def test_linear_model_gmm_kernel_smoke(data):
mod = LinearFactorModelGMM(data.portfolios, data.factors)
res = mod.fit(cov_type="kernel", disp=10)
get_all(res)
str(res._cov_est)
res._cov_est.__repr__()
str(res._cov_est.config)
def test_linear_model_gmm_smoke_risk_free(data):
mod = LinearFactorModelGMM(data.portfolios, data.factors, risk_free=True)
res = mod.fit(cov_type="robust", disp=10)
get_all(res)
str(res._cov_est)
res._cov_est.__repr__()
str(res._cov_est.config)
def test_linear_model_gmm_moments_jacobian(data):
mod = LinearFactorModelGMM(data.portfolios, data.factors)
res = mod.fit(cov_type="robust", disp=0, debiased=False)
params = np.r_[res.betas.values.ravel(),
res.risk_premia.values.ravel(),
mod.factors.ndarray.mean(0), ]
mod_mom = mod._moments(params[:, None], True)
mom = []
p = mod.portfolios.ndarray
f = mod.factors.ndarray
n = f.shape[0]
fc = np.c_[np.ones((n, 1)), f]
mu = f.mean(0)[None, :]
lam = res.risk_premia.values[None, :]
x = f - mu + lam
b = res.betas.values
for i in range(p.shape[1]):
eps = p[:, i:(i + 1)] - x @ b[[i]].T
for j in range(fc.shape[1]):
mom.append(eps * fc[:, [j]])
mom.append(f - mu)
mom_arr = np.hstack(tuple(mom))
mod_jac = mod._jacobian(params, True)
jac = np.zeros((mom_arr.shape[1], params.shape[0]))
nport, nf = p.shape[1], f.shape[1]
# 1,1
jac[:(nport * (nf + 1)), :nport * nf] = np.kron(np.eye(nport),
fc.T @ x / n)
# 1, 2
col = []
for i in range(nport):
col.append(fc.T @ np.ones((n, 1)) @ b[[i]] / n)
col = np.vstack(tuple(col))
jac[:(nport * (nf + 1)), nport * nf:nport * nf + nf] = col
# 1, 3
col = []
for i in range(nport):
col.append(-fc.T @ np.ones((n, 1)) @ b[[i]] / n)
col = np.vstack(tuple(col))
jac[:(nport * (nf + 1)), -nf:] = col
# 2,2
jac[-nf:, -nf:] = np.eye(nf)
assert_allclose(mom_arr, mod_mom)
assert_allclose(jac, mod_jac)
me = mom_arr - mom_arr.mean(0)[None, :]
s = me.T @ me / n
s = (s + s.T) / 2
cov = np.linalg.inv(jac.T @ np.linalg.inv(s) @ jac) / n
cov = (cov + cov.T) / 2
assert_allclose(np.diag(cov), np.diag(res.cov), rtol=5e-3)
get_all(res)
def test_starting_values_options(data):
mod = LinearFactorModelGMM(data.portfolios, data.factors)
res = mod.fit(steps=1, disp=0)
res_full = mod.fit(disp=0)
mod2 = LinearFactorModelGMM(data.portfolios, data.factors)
oo = {"method": "L-BFGS-B"}
sv = np.r_[np.asarray(res.betas).ravel(), np.asarray(res.risk_premia)]
sv = np.r_[sv, data.factors.mean()]
res2 = mod2.fit(starting=sv, opt_options=oo, disp=0)
assert res_full.iterations == res2.iterations
res3 = mod2.fit(starting=sv[:, None], opt_options=oo, disp=0)
assert_frame_equal(res2.params, res3.params)
with pytest.raises(ValueError, match="tarting values"):
mod2.fit(starting=sv[:-3], opt_options=oo, disp=0)
def test_linear_model_gmm_kernel_bandwidth_smoke(data):
mod = LinearFactorModelGMM(data.portfolios, data.factors)
res = mod.fit(cov_type="kernel", bandwidth=10, disp=10)
get_all(res)
def test_linear_model_gmm_smoke_iterate(data):
mod = LinearFactorModelGMM(data.portfolios, data.factors)
res = mod.fit(cov_type="robust", disp=5, steps=20)
get_all(res)
def test_linear_model_gmm_cue_smoke(data):
mod = LinearFactorModelGMM(data.portfolios, data.factors, risk_free=True)
res = mod.fit(cov_type="robust", disp=10, use_cue=True)
get_all(res)
def test_linear_model_gmm_smoke_risk_free(data):
mod = LinearFactorModelGMM(data.portfolios, data.factors, risk_free=True)
res = mod.fit(cov_type='robust', disp=10)
get_all(res)
def test_linear_model_gmm_smoke(data):
mod = LinearFactorModelGMM(data.portfolios, data.factors)
res = mod.fit(cov_type='robust', disp=5)
get_all(res)