Ejemplo n.º 1
0
    def test_egarch_100(self):
        egarch = EGARCH(p=1, o=0, q=0)

        sv = egarch.starting_values(self.resids)
        assert_equal(sv.shape[0], egarch.num_params)

        backcast = egarch.backcast(self.resids)
        w = 0.94 ** np.arange(75)
        backcast_test = np.sum((self.resids[:75] ** 2) * (w / w.sum()))
        assert_almost_equal(backcast, np.log(backcast_test))

        var_bounds = egarch.variance_bounds(self.resids)
        parameters = np.array([.1, .4])
        egarch.compute_variance(parameters, self.resids, self.sigma2, backcast,
                                var_bounds)
        cond_var_direct = np.zeros_like(self.sigma2)
        lnsigma2 = np.empty(self.T)
        std_resids = np.empty(self.T)
        abs_std_resids = np.empty(self.T)
        rec.egarch_recursion(parameters, self.resids, cond_var_direct, 1, 0, 0,
                             self.T, backcast, var_bounds, lnsigma2,
                             std_resids, abs_std_resids)
        assert_allclose(self.sigma2, cond_var_direct)

        state = self.rng.get_state()
        rng = Normal()
        rng.random_state.set_state(state)
        sim_data = egarch.simulate(parameters, self.T, rng.simulate([]))
        self.rng.set_state(state)
        e = self.rng.standard_normal(self.T + 500)
        initial_value = 0.1 / (1 - 0.95)
        lnsigma2 = np.zeros(self.T + 500)
        lnsigma2[0] = initial_value
        sigma2 = np.zeros(self.T + 500)
        sigma2[0] = np.exp(lnsigma2[0])
        data = np.zeros(self.T + 500)
        data[0] = np.sqrt(sigma2[0]) * e[0]
        norm_const = np.sqrt(2 / np.pi)
        for t in range(1, self.T + 500):
            lnsigma2[t] = parameters[0]
            lnsigma2[t] += parameters[1] * (np.abs(e[t - 1]) - norm_const)

        sigma2 = np.exp(lnsigma2)
        data = e * np.sqrt(sigma2)

        data = data[500:]
        sigma2 = sigma2[500:]

        assert_almost_equal(data - sim_data[0] + 1.0, np.ones_like(data))
        assert_almost_equal(sigma2 / sim_data[1], np.ones_like(sigma2))
Ejemplo n.º 2
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    def test_egarch_100(self):
        egarch = EGARCH(p=1, o=0, q=0)

        sv = egarch.starting_values(self.resids)
        assert_equal(sv.shape[0], egarch.num_params)

        backcast = egarch.backcast(self.resids)
        w = 0.94 ** np.arange(75)
        backcast_test = np.sum((self.resids[:75] ** 2) * (w / w.sum()))
        assert_almost_equal(backcast, np.log(backcast_test))

        var_bounds = egarch.variance_bounds(self.resids)
        parameters = np.array([.1, .4])
        egarch.compute_variance(parameters, self.resids, self.sigma2, backcast,
                                var_bounds)
        cond_var_direct = np.zeros_like(self.sigma2)
        lnsigma2 = np.empty(self.T)
        std_resids = np.empty(self.T)
        abs_std_resids = np.empty(self.T)
        rec.egarch_recursion(parameters, self.resids, cond_var_direct, 1, 0, 0,
                             self.T, backcast, var_bounds, lnsigma2,
                             std_resids, abs_std_resids)
        assert_allclose(self.sigma2, cond_var_direct)

        state = np.random.get_state()
        rng = Normal()
        sim_data = egarch.simulate(parameters, self.T, rng.simulate([]))
        np.random.set_state(state)
        e = np.random.standard_normal(self.T + 500)
        initial_value = 0.1 / (1 - 0.95)
        lnsigma2 = np.zeros(self.T + 500)
        lnsigma2[0] = initial_value
        sigma2 = np.zeros(self.T + 500)
        sigma2[0] = np.exp(lnsigma2[0])
        data = np.zeros(self.T + 500)
        data[0] = np.sqrt(sigma2[0]) * e[0]
        norm_const = np.sqrt(2 / np.pi)
        for t in range(1, self.T + 500):
            lnsigma2[t] = parameters[0]
            lnsigma2[t] += parameters[1] * (np.abs(e[t - 1]) - norm_const)

