Chapter 3. Multivariate Volatility Models (in Python/Julia)


Copyright 2011, 2016, 2018 Jon Danielsson. This code is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This code is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. The GNU General Public License is available at: https://www.gnu.org/licenses/.
The original 2011 R code will not fully work on a recent R because there have been some changes to libraries. The latest version of the Matlab code only uses functions from Matlab toolboxes.
The GARCH functionality in the econometric toolbox in Matlab is trying to be too clever, but can't deliver and could well be buggy. If you want to try that, here are the docs (estimate). Besides, it can only do univariate GARCH and so can't be used in Chapter 3. Kevin Sheppard's MFE toolbox is much better, while not as user friendly, it is much better written and is certainly more comprehensive. It can be downloaded here and the documentation here is quite detailed.


Listing 3.1/3.2: Download stock prices in Python
Last updated June 2018

import numpy as np
p = np.loadtxt('stocks.csv',delimiter=',',skiprows=1)
p = p[:,[0,1]]                                        # consider first two stocks
y = np.diff(np.log(p), n=1, axis=0)*100               # calculate returns
y[:,0] = y[:,0]-np.mean(y[:,0])                       # subtract mean
y[:,1] = y[:,1]-np.mean(y[:,1])
T = len(y[:,0])
		
Listing 3.1/3.2: Download stock prices in Julia
Last updated June 2018

using CSV;
p = CSV.read("stocks.csv",nullable=false);
y1 = diff(log.(p[:,1])).*100;              # consider first two stocks
y2 = diff(log.(p[:,2])).*100;              # convert prices to returns
y1 = y1-mean(y1);                          # subtract mean
y2 = y2-mean(y2);
y = hcat(y1,y2);                           # combine both series horizontally
T = size(y,1);                             # get the height of timeseries
		

Listing 3.3/3.4: EWMA in Python
Last updated June 2018

EWMA = np.full([T,3], np.nan)
lmbda = 0.94
S = np.cov(y, rowvar = False)
EWMA[0,] = S.flatten()[[0,3,1]]
for i in range(1,T):
    S = lmbda * S + (1-lmbda) * np.transpose(np.asmatrix(y[i-1]))* np.asmatrix(y[i-1])
    EWMA[i,] = [S[0,0], S[1,1], S[0,1]]
EWMArho = np.divide(EWMA[:,2], np.sqrt(np.multiply(EWMA[:,0],EWMA[:,1])))
print(EWMArho)
		
Listing 3.3/3.4: EWMA in Julia
Last updated June 2018

## create a matrix to hold covariance matrix for each t
EWMA = fill!(Array{Float64}(T,3), NaN)
lambda = 0.94
S = cov(y)                                       # initial (t=1) covar matrix
EWMA[1,:] = [S[1], S[4], S[2]]                   # extract var and covar
for i in range(2,T-1)                            # loop though the sample
    S = lambda*S+(1-lambda)*y[i-1,:]*(y[i-1,:])'
    EWMA[i,:] = [S[1], S[4], S[2]]               # convert matrix to vector
end
EWMArho = EWMA[:,3]./sqrt.(EWMA[:,1].*EWMA[:,2]) # calculate correlations
		

Listing 3.5/3.6: OGARCH in Python
Last updated June 2018

## Python does not have a proper OGARCH package at present
		
Listing 3.5/3.6: OGARCH in Julia
Last updated June 2018

## No OGARCH code available in Julia at present
		

Listing 3.7/3.8: DCC in Python
Last updated June 2018

## Python does not have a proper DCC package at present
		
Listing 3.7/3.8: DCC in Julia
Last updated June 2018

## No DCC code available in Julia at present
		

Listing 3.9/3.10: Correlation comparison in Python
Last updated June 2018

## Python does not have a proper OGARCH/DCC package at present
		
Listing 3.9/3.10: Correlation comparison in Julia
Last updated June 2018

## No OGARCH/DCC code available in Julia at present