def test():
print('gtwoext test...', filename)
ref_date = mx.Date(2018, 9, 9)
tenor_rates = [('3M', 0.0151), ('6M', 0.0152), ('9M', 0.0153),
('1Y', 0.0154), ('2Y', 0.0155), ('3Y', 0.0156),
('4Y', 0.0157), ('5Y', 0.0158), ('7Y', 0.0159),
('10Y', 0.016), ('15Y', 0.0161), ('20Y', 0.0162)]
tenors = []
zerorates = []
interpolator1DType = mx.Interpolator1D.Linear
extrapolator1DType = mx.Extrapolator1D.FlatForward
for tr in tenor_rates:
tenors.append(tr[0])
zerorates.append(tr[1])
fittingCurve = mx.ZeroYieldCurveExt(ref_date, tenors, zerorates,
interpolator1DType, extrapolator1DType,
'irskrw_krccp')
alpha1 = 0.1
sigma1 = 0.01
alpha2 = 0.2
sigma2 = 0.02
corr = 0.5
g2ext = mx.GTwoExtModel('g2ext', fittingCurve, alpha1, sigma1, alpha2,
sigma2, corr)
models = [g2ext]
corrMatrix = mx.Matrix([[1.0]])
timeGrid = mx.TimeEqualGrid(ref_date, 3, 365)
# random
scenario_num = 5000
dimension = (len(timeGrid) - 1) * len(models)
seed = 100
skip = 0
isMomentMatching = False
randomType = "crude"
subType = "mersennetwister"
randomTransformType = "invnormal"
rsg = mx.Rsg(scenario_num, dimension, seed, skip, isMomentMatching,
randomType, subType, randomTransformType)
scen = mx.ScenarioGenerator(models, corrMatrix, timeGrid, rsg, False,
filename, False)
scen.generate()
results = mx.ScenarioResult(filename)
print(results.multiPath(scenCount=10))
def test():
print('puedo random test...')
scenario_num = 1000
dimension = 100
seed = 1
skip = 7
isMomentMatching = False
randomType = "crude"
subType = "mersennetwister"
randomTransformType = "boxmullernormal"
rsg = mx.Rsg(scenario_num, dimension, seed, skip, isMomentMatching,
randomType, subType, randomTransformType)
print(rsg.nextSequence())
def test():
print('puedo random test...')
scenario_num = 1000
dimension = 365
seed = 0
skip = 1024
isMomentMatching = False
randomType = "sobol"
subType = "joekuod7"
randomTransformType = "invnormal"
rsg = mx.Rsg(scenario_num, dimension, seed, skip, isMomentMatching,
randomType, subType, randomTransformType)
print(rsg.nextSequence())
def build_scenario(overwrite=True):
print('stepdown test...', filename)
if not overwrite:
return
initialValues = [387833, 27450]
dividends = [0.0247, 0.0181]
volatilities = [0.2809, 0.5795]
gbmconst1 = mx.GBMConstModel('gbmconst', initialValues[0], riskFree,
dividends[0], volatilities[0])
gbmconst2 = mx.GBMConstModel('gbmconst', initialValues[1], riskFree,
dividends[1], volatilities[1])
models = [gbmconst1, gbmconst2]
corr = 0.6031
corrMatrix = mx.Matrix([[1.0, corr], [corr, 1.0]])
timeGrid = mx.TimeEqualGrid(refDate, 3, 365)
# random
scenario_num = 30000
dimension = (len(timeGrid) - 1) * len(models)
seed = 100
skip = 0
isMomentMatching = False
randomType = "crude"
subType = "mersennetwister"
randomTransformType = "invnormal"
rsg = mx.Rsg(scenario_num, dimension, seed, skip, isMomentMatching,
randomType, subType, randomTransformType)
scen = mx.ScenarioGenerator(models, corrMatrix, timeGrid, rsg, False,
filename, False)
scen.generate()
results = mx.ScenarioResult(filename)
def test():
print('multiplemodels test...', filename)
ref_date = mx.Date(2018, 9, 9)
initialValue = 10000
riskFree = 0.032
dividend = 0.01
volatility = 0.15