        sigma2 = np.exp(lnsigma2)
        data = e * np.sqrt(sigma2)

        data = data[500:]
        sigma2 = sigma2[500:]

        assert_almost_equal(data - sim_data[0] + 1.0, np.ones_like(data))
        assert_almost_equal(sigma2 / sim_data[1], np.ones_like(sigma2))
Ejemplo n.º 3
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    def test_egarch(self):
        egarch = EGARCH(p=1, o=1, q=1)

        sv = egarch.starting_values(self.resids)
        assert_equal(sv.shape[0], egarch.num_params)

        bounds = egarch.bounds(self.resids)
        assert_equal(len(bounds), egarch.num_params)
        const = np.log(10000.0)
        lnv = np.log(np.mean(self.resids ** 2.0))
        assert_equal(bounds[0], (lnv - const, lnv + const))
        assert_equal(bounds[1], (-np.inf, np.inf))
        assert_equal(bounds[2], (-np.inf, np.inf))
        assert_equal(bounds[3], (0.0, 1.0))
        backcast = egarch.backcast(self.resids)

        w = 0.94 ** np.arange(75)
        backcast_test = np.sum((self.resids[:75] ** 2) * (w / w.sum()))
        assert_almost_equal(backcast, np.log(backcast_test))

        var_bounds = egarch.variance_bounds(self.resids)
        parameters = np.array([.1, .1, -.1, .95])
        egarch.compute_variance(parameters, self.resids, self.sigma2, backcast,
                                var_bounds)
        cond_var_direct = np.zeros_like(self.sigma2)
        lnsigma2 = np.empty(self.T)
        std_resids = np.empty(self.T)
        abs_std_resids = np.empty(self.T)
        rec.egarch_recursion(parameters, self.resids, cond_var_direct, 1, 1, 1,
                             self.T, backcast, var_bounds, lnsigma2,
                             std_resids, abs_std_resids)
        assert_allclose(self.sigma2, cond_var_direct)

        a, b = egarch.constraints()
        a_target = np.vstack((np.array([[0, 0, 0, -1.0]])))
        b_target = np.array([-1.0])
        assert_array_equal(a, a_target)
        assert_array_equal(b, b_target)

        state = np.random.get_state()
        rng = Normal()
        sim_data = egarch.simulate(parameters, self.T, rng.simulate([]))
        np.random.set_state(state)
        e = np.random.standard_normal(self.T + 500)
        initial_value = 0.1 / (1 - 0.95)
        lnsigma2 = np.zeros(self.T + 500)
        lnsigma2[0] = initial_value
        sigma2 = np.zeros(self.T + 500)
        sigma2[0] = np.exp(lnsigma2[0])
        data = np.zeros(self.T + 500)
        data[0] = np.sqrt(sigma2[0]) * e[0]
        norm_const = np.sqrt(2 / np.pi)
        for t in range(1, self.T + 500):
            lnsigma2[t] = parameters[0]
            lnsigma2[t] += parameters[1] * (np.abs(e[t - 1]) - norm_const)
            lnsigma2[t] += parameters[2] * e[t - 1]
            lnsigma2[t] += parameters[3] * lnsigma2[t - 1]

        sigma2 = np.exp(lnsigma2)
        data = e * np.sqrt(sigma2)

        data = data[500:]
        sigma2 = sigma2[500:]

        assert_almost_equal(data - sim_data[0] + 1.0, np.ones_like(data))
        assert_almost_equal(sigma2 / sim_data[1], np.ones_like(sigma2))

        names = egarch.parameter_names()
        names_target = ['omega', 'alpha[1]', 'gamma[1]', 'beta[1]']
        assert_equal(names, names_target)
        assert_equal(egarch.name, 'EGARCH')
        assert_equal(egarch.num_params, 4)

