def test_unknown_kernel(data):
mod = LinearFactorModel(data.portfolios, data.factors)
with pytest.raises(ValueError):
mod.fit(cov_type="unknown")
mod = LinearFactorModelGMM(data.portfolios, data.factors)
with pytest.raises(ValueError):
mod.fit(cov_type="unknown")
def test_repr(data):
mod = LinearFactorModelGMM(data.portfolios, data.factors)
assert "LinearFactorModelGMM" in mod.__repr__()
assert str(data.portfolios.shape[1]) + " test portfolios" in mod.__repr__()
assert str(data.factors.shape[1]) + " factors" in mod.__repr__()
mod = LinearFactorModel(data.portfolios, data.factors, risk_free=True)
assert "LinearFactorModel" in mod.__repr__()
assert "Estimated risk-free" in mod.__repr__()
assert "True" in mod.__repr__()
mod = TradedFactorModel(data.portfolios, data.factors)
assert "TradedFactorModel" in mod.__repr__()
assert str(hex(id(mod))) in mod.__repr__()
def test_repr(data):
mod = LinearFactorModelGMM(data.portfolios, data.factors)
assert 'LinearFactorModelGMM' in mod.__repr__()
assert str(data.portfolios.shape[1]) + ' test portfolios' in mod.__repr__()
assert str(data.factors.shape[1]) + ' factors' in mod.__repr__()
mod = LinearFactorModel(data.portfolios, data.factors, risk_free=True)
assert 'LinearFactorModel' in mod.__repr__()
assert 'Estimated risk-free' in mod.__repr__()
assert 'True' in mod.__repr__()
mod = TradedFactorModel(data.portfolios, data.factors)
assert 'TradedFactorModel' in mod.__repr__()
assert str(hex(id(mod))) in mod.__repr__()
def test_errors(data):
p = data.portfolios.copy()
f = data.factors.copy()
if isinstance(p, pd.DataFrame):
p2 = p.copy()
p3 = p.copy().iloc[:-1]
p4 = p.copy()
p5 = p.copy().iloc[:f.shape[1] - 1, :1]
p4 = p4.iloc[:, :(f.shape[1] - 1)]
p2["dupe"] = p.iloc[:, 0]
p["const"] = 1.0
f5 = f.copy()
f5 = f5.iloc[:p5.shape[0]]
f2 = f.copy()
f2["dupe"] = f.iloc[:, 0]
f["const"] = 1.0
else:
p2 = np.c_[p, p[:, [0]]]
p3 = p.copy()[:-1]
p4 = p.copy()
p5 = p.copy()[:f.shape[1] - 1, :1]
p4 = p4[:, :(f.shape[1] - 1)]
p = np.c_[np.ones((p.shape[0], 1)), p]
f5 = f.copy()
f5 = f5[:p5.shape[0]]
f2 = np.c_[f, f[:, [0]]]
f = np.c_[np.ones((f.shape[0], 1)), f]
with pytest.raises(ValueError):
TradedFactorModel(p, data.factors)
with pytest.raises(ValueError):
TradedFactorModel(p2, data.factors)
with pytest.raises(ValueError):
TradedFactorModel(p3, data.factors)
with pytest.raises(ValueError):
TradedFactorModel(data.portfolios, f)
with pytest.raises(ValueError):
TradedFactorModel(data.portfolios, f2)
with pytest.raises(ValueError):
TradedFactorModel(p5, f5)
with pytest.raises(ValueError):
LinearFactorModel(p4, data.factors)
def test_linear_model_parameters_risk_free_gls(data):
mod = LinearFactorModel(data.portfolios, data.factors, risk_free=True)
p = mod.portfolios.ndarray
sigma = np.cov(p.T)
val, vec = np.linalg.eigh(sigma)
sigma_m12 = vec @ np.diag(1.0 / np.sqrt(val)) @ vec.T
sigma_inv = np.linalg.inv(sigma)
mod = LinearFactorModel(data.portfolios,
data.factors,
risk_free=True,
sigma=sigma)
assert 'using GLS' in str(mod)
res = mod.fit()
f = mod.factors.ndarray
p = mod.portfolios.ndarray
n = f.shape[0]
moments = np.zeros(
