def test_FinEquityBasketOption():
import time
valueDate = FinDate(2015, 1, 1)
expiryDate = FinDate(2016, 1, 1)
volatility = 0.30
interestRate = 0.05
discountCurve = FinDiscountCurveFlat(valueDate, interestRate)
##########################################################################
# Homogeneous Basket
##########################################################################
numAssets = 5
volatilities = np.ones(numAssets) * volatility
dividendYields = np.ones(numAssets) * 0.01
stockPrices = np.ones(numAssets) * 100
betaList = np.linspace(0.0, 0.999999, 11)
testCases.header("NumPaths", "Beta", "Value", "ValueMC", "TIME")
for beta in betaList:
for numPaths in [10000]:
callOption = FinEquityBasketOption(expiryDate, 100.0,
FinOptionTypes.EUROPEAN_CALL,
numAssets)
betas = np.ones(numAssets) * beta
corrMatrix = betaVectorToCorrMatrix(betas)
start = time.time()
v = callOption.value(valueDate, stockPrices, discountCurve,
dividendYields, volatilities, corrMatrix)
vMC = callOption.valueMC(valueDate, stockPrices, discountCurve,
dividendYields, volatilities, corrMatrix,
numPaths)
end = time.time()
duration = end - start
testCases.print(numPaths, beta, v, vMC, duration)
##########################################################################
# INHomogeneous Basket
##########################################################################
numAssets = 5
volatilities = np.array([0.3, 0.2, 0.25, 0.22, 0.4])
dividendYields = np.array([0.01, 0.02, 0.04, 0.01, 0.02])
stockPrices = np.array([100, 105, 120, 100, 90])
betaList = np.linspace(0.0, 0.999999, 11)
testCases.header("NumPaths", "Beta", "Value", "ValueMC", "TIME")
for beta in betaList:
for numPaths in [10000]:
callOption = FinEquityBasketOption(expiryDate, 100.0,
FinOptionTypes.EUROPEAN_CALL,
numAssets)
betas = np.ones(numAssets) * beta
corrMatrix = betaVectorToCorrMatrix(betas)
start = time.time()
v = callOption.value(valueDate, stockPrices, discountCurve,
dividendYields, volatilities, corrMatrix)
vMC = callOption.valueMC(valueDate, stockPrices, discountCurve,
dividendYields, volatilities, corrMatrix,
numPaths)
end = time.time()
duration = end - start
testCases.print(numPaths, beta, v, vMC, duration)
##########################################################################
# Homogeneous Basket
##########################################################################
numAssets = 5
volatilities = np.ones(numAssets) * volatility
dividendYields = np.ones(numAssets) * 0.01
stockPrices = np.ones(numAssets) * 100
betaList = np.linspace(0.0, 0.999999, 11)
testCases.header("NumPaths", "Beta", "Value", "ValueMC", "TIME")
for beta in betaList:
for numPaths in [10000]:
callOption = FinEquityBasketOption(expiryDate, 100.0,
FinOptionTypes.EUROPEAN_PUT,
numAssets)
betas = np.ones(numAssets) * beta
corrMatrix = betaVectorToCorrMatrix(betas)
start = time.time()
v = callOption.value(valueDate, stockPrices, discountCurve,
dividendYields, volatilities, corrMatrix)
vMC = callOption.valueMC(valueDate, stockPrices, discountCurve,
dividendYields, volatilities, corrMatrix,
numPaths)
end = time.time()
duration = end - start
testCases.print(numPaths, beta, v, vMC, duration)
##########################################################################
# INHomogeneous Basket
##########################################################################
numAssets = 5
volatilities = np.array([0.3, 0.2, 0.25, 0.22, 0.4])
dividendYields = np.array([0.01, 0.02, 0.04, 0.01, 0.02])
stockPrices = np.array([100, 105, 120, 100, 90])
betaList = np.linspace(0.0, 0.999999, 11)
testCases.header("NumPaths", "Beta", "Value", "ValueMC", "TIME")
for beta in betaList:
for numPaths in [10000]:
callOption = FinEquityBasketOption(expiryDate, 100.0,
FinOptionTypes.EUROPEAN_PUT,
numAssets)
betas = np.ones(numAssets) * beta
corrMatrix = betaVectorToCorrMatrix(betas)
start = time.time()
v = callOption.value(valueDate, stockPrices, discountCurve,
dividendYields, volatilities, corrMatrix)
vMC = callOption.valueMC(valueDate, stockPrices, discountCurve,