        assert_equal(egarch.p, 1)
        assert_equal(egarch.o, 1)
        assert_equal(egarch.q, 1)
        assert isinstance(egarch.__str__(), str)
        txt = egarch.__repr__()
        assert str(hex(id(egarch))) in txt

        with pytest.raises(ValueError):
            EGARCH(p=0, o=0, q=1)
        with pytest.raises(ValueError):
            EGARCH(p=1, o=1, q=-1)
Ejemplo n.º 4
0
    def test_egarch(self):
        egarch = EGARCH(p=1, o=1, q=1)

        sv = egarch.starting_values(self.resids)
        assert_equal(sv.shape[0], egarch.num_params)

        bounds = egarch.bounds(self.resids)
        assert_equal(len(bounds), egarch.num_params)
        const = np.log(10000.0)
        lnv = np.log(np.mean(self.resids ** 2.0))
        assert_equal(bounds[0], (lnv - const, lnv + const))
        assert_equal(bounds[1], (-np.inf, np.inf))
        assert_equal(bounds[2], (-np.inf, np.inf))
        assert_equal(bounds[3], (0.0, 1.0))
        backcast = egarch.backcast(self.resids)

        w = 0.94 ** np.arange(75)
        backcast_test = np.sum((self.resids[:75] ** 2) * (w / w.sum()))
        assert_almost_equal(backcast, np.log(backcast_test))

        var_bounds = egarch.variance_bounds(self.resids)
        parameters = np.array([.1, .1, -.1, .95])
        egarch.compute_variance(parameters, self.resids, self.sigma2, backcast,
                                var_bounds)
        cond_var_direct = np.zeros_like(self.sigma2)
        lnsigma2 = np.empty(self.T)
        std_resids = np.empty(self.T)
        abs_std_resids = np.empty(self.T)
        rec.egarch_recursion(parameters, self.resids, cond_var_direct, 1, 1, 1,
                             self.T, backcast, var_bounds, lnsigma2, std_resids,
                             abs_std_resids)
        assert_allclose(self.sigma2, cond_var_direct)

        A, b = egarch.constraints()
        A_target = np.vstack((np.array([[0, 0, 0, -1.0]])))
        b_target = np.array([-1.0])
        assert_array_equal(A, A_target)
        assert_array_equal(b, b_target)

        state = np.random.get_state()
        rng = Normal()
        sim_data = egarch.simulate(parameters, self.T, rng.simulate([]))
        np.random.set_state(state)
        e = np.random.standard_normal(self.T + 500)
        initial_value = 0.1 / (1 - 0.95)
        lnsigma2 = np.zeros(self.T + 500)
        lnsigma2[0] = initial_value
        sigma2 = np.zeros(self.T + 500)
        sigma2[0] = np.exp(lnsigma2[0])
        data = np.zeros(self.T + 500)
        data[0] = np.sqrt(sigma2[0]) * e[0]
        norm_const = np.sqrt(2 / np.pi)
        for t in range(1, self.T + 500):
            lnsigma2[t] = parameters[0]
            lnsigma2[t] += parameters[1] * (np.abs(e[t - 1]) - norm_const)
            lnsigma2[t] += parameters[2] * e[t - 1]
            lnsigma2[t] += parameters[3] * lnsigma2[t - 1]

        sigma2 = np.exp(lnsigma2)
        data = e * np.sqrt(sigma2)

        data = data[500:]
        sigma2 = sigma2[500:]

        assert_almost_equal(data - sim_data[0] + 1.0, np.ones_like(data))
        assert_almost_equal(sigma2 / sim_data[1], np.ones_like(sigma2))

        names = egarch.parameter_names()
        names_target = ['omega', 'alpha[1]', 'gamma[1]', 'beta[1]']
        assert_equal(names, names_target)
        assert_equal(egarch.name, 'EGARCH')
        assert_equal(egarch.num_params, 4)

        assert_equal(egarch.p, 1)
        assert_equal(egarch.o, 1)
        assert_equal(egarch.q, 1)