def test_inhomogeneous_put():
volatilities = np.array([0.3, 0.2, 0.25, 0.22, 0.4])
dividend_yields = np.array([0.01, 0.02, 0.04, 0.01, 0.02])
stock_prices = np.array([100, 105, 120, 100, 90])
dividend_curves = []
for q in dividend_yields:
dividend_curve = DiscountCurveFlat(valuation_date, q)
dividend_curves.append(dividend_curve)
call_option = EquityBasketOption(expiry_date, 100.0,
OptionTypes.EUROPEAN_PUT, num_assets)
value = call_option.value(valuation_date, stock_prices, discount_curve,
dividend_curves, volatilities, corr_matrix)
assert round(value, 4) == 7.9126
value_mc = call_option.value_mc(valuation_date, stock_prices,
discount_curve, dividend_curves,
volatilities, corr_matrix, num_paths)
assert round(value_mc, 4) == 7.8216
def test_homogeneous_put():
volatilities = np.ones(num_assets) * volatility
dividend_yields = np.ones(num_assets) * 0.01
stock_prices = np.ones(num_assets) * 100
dividend_curves = []
for q in dividend_yields:
dividend_curve = DiscountCurveFlat(valuation_date, q)
dividend_curves.append(dividend_curve)
call_option = EquityBasketOption(expiry_date, 100.0,
OptionTypes.EUROPEAN_PUT, num_assets)
value = call_option.value(valuation_date, stock_prices, discount_curve,
dividend_curves, volatilities, corr_matrix)
assert round(value, 4) == 9.7344
value_mc = call_option.value_mc(valuation_date, stock_prices,
discount_curve, dividend_curves,
volatilities, corr_matrix, num_paths)
assert round(value_mc, 4) == 9.6986
def test_EquityBasketOption():
import time
valuation_date = Date(1, 1, 2015)
expiry_date = Date(1, 1, 2016)
volatility = 0.30
interest_rate = 0.05
discount_curve = DiscountCurveFlat(valuation_date, interest_rate)
##########################################################################
# Homogeneous Basket
##########################################################################
num_assets = 5
volatilities = np.ones(num_assets) * volatility
dividend_yields = np.ones(num_assets) * 0.01
stock_prices = np.ones(num_assets) * 100
dividend_curves = []
for q in dividend_yields:
dividend_curve = DiscountCurveFlat(valuation_date, q)
dividend_curves.append(dividend_curve)
betaList = np.linspace(0.0, 0.999999, 11)
testCases.header("NumPaths", "Beta", "Value", "ValueMC", "TIME")
for beta in betaList:
for num_paths in [10000]:
call_option = EquityBasketOption(expiry_date, 100.0,
OptionTypes.EUROPEAN_CALL,
num_assets)
betas = np.ones(num_assets) * beta
corr_matrix = beta_vector_to_corr_matrix(betas)
start = time.time()
v = call_option.value(valuation_date, stock_prices, discount_curve,
dividend_curves, volatilities, corr_matrix)
vMC = call_option.value_mc(valuation_date, stock_prices,
discount_curve, dividend_curves,
volatilities, corr_matrix, num_paths)
end = time.time()
duration = end - start
testCases.print(num_paths, beta, v, vMC, duration)
##########################################################################
# INHomogeneous Basket
##########################################################################
num_assets = 5
volatilities = np.array([0.3, 0.2, 0.25, 0.22, 0.4])
dividend_yields = np.array([0.01, 0.02, 0.04, 0.01, 0.02])
stock_prices = np.array([100, 105, 120, 100, 90])
dividend_curves = []
for q in dividend_yields:
dividend_curve = DiscountCurveFlat(valuation_date, q)
dividend_curves.append(dividend_curve)
betaList = np.linspace(0.0, 0.999999, 11)
testCases.header("NumPaths", "Beta", "Value", "ValueMC", "TIME")
for beta in betaList:
for num_paths in [10000]:
call_option = EquityBasketOption(expiry_date, 100.0,
OptionTypes.EUROPEAN_CALL,
num_assets)
betas = np.ones(num_assets) * beta
corr_matrix = beta_vector_to_corr_matrix(betas)
start = time.time()
v = call_option.value(valuation_date, stock_prices, discount_curve,
dividend_curves, volatilities, corr_matrix)
vMC = call_option.value_mc(valuation_date, stock_prices,
discount_curve, dividend_curves,
volatilities, corr_matrix, num_paths)
end = time.time()
duration = end - start
testCases.print(num_paths, beta, v, vMC, duration)
##########################################################################
# Homogeneous Basket
##########################################################################
num_assets = 5
volatilities = np.ones(num_assets) * volatility
dividend_yields = np.ones(num_assets) * 0.01
stock_prices = np.ones(num_assets) * 100
betaList = np.linspace(0.0, 0.999999, 11)
dividend_curves = []
for q in dividend_yields:
dividend_curve = DiscountCurveFlat(valuation_date, q)
dividend_curves.append(dividend_curve)
testCases.header("NumPaths", "Beta", "Value", "ValueMC", "TIME")
for beta in betaList:
for num_paths in [10000]:
call_option = EquityBasketOption(expiry_date, 100.0,
OptionTypes.EUROPEAN_PUT,
num_assets)
betas = np.ones(num_assets) * beta
corr_matrix = beta_vector_to_corr_matrix(betas)
start = time.time()
v = call_option.value(valuation_date, stock_prices, discount_curve,
dividend_curves, volatilities, corr_matrix)
vMC = call_option.value_mc(valuation_date, stock_prices,
discount_curve, dividend_curves,
volatilities, corr_matrix, num_paths)
end = time.time()
duration = end - start
testCases.print(num_paths, beta, v, vMC, duration)
##########################################################################
# INHomogeneous Basket
##########################################################################
num_assets = 5
volatilities = np.array([0.3, 0.2, 0.25, 0.22, 0.4])
dividend_yields = np.array([0.01, 0.02, 0.04, 0.01, 0.02])
stock_prices = np.array([100, 105, 120, 100, 90])
betaList = np.linspace(0.0, 0.999999, 11)
dividend_curves = []
for q in dividend_yields:
dividend_curve = DiscountCurveFlat(valuation_date, q)
dividend_curves.append(dividend_curve)
testCases.header("NumPaths", "Beta", "Value", "ValueMC", "TIME")
for beta in betaList:
for num_paths in [10000]:
call_option = EquityBasketOption(expiry_date, 100.0,
OptionTypes.EUROPEAN_PUT,
num_assets)
betas = np.ones(num_assets) * beta
corr_matrix = beta_vector_to_corr_matrix(betas)
start = time.time()
v = call_option.value(valuation_date, stock_prices, discount_curve,
dividend_curves, volatilities, corr_matrix)
vMC = call_option.value_mc(valuation_date, stock_prices,
discount_curve, dividend_curves,
volatilities, corr_matrix, num_paths)
end = time.time()
duration = end - start
testCases.print(num_paths, beta, v, vMC, duration)