(n, p.shape[1] * (f.shape[1] + 1) + f.shape[1] + 1 + p.shape[1]))
fc = np.c_[np.ones((n, 1)), f]
betas = lstsq(fc, p)[0]
eps = p - fc @ betas
loc = 0
for i in range(eps.shape[1]):
for j in range(fc.shape[1]):
moments[:, loc] = eps[:, i] * fc[:, j]
loc += 1
bc = np.c_[np.ones((p.shape[1], 1)), betas[1:, :].T]
lam = lstsq(sigma_m12 @ bc, sigma_m12 @ p.mean(0)[:, None])[0]
pricing_errors = p - (bc @ lam).T
for i in range(lam.shape[0]):
lam_error = pricing_errors @ sigma_inv @ bc[:, [i]]
moments[:, loc] = lam_error.squeeze()
loc += 1
alphas = p.mean(0)[:, None] - bc @ lam
moments[:, loc:] = pricing_errors - alphas.T
mod_moments = mod._moments(eps, bc, lam, alphas, pricing_errors)
assert_allclose(res.betas, bc[:, 1:])
assert_allclose(res.risk_premia, lam.squeeze())
assert_allclose(res.alphas, alphas.squeeze())
assert_allclose(moments, mod_moments)
m = moments.shape[1]
jac = np.eye(m)
block1 = p.shape[1] * (f.shape[1] + 1)
# 1,1
jac[:block1, :block1] = np.kron(np.eye(p.shape[1]), fc.T @ fc / n)
# 2, 1
loc = 0
nport, nf = p.shape[1], f.shape[1]
block2 = block1 + nf + 1
bct = sigma_inv @ bc
at = sigma_inv @ alphas
for i in range(nport):
block = np.zeros((nf + 1, nf + 1))
for j in range(nf + 1): # rows
for k in range(1, nf + 1): # cols
block[j, k] = bct[i][j] * lam[k]
if j == k:
block[j, k] -= at[i]
jac[block1:block2, loc:loc + nf + 1] = block
loc += nf + 1
# 2, 2
jac[block1:block2, block1:block2] = bc.T @ sigma_inv @ bc
# 3,1
block = np.zeros((nport, nport * (nf + 1)))
row = col = 0
for i in range(nport):
for j in range(nf + 1):
if j != 0:
block[row, col] = lam[j]
col += 1
row += 1
jac[-nport:, :(nport * (nf + 1))] = block
# 3, 2
jac[-nport:, (nport * (nf + 1)):(nport * (nf + 1)) + nf + 1] = bc
# 3, 3: already done since eye
mod_jac = mod._jacobian(bc, lam, alphas)
assert_allclose(mod_jac[:block1], jac[:block1])
assert_allclose(mod_jac[block1:block2, :block1],
jac[block1:block2, :block1])
assert_allclose(mod_jac[block1:block2, block1:block2], jac[block1:block2,
block1:block2])
assert_allclose(mod_jac[block1:block2, block2:], jac[block1:block2,
block2:])
assert_allclose(mod_jac[block2:], jac[block2:])
s = moments.T @ moments / (n - (nf + 1))
ginv = np.linalg.inv(jac)
cov = ginv @ s @ ginv.T / n
order = np.zeros((nport, nf + 1), dtype=np.int64)
order[:, 0] = np.arange(block2, block2 + nport)
for i in range(nf):
order[:, i + 1] = (nf + 1) * np.arange(nport) + (i + 1)
order = np.r_[order.ravel(), block1:block2]
cov = cov[order][:, order]
cov = (cov + cov.T) / 2
assert_allclose(cov, res.cov)
acov = cov[:block1:(nf + 1), :block1:(nf + 1)]
jstat = float(alphas.T @ np.linalg.pinv(acov) @ alphas)
assert_allclose(res.cov.values[:block1:(nf + 1), :block1:(nf + 1)], acov)
assert_allclose(res.j_statistic.stat, jstat, rtol=1e-1)
assert_allclose(res.j_statistic.pval,
1 - stats.chi2(nport - nf - 1).cdf(jstat),
rtol=1e-2)
get_all(res)
def test_linear_model_parameters(data):
mod = LinearFactorModel(data.portfolios, data.factors)
res = mod.fit()
f = mod.factors.ndarray
p = mod.portfolios.ndarray
n = f.shape[0]
moments = np.zeros(
(n, p.shape[1] * (f.shape[1] + 1) + f.shape[1] + p.shape[1]))