gbmconst1 = mx.GBMConstModel('gbmconst', initialValue, riskFree, dividend,
volatility)
gbmconst2 = mx.GBMConstModel('gbmconst', initialValue, riskFree, dividend,
volatility)
models = [gbmconst1, gbmconst2]
corrMatrix = mx.Matrix([[1.0, 0.0], [0.0, 1.0]])
timeGrid = mx.TimeEqualGrid(ref_date, 3, 365)
# random
scenario_num = 5000
dimension = (len(timeGrid) - 1) * len(models)
seed = 100
skip = 0
isMomentMatching = False
randomType = "crude"
subType = "mersennetwister"
randomTransformType = "invnormal"
rsg = mx.Rsg(scenario_num, dimension, seed, skip, isMomentMatching,
randomType, subType, randomTransformType)
scen = mx.ScenarioGenerator(models, corrMatrix, timeGrid, rsg, False,
filename, False)
scen.generate()
results = mx.ScenarioResult(filename)
print(results.multiPath(scenCount=10))
def test():
print('cir1f test...', filename)
ref_date = mx.Date(2018, 10, 10)
initialValue = 0.02
meanReverting = 0.1
longTermRate = 0.042
volatility = 0.03
cir1f = mx.CIR1FModel('cir1f', initialValue, meanReverting, longTermRate,
volatility)
models = [cir1f]
corrMatrix = mx.Matrix([[1.0]])
timeGrid = mx.TimeEqualGrid(ref_date, 3, 365)
# random
scenario_num = 5000
dimension = (len(timeGrid) - 1) * len(models)
seed = 100
skip = 0
isMomentMatching = False
randomType = "crude"
subType = "mersennetwister"
randomTransformType = "invnormal"
rsg = mx.Rsg(scenario_num, dimension, seed, skip, isMomentMatching,
randomType, subType, randomTransformType)
scen = mx.ScenarioGenerator(models, corrMatrix, timeGrid, rsg, False,
filename, False)
scen.generate()
results = mx.ScenarioResult(filename)
print(results.multiPath(scenCount=10))
def test():
print('gbm test...', filename)
ref_date = mx.Date(2018, 9, 9)
initialValue = 10000
# (period, rf, div)
tenor_rates = [('3M', 0.0151, 0.01), ('6M', 0.0152, 0.01),
('9M', 0.0153, 0.01), ('1Y', 0.0154, 0.01),
('2Y', 0.0155, 0.01), ('3Y', 0.0156, 0.01),
('4Y', 0.0157, 0.01), ('5Y', 0.0158, 0.01),
('7Y', 0.0159, 0.01), ('10Y', 0.016, 0.01),
('15Y', 0.0161, 0.01), ('20Y', 0.0162, 0.01)]
tenors = []
rf_rates = []
div_rates = []
vols = []
interpolator1DType = mx.Interpolator1D.Linear
extrapolator1DType = mx.Extrapolator1D.FlatForward
for tr in tenor_rates:
tenors.append(tr[0])
rf_rates.append(tr[1])
div_rates.append(tr[2])
rfCurve = mx.ZeroYieldCurveExt(ref_date, tenors, rf_rates,
interpolator1DType, extrapolator1DType,
'irskrw_krccp')
divCurve = mx.ZeroYieldCurveExt(ref_date, tenors, div_rates,
interpolator1DType, extrapolator1DType,
'irskrw_krccp')
volCurve = mx.BlackConstantVol(ref_date, 0.2, 'irskrw_krccp')
gbm = mx.GBMModel('gbm', initialValue, rfCurve, divCurve, volCurve)
models = [gbm]
corrMatrix = mx.Matrix([[1.0]])
timeGrid = mx.TimeEqualGrid(ref_date, 3, 365)
# random
scenario_num = 5000
dimension = (len(timeGrid) - 1) * len(models)
seed = 100
skip = 0
isMomentMatching = False
randomType = "crude"
subType = "mersennetwister"
randomTransformType = "invnormal"
rsg = mx.Rsg(scenario_num, dimension, seed, skip, isMomentMatching,
randomType, subType, randomTransformType)
scen = mx.ScenarioGenerator(models, corrMatrix, timeGrid, rsg, False,
filename, False)
scen.generate()
results = mx.ScenarioResult(filename)
print(results.multiPath(scenCount=10))