dividendYields, volatilities, corrMatrix,
numPaths)
end = time.time()
duration = end - start
testCases.print(numPaths, beta, v, vMC, duration)
def test_FinEquityRainbowOption():
# import matplotlib.pyplot as plt
valueDate = FinDate(2015, 1, 1)
expiryDate = FinDate(2016, 1, 1)
interestRate = 0.05
discountCurve = FinDiscountCurveFlat(valueDate, interestRate)
numAssets = 2
volatilities = np.ones(numAssets) * 0.3
dividendYields = np.ones(numAssets) * 0.01
stockPrices = np.ones(numAssets) * 100
numPathsList = [10000]
corrList = np.linspace(0.0, 0.999999, 6)
strike = 100.0
testCases.banner(
"===================================================================")
testCases.banner(" CALL ON MAXIMUM")
testCases.banner(
"===================================================================")
payoffType = FinEquityRainbowOptionTypes.CALL_ON_MAXIMUM
payoffParams = [strike]
rainbowOption = FinEquityRainbowOption(expiryDate, payoffType,
payoffParams, numAssets)
rainboxOptionValues = []
rainbowOptionValuesMC = []
testCases.header("NUMPATHS", "CORRELATION", "VALUE", "VALUE_MC", "TIME")
for correlation in corrList:
betas = np.ones(numAssets) * sqrt(correlation)
corrMatrix = betaVectorToCorrMatrix(betas)
for numPaths in numPathsList:
start = time.time()
v = rainbowOption.value(valueDate, stockPrices, discountCurve,
dividendYields, volatilities, corrMatrix)
v_MC = rainbowOption.valueMC(valueDate, stockPrices, discountCurve,
dividendYields, volatilities,
corrMatrix, numPaths)
end = time.time()
duration = end - start
testCases.print(numPaths, correlation, v, v_MC, duration)
rainboxOptionValues.append(v)
rainbowOptionValuesMC.append(v_MC)
# plt.figure(figsize=(10,8))
# plt.plot(corrList, rainboxOptionValues, color = 'r', label = "CALL ON MAX Rainbow Option Analytical")
# plt.plot(corrList, rainbowOptionValuesMC, 'o', color = 'b', label = "CALL ON MAX Rainbow Option MC")
# plt.xlabel("Correlation")
# plt.legend(loc='best')
##########################################################################
testCases.banner(
"===================================================================")
testCases.banner(" CALL ON MINIMUM")
testCases.banner(
"===================================================================")
payoffType = FinEquityRainbowOptionTypes.CALL_ON_MINIMUM
payoffParams = [strike]
rainbowOption = FinEquityRainbowOption(expiryDate, payoffType,
payoffParams, numAssets)
rainboxOptionValues = []
rainbowOptionValuesMC = []
testCases.header("NUMPATHS", "CORRELATION", "VALUE", "VALUE_MC", "TIME")
for correlation in corrList:
betas = np.ones(numAssets) * sqrt(correlation)
corrMatrix = betaVectorToCorrMatrix(betas)
for numPaths in numPathsList:
start = time.time()
v = rainbowOption.value(valueDate, stockPrices, discountCurve,
dividendYields, volatilities, corrMatrix)
v_MC = rainbowOption.valueMC(valueDate, stockPrices, discountCurve,
dividendYields, volatilities,
corrMatrix, numPaths)
end = time.time()
duration = end - start
testCases.print(numPaths, correlation, v, v_MC, duration)
rainboxOptionValues.append(v)
rainbowOptionValuesMC.append(v_MC)
# plt.figure(figsize=(10,8))
# plt.plot(corrList, rainboxOptionValues, color = 'r', label = "CALL ON MIN Rainbow Option Analytical")
# plt.plot(corrList, rainbowOptionValuesMC, 'o', color = 'b', label = "CALL ON MIN Rainbow Option MC")
# plt.xlabel("Correlation")
# plt.legend(loc='best')
###############################################################################
testCases.banner(
"===================================================================")
testCases.banner(" PUT ON MAXIMUM")
testCases.banner(
"===================================================================")
payoffType = FinEquityRainbowOptionTypes.PUT_ON_MAXIMUM
payoffParams = [strike]
rainbowOption = FinEquityRainbowOption(expiryDate, payoffType,
payoffParams, numAssets)
rainboxOptionValues = []
rainbowOptionValuesMC = []
testCases.header("NUMPATHS", "CORRELATION", "VALUE", "VALUE_MC", "TIME")
for correlation in corrList:
betas = np.ones(numAssets) * sqrt(correlation)
