_for_dummies_100.m”, which goes through each
step with explicit instructions. *If you
have any problems using the functions in this toolbox please feel free to email
me: *andrew.patton@duke.edu .

__Updates:
__23may08: Updated toolbox to compute the Wolak
test (Journal of Econometrics 1989) for comparison, and to allow for two-way
sorted returns.

15jun08: Updated toolbox to include a “MR
test for dummies” function, which goes through the implementation of the
MR test step-by-step – designed for Matlab
beginners.

2may09:
Large update, to go with the revision of the paper. Main MR test
function was updated, example code to replicate all of the empirical results
was extended to reflect the broader selection of empirical applications
considered in the paper. Code for the “up” and “down”
bootstrap tests was added.

**This zip file (version 14may13)**
contains Matlab code to replicate almost all of the
empirical results presented in Patton (2013). The
main program is called "AAA_empirical_work_CLEAN_14may13.m". This
code is broken into blocks to replicated the tables
and figures presented in various sections of the chapter. Also included are
over 100 helper functions, and the data set used in the chapter (as an Excel
file).

__Notes:__

1) You will need to install the above "UCSD
GARCH" and "LeSage Spatial
Econometrics" toolboxes for this code to work.

2) You need to have Matlab's
statistics and optimization toolboxes

3) Updated May 2013 to conform to Matlab R2013a (which is case sensitive for function and
file names). Please email me if I've missed a correction.

**This zip file (16feb14)
** contains Matlab
code to implement the multi-horizon bounds tests of forecast rationality
proposed in Patton and Timmermann (2012, JBES). The code is illustrated in
"bounds_tests_example_code_1.m", and the main function to conduct the
tests is "bounds_tests_function_40nan_simple.m". The function "ineqtest.m" was provided by Raymond
Kan, and is much faster than our initial
implementation of the Wolak test.

__Notes:__

1) You will need to install the above "LeSage Spatial Econometrics" toolboxes for this code
to work.

2) You need to have Matlab's
statistics and optimization toolboxes

3) Updated Feb 2014 to conform to Matlab R2013a (which is case sensitive for function and
file names). Please email me if I've missed a correction.

**This zip file (17feb16) ** contains Matlab
code to illustrate the estimation of a skew t-t factor copula with GAS dynamics
on a sample of 10 variables. The main file is “AAA_main_GASFacCop_Skewtt_Ngroup.m”.

__Notes:__

1) You need to have Matlab's
statistics and optimization toolboxes.

2) You probably need to be familiar with the
dynamic copula tools in my Handbook of Economic
Forecasting chapter (see item 9 above).

*
*__________________________________________

Andrew Patton

Department of Economics

213 Social Sciences Building

Durham NC 27708-0097

USA

Email:
andrew.patton@duke.edu

Phone: +1 919 660 1849

Fax: +1 919 684
8974

Web: . http://econ.duke.edu/~ap172

__________________________________________

*Last Updated:* 16 February 2014.

Go to Andrew's main page.