Chapter 5. Implementing Risk Forecasts

stocks = csvread('stocks.csv',1,0);
p1 = stocks(:,1);             % consider first two stocks
p2 = stocks(:,2); 
y1=diff(log(p1));             % convert prices to returns
y2=diff(log(p2));
y=[y1 y2];
T=length(y1);
value = 1000;                 % portfolio value
p = 0.01;                     % probability

import numpy as np
from scipy import stats
p = np.loadtxt('stocks.csv',delimiter=',',skiprows=1)
p = p[:,[0,1]]      # consider two stocks
y1 = np.diff(np.log(p[:,0]), n=1, axis=0)
y2 = np.diff(np.log(p[:,1]), n=1, axis=0)
y = np.stack([y1,y2], axis = 1)
T = len(y1)
value = 1000 # portfolio value
p = 0.01 # probability

ys = sort(y1);   % sort returns
op = ceil(T*p);  % p percent smallest, rounded up to meet VaR probability requirement
VaR1 = -ys(op)*value

from math import ceil
ys = np.sort(y1) # sort returns
op = ceil(T*p)    # p percent smallest
VaR1 = -ys[op - 1] * value
print(VaR1)

w = [0.3; 0.7];    % vector of portfolio weights
yp = y*w;          % portfolio returns
yps = sort(yp);
VaR2 = -yps(op)*value

w = [0.3, 0.7]               # vector of portfolio weights
yp = np.squeeze(np.matmul(y, w)) # portfolio returns
yps = np.sort(yp)
VaR2= -yps[op - 1] * value
print(VaR2)

ES1 = -mean(ys(1:op))*value

ES1 = -np.mean(ys[:op]) * value
print(ES1)

sigma = std(y1); % estimate volatility
VaR3 = -sigma * norminv(p) * value

sigma = np.std(y1, ddof=1) # estimate volatility
VaR3 = -sigma * stats.norm.ppf(p) * value 
print(VaR3)

sigma = sqrt(w' * cov(y) * w); % portfolio volatility
VaR4 = - sigma * norminv(p) *  value

sigma = np.sqrt(np.mat(w)*np.mat(np.cov(y,rowvar=False))*np.transpose(np.mat(w)))[0,0]
VaR4 = -sigma * stats.norm.ppf(p) * value 
print(VaR4)

scy1=y1*100;          % scale the returns
res=mle(scy1,'distribution','tlocationscale');
sigma1 = res(2)/100;  % rescale the volatility
nu = res(3);
VaR5 = - sigma1 * tinv(p,nu) * value

scy1 = y1 * 100         # scale the returns
res = stats.t.fit(scy1)
sigma = res[2]/100      # rescale volatility
nu = res[0]
VaR5 = -sigma*stats.t.ppf(p,nu)*value
print(VaR5)

sigma = std(y1);
ES2=sigma*normpdf(norminv(p))/p * value

sigma = np.std(y1, ddof=1)
ES2 = sigma * stats.norm.pdf(stats.norm.ppf(p)) / p * value
print(ES2)

VaR = -norminv(p);
ES = -sigma*quad(@(q) q.*normpdf(q),-6,-VaR)/p*value

from scipy.integrate import quad
VaR = -stats.norm.ppf(p)
integrand = lambda q: q * stats.norm.pdf(q)
ES = -sigma * quad(integrand, -np.inf, -VaR)[0] / p * value
print(ES)

WE=20;
for t=T-5:T
    t1=t-WE+1;
    window=y1(t1:t);  % estimation window
    sigma=std(window);
    VaR6 = -sigma * norminv(p) * value
end

WE = 20
for t in range(T-5,T+1):
    t1 = t-WE
    window = y1[t1:t]      # estimation window
    sigma = np.std(window, ddof=1)
    VaR6 = -sigma*stats.norm.ppf(p)*value
    print (VaR6)

lambda = 0.94;	
s11 = var(y1(1:30)); % initial variance
for t = 2:T	
    s11 = lambda * s11  + (1-lambda) * y1(t-1)^2;
end
VaR7 = -norminv(p) * sqrt(s11) * value 

lmbda = 0.94
s11 = np.var(y1[0:30], ddof = 1)     # initial variance
for t in range(1, T):
    s11 = lmbda*s11 + (1-lmbda)*y1[t-1]**2
VaR7 = -np.sqrt(s11)*stats.norm.ppf(p)*value
print(VaR7)

s = cov(y);               % initial covariance
for t = 2:T
    s = lambda * s +  (1-lambda) * y(t-1,:)' * y(t-1,:);
end
sigma = sqrt(w' * s * w); % portfolio vol
VaR8 = - sigma * norminv(p) * value 

s = np.cov(y, rowvar = False)
for t in range(1,T):
    s = lmbda*s+(1-lmbda)*np.transpose(np.asmatrix(y[t-1,:]))*np.asmatrix(y[t-1,:])
sigma = np.sqrt(np.mat(w)*s*np.transpose(np.mat(w)))[0,0]
VaR8 = -sigma * stats.norm.ppf(p) * value
print(VaR8)

[parameters,ll,ht]=tarch(y1,1,0,1);
omega = parameters(1)
alpha = parameters(2)
beta = parameters(3)
sigma2 = omega + alpha*y1(end)^2 + beta*ht(end) % calc sigma2 for t+1
VaR9 = -sqrt(sigma2) * norminv(p) * value 

from arch import arch_model 
am = arch_model(y1, mean = 'Zero', vol='Garch', p=1, o=0, q=1, dist='Normal', rescale = False)
res = am.fit(update_freq=5, disp = "off")
omega = res.params.loc['omega']
alpha = res.params.loc['alpha[1]']
beta = res.params.loc['beta[1]']
sigma2 = omega + alpha*y1[T-1]**2 + beta * res.conditional_volatility[-1]**2
VaR9 = -np.sqrt(sigma2) * stats.norm.ppf(p) * value
print(VaR9)