corrMatrix = betaVectorToCorrMatrix(betas)
for numPaths in numPathsList:
start = time.time()
v = rainbowOption.value(valueDate, stockPrices, discountCurve,
dividendYields, volatilities, corrMatrix)
v_MC = rainbowOption.valueMC(valueDate, stockPrices, discountCurve,
dividendYields, volatilities,
corrMatrix, numPaths)
end = time.time()
duration = end - start
testCases.print(numPaths, correlation, v, v_MC, duration)
rainboxOptionValues.append(v)
rainbowOptionValuesMC.append(v_MC)
# plt.figure(figsize=(10,8))
# plt.plot(corrList, rainboxOptionValues, color = 'r', label = "PUT ON MAX Rainbow Option Analytical")
# plt.plot(corrList, rainbowOptionValuesMC, 'o', color = 'b', label = "PUT ON MAX Rainbow Option MC")
# plt.xlabel("Correlation")
# plt.legend(loc='best')
##########################################################################
testCases.banner(
"===================================================================")
testCases.banner(" PUT ON MINIMUM")
testCases.banner(
"===================================================================")
payoffType = FinEquityRainbowOptionTypes.PUT_ON_MINIMUM
payoffParams = [strike]
rainbowOption = FinEquityRainbowOption(expiryDate, payoffType,
payoffParams, numAssets)
rainboxOptionValues = []
rainbowOptionValuesMC = []
testCases.header("NUMPATHS", "CORRELATION", "VALUE", "VALUE_MC", "TIME")
for correlation in corrList:
betas = np.ones(numAssets) * sqrt(correlation)
corrMatrix = betaVectorToCorrMatrix(betas)
for numPaths in numPathsList:
start = time.time()
v = rainbowOption.value(valueDate, stockPrices, discountCurve,
dividendYields, volatilities, corrMatrix)
v_MC = rainbowOption.valueMC(valueDate, stockPrices, discountCurve,
dividendYields, volatilities,
corrMatrix, numPaths)
end = time.time()
duration = end - start
testCases.print(numPaths, correlation, v, v_MC, duration)
rainboxOptionValues.append(v)
rainbowOptionValuesMC.append(v_MC)
# plt.figure(figsize=(10,8))
# plt.plot(corrList, rainboxOptionValues, color = 'r', label = "PUT ON MIN Rainbow Option Analytical")
# plt.plot(corrList, rainbowOptionValuesMC, 'o', color = 'b', label = "PUT ON MIN Rainbow Option MC")
# plt.xlabel("Correlation")
# plt.legend(loc='best')
##########################################################################
numAssets = 2
volatilities = np.ones(numAssets) * 0.3
dividendYields = np.ones(numAssets) * 0.01
stockPrices = np.ones(numAssets) * 100
strike = 100.0
correlation = 0.50
testCases.banner(
"===================================================================")
testCases.banner(" CALL ON 1st")
testCases.banner(
"===================================================================")
rainboxOptionValues = []
rainbowOptionValuesMC = []
testCases.header("NUMPATHS", "CORRELATION", "VALUE", "VALUE_MC", "TIME")
for correlation in corrList:
betas = np.ones(numAssets) * sqrt(correlation)
corrMatrix = betaVectorToCorrMatrix(betas)
for numPaths in numPathsList:
payoffType1 = FinEquityRainbowOptionTypes.CALL_ON_MAXIMUM
payoffParams1 = [strike]
rainbowOption1 = FinEquityRainbowOption(expiryDate, payoffType1,
payoffParams1, numAssets)
payoffType2 = FinEquityRainbowOptionTypes.CALL_ON_NTH
payoffParams2 = [1, strike]
rainbowOption2 = FinEquityRainbowOption(expiryDate, payoffType2,
payoffParams2, numAssets)
start = time.time()
v = rainbowOption1.value(valueDate, stockPrices, discountCurve,
dividendYields, volatilities, corrMatrix)
v_MC = rainbowOption2.valueMC(valueDate, stockPrices,
discountCurve, dividendYields,
volatilities, corrMatrix, numPaths)
end = time.time()
duration = end - start
testCases.print(numPaths, correlation, v, v_MC, duration)
rainboxOptionValues.append(v)
rainbowOptionValuesMC.append(v_MC)
# plt.figure(figsize=(10,8))
# plt.plot(corrList, rainboxOptionValues, color = 'r', label = "CALL ON MAX Rainbow Option Analytical")
# plt.plot(corrList, rainbowOptionValuesMC, 'o', color = 'b', label = "CALL ON 1st Rainbow Option MC")
# plt.xlabel("Correlation")
# plt.legend(loc='best')
testCases.banner(