fc = np.c_[np.ones((n, 1)), f]
betas = lstsq(fc, p)[0]
eps = p - fc @ betas
loc = 0
for i in range(eps.shape[1]):
for j in range(fc.shape[1]):
moments[:, loc] = eps[:, i] * fc[:, j]
loc += 1
b = betas[1:, :].T
lam = lstsq(b, p.mean(0)[:, None])[0]
pricing_errors = p - (b @ lam).T
for i in range(lam.shape[0]):
lam_error = (p - (b @ lam).T) @ b[:, [i]]
moments[:, loc] = lam_error.squeeze()
loc += 1
alphas = pricing_errors.mean(0)[:, None]
moments[:, loc:] = pricing_errors - alphas.T
mod_moments = mod._moments(eps, b, lam, alphas, pricing_errors)
assert_allclose(res.betas, b)
assert_allclose(res.risk_premia, lam.squeeze())
assert_allclose(res.alphas, alphas.squeeze())
assert_allclose(moments, mod_moments)
m = moments.shape[1]
jac = np.eye(m)
block1 = p.shape[1] * (f.shape[1] + 1)
# 1,1
jac[:block1, :block1] = np.kron(np.eye(p.shape[1]), fc.T @ fc / n)
# 2, 1
loc = 0
nport, nf = p.shape[1], f.shape[1]
block2 = block1 + nf
for i in range(nport):
block = np.zeros((nf, nf + 1))
for j in range(nf): # rows
for k in range(1, nf + 1): # cols
block[j, k] = b[i][j] * lam[k - 1]
if j + 1 == k:
block[j, k] -= alphas[i]
jac[block1:block2, loc:loc + nf + 1] = block
loc += nf + 1
# 2, 2
jac[block1:block2, block1:block2] = b.T @ b
# 3,1
block = np.zeros((nport, nport * (nf + 1)))
row = col = 0
for i in range(nport):
for j in range(nf + 1):
if j != 0:
block[row, col] = lam[j - 1]
col += 1
row += 1
jac[-nport:, :(nport * (nf + 1))] = block
# 3, 2
jac[-nport:, (nport * (nf + 1)):(nport * (nf + 1)) + nf] = b
# 3, 3: already done since eye
mod_jac = mod._jacobian(b, lam, alphas)
assert_allclose(mod_jac[:block1], jac[:block1])
assert_allclose(mod_jac[block1:block2, :block1],
jac[block1:block2, :block1])
assert_allclose(mod_jac[block1:block2, block1:block2], jac[block1:block2,
block1:block2])
assert_allclose(mod_jac[block1:block2, block2:], jac[block1:block2,
block2:])
assert_allclose(mod_jac[block2:], jac[block2:])
s = moments.T @ moments / (n - (nf + 1))
ginv = np.linalg.inv(jac)
cov = ginv @ s @ ginv.T / n
order = np.zeros((nport, nf + 1), dtype=np.int64)
order[:, 0] = np.arange(block2, block2 + nport)
for i in range(nf):
order[:, i + 1] = (nf + 1) * np.arange(nport) + (i + 1)
order = np.r_[order.ravel(), block1:block2]
cov = cov[order][:, order]
cov = (cov + cov.T) / 2
assert_allclose(cov, res.cov)
acov = cov[:block1:(nf + 1), :block1:(nf + 1)]
jstat = float(alphas.T @ np.linalg.pinv(acov) @ alphas)
assert_allclose(res.j_statistic.stat, jstat)
assert_allclose(res.j_statistic.pval,
1 - stats.chi2(nport - nf).cdf(jstat))
get_all(res)
res = LinearFactorModel(data.portfolios,
data.factors).fit(cov_type='kernel',
debiased=False)
std_mom = moments / moments.std(0)[None, :]
mom = std_mom.sum(1)
bw = kernel_optimal_bandwidth(mom)
w = kernel_weight_bartlett(bw, n - 1)
s = _cov_kernel(moments, w)
cov = ginv @ s @ ginv.T / n
cov = cov[order][:, order]
cov = (cov + cov.T) / 2
assert_allclose(cov, res.cov)
def test_infeasible(output):
data = generate_data(nfactor=10, nportfolio=20, nobs=10, output=output)
with pytest.raises(ValueError):
LinearFactorModel(data.portfolios, data.factors)
