def simulateCorrelatedTransitionData(K, N, T, Pin, wStart, myRho):
Q = cumulateTransitionMatrix(K, Pin)
Delta = transformCumulativeTransitionMatrix(K, Q)
Y = np.zeros([N, T]) # latent variables
X = np.zeros([N, T]) # credit states
allP = np.zeros([N, T]) # default probabilities
Xlast = mc.initializeCounterparties(N, wStart) # initial states
X0 = Xlast
Plast = Pin[(Xlast - 1).astype(int), -1]
for t in range(0, T):
Y[:, t] = th.getY(N, 1, Plast, myRho)
for n in range(0, N):
if Xlast[n] == 4:
X[n, t] = 4
continue
else:
X[n, t] = migrateRating(Xlast[n], Delta, Y[n, t])
allP[:, t] = Pin[(Xlast - 1).astype(int), -1]
Plast = allP[:, t]
Xlast = X[:, t]
return X, Y, Delta, allP, X0
def createRatingData2r(K, N, T, P, wStart, rStart, myRho, nu, isT):
Q = cumulateTransitionMatrix(K, P)
Delta = transformCumulativeTransitionMatrix(K, Q)
rId = initializeRegion2r(N, rStart).astype(int)
Y = np.zeros([N, T]) # latent variables
X = np.zeros([N, T]) # credit states
allP = np.zeros([N, T]) # default probabilities
Xlast = mc.initializeCounterparties(N, wStart) # initial states
X0 = Xlast
Plast = P[(Xlast - 1).astype(int), -1]
for t in range(0, T):
Y[:, t] = th.getY2r(N, 1, Plast, myRho, rId, nu, P, isT)
for n in range(0, N):
if Xlast[n] == 4:
X[n, t] = 4
continue
else:
X[n, t] = migrateRating(Xlast[n], Delta, Y[n, t])
allP[:, t] = P[(Xlast - 1).astype(int), -1]
Plast = allP[:, t]
Xlast = X[:, t]
return X, Y, Delta, allP, X0, rId