def __init__(self):
dataset = data.sp500_closing_prices(num_points=100)
super(StochasticVolatilitySP500Small, self).__init__(
name='stochastic_volatility_sp500_small',
pretty_name=
'Smaller stochastic volatility model of S&P500 returns.',
**dataset)
def testStochasticVolatilityModelSP500(self):
num_train_points = 2516
dataset = data.sp500_closing_prices()
self.assertEqual((num_train_points, ),
dataset['centered_returns'].shape)
self.assertAllClose(0.0,
np.mean(dataset['centered_returns']),
atol=1e-5)
def stochastic_volatility_sp500():
"""Stochastic volatility model.
This uses a dataset of 2517 daily closing prices of the S&P 500 index,
representing the time period 6/25/2010-6/24/2020.
Returns:
target: StanModel.
"""
dataset = data.sp500_closing_prices()
return stochastic_volatility.stochastic_volatility(**dataset)
def stochastic_volatility_sp500_small():
"""Stochastic volatility model.
This is a smaller version of `stochastic_volatility_model_sp500` using only
100 days of returns from the S&P 500, ending 6/24/2020.
Returns:
target: StanModel.
"""
dataset = data.sp500_closing_prices(num_points=100)
return stochastic_volatility.stochastic_volatility(**dataset)
def __init__(self):
dataset = data.sp500_closing_prices()
super(StochasticVolatilitySP500, self).__init__(
name='stochastic_volatility_sp500',
pretty_name='Stochastic volatility model of S&P500 returns.',
**dataset)