"===================================================================")
testCases.banner(" CALL ON 2nd")
testCases.banner(
"===================================================================")
rainboxOptionValues = []
rainbowOptionValuesMC = []
testCases.header("NUMPATHS", "CORRELATION", "VALUE", "VALUE_MC", "TIME")
for correlation in corrList:
betas = np.ones(numAssets) * sqrt(correlation)
corrMatrix = betaVectorToCorrMatrix(betas)
for numPaths in numPathsList:
payoffType1 = FinEquityRainbowOptionTypes.CALL_ON_MINIMUM
payoffParams1 = [strike]
rainbowOption1 = FinEquityRainbowOption(expiryDate, payoffType1,
payoffParams1, numAssets)
payoffType2 = FinEquityRainbowOptionTypes.CALL_ON_NTH
payoffParams2 = [2, strike]
rainbowOption2 = FinEquityRainbowOption(expiryDate, payoffType2,
payoffParams2, numAssets)
start = time.time()
v = rainbowOption1.value(valueDate, stockPrices, discountCurve,
dividendYields, volatilities, corrMatrix)
v_MC = rainbowOption2.valueMC(valueDate, stockPrices,
discountCurve, dividendYields,
volatilities, corrMatrix, numPaths)
end = time.time()
duration = end - start
testCases.print(numPaths, correlation, v, v_MC, duration)
rainboxOptionValues.append(v)
rainbowOptionValuesMC.append(v_MC)
# plt.figure(figsize=(10,8))
# plt.plot(corrList, rainboxOptionValues, color = 'r', label = "CALL ON MIN Rainbow Option Analytical")
# plt.plot(corrList, rainbowOptionValuesMC, 'o', color = 'b', label = "CALL ON 2nd Rainbow Option MC")
# plt.xlabel("Correlation")
# plt.legend(loc='best')
testCases.banner(
"===================================================================")
testCases.banner(" CALL ON 1-5")
testCases.banner(
"===================================================================")
rainboxOptionValues = []
rainbowOptionValuesMC = []
numPaths = 10000
numAssets = 5
volatilities = np.ones(numAssets) * 0.3
dividendYields = np.ones(numAssets) * 0.01
stockPrices = np.ones(numAssets) * 100
# plt.figure(figsize=(10,8))
testCases.header("NUMPATHS", "CORRELATION", "NTD", "VALUE", "VALUE_MC",
"TIME")
for n in [1, 2, 3, 4, 5]:
rainboxOptionValues = []
rainbowOptionValuesMC = []
payoffType2 = FinEquityRainbowOptionTypes.CALL_ON_NTH
payoffParams2 = [n, strike]
rainbowOption2 = FinEquityRainbowOption(expiryDate, payoffType2,
payoffParams2, numAssets)
for correlation in corrList:
betas = np.ones(numAssets) * sqrt(correlation)
corrMatrix = betaVectorToCorrMatrix(betas)
start = time.time()
v_MC = rainbowOption2.valueMC(valueDate, stockPrices,
discountCurve, dividendYields,
volatilities, corrMatrix, numPaths)
end = time.time()
duration = end - start
testCases.print(numPaths, correlation, n, v, v_MC, duration)
rainbowOptionValuesMC.append(v_MC)
# plt.plot(corrList, rainbowOptionValuesMC, 'o-', label = "CALL Rainbow Option MC NTH = " + str(n))
# plt.xlabel("Correlation")
# plt.legend(loc='best')
testCases.banner(
"===================================================================")
testCases.banner(" PUT ON 1-5")
testCases.banner(
"===================================================================")
rainboxOptionValues = []
rainbowOptionValuesMC = []
numPaths = 10000
numAssets = 5
volatilities = np.ones(numAssets) * 0.3
dividendYields = np.ones(numAssets) * 0.01
stockPrices = np.ones(numAssets) * 100
# plt.figure(figsize=(10,8))
testCases.header("NUMPATHS", "CORRELATION", "NTD", "VALUE", "VALUE_MC",
"TIME")
for n in [1, 2, 3, 4, 5]:
rainboxOptionValues = []
rainbowOptionValuesMC = []
payoffType2 = FinEquityRainbowOptionTypes.PUT_ON_NTH
payoffParams2 = [n, strike]
rainbowOption2 = FinEquityRainbowOption(expiryDate, payoffType2,
payoffParams2, numAssets)
for correlation in corrList:
betas = np.ones(numAssets) * sqrt(correlation)
corrMatrix = betaVectorToCorrMatrix(betas)
start = time.time()
v_MC = rainbowOption2.valueMC(valueDate, stockPrices,
discountCurve, dividendYields,
volatilities, corrMatrix, numPaths)
end = time.time()
duration = end - start
testCases.print(numPaths, correlation, n, v, v_MC, duration)
rainbowOptionValuesMC.append(v_MC)