Matlab and Python Chapter 3. Multivariate Volatility Models

Chapter 3. Multivariate Volatility Models

Matlab and Python

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Listing 3.1/3.2
% Download stock prices in MATLAB
p = csvread('stocks.csv',1,0);
p = p(:,[1,2]); % consider first two stocks
y = diff(log(p))*100; % convert prices to returns
y(:,1)=y(:,1)-mean(y(:,1)); % subtract mean
T = length(y);
Listing 3.1/3.2
Download stock prices in Python
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 3.3/3.4
EWMA = nan(T,3); 
lambda = 0.94;
S = cov(y);                 % initial (t=1) covar matrix
EWMA(1,:) = S([1,4,2]);     % extract var and covar
for i = 2:T                 % loop though the sample
    S = lambda*S+(1-lambda)* y(i-1,:)'*y(i-1,:);
    EWMA(i,:) = S([1,4,2]); % convert matrix to vector
EWMArho = EWMA(:,3)./sqrt(EWMA(:,1).*EWMA(:,2)); % calculate correlations
Listing 3.3/3.4
EWMA in Python
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])))

Listing 3.5/3.6
[par, Ht] = o_mvgarch(y,2, 1,1,1);
Ht = reshape(Ht,4,T)';
OOrho = Ht(:,3) ./ sqrt(Ht(:,1) .* Ht(:,4));
Listing 3.5/3.6
OGARCH in Python

Listing 3.7/3.8
[p, lik, Ht] = dcc(y,1,1,1,1);
Ht = reshape(Ht,4,T)';
DCCrho = Ht(:,3) ./ sqrt(Ht(:,1) .* Ht(:,4));
Listing 3.7/3.8
DCC in Python

Listing 3.9/3.10
% Correlation comparison in MATLAB
Listing 3.9/3.10
Correlation comparison in Python

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Market risk forecasting with R, Julia, Python and Matlab. Code, lecture slides, implementation notes, seminar assignments and questions.
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