Any QuantLib experts here?
- Thread starter earth_imperator
- Start date
Been using it a while ago for some fixed income implementations (C++). What you wanna know?
GPT is an expert
import QuantLib as ql
# Setting the evaluation date
calculation_date = ql.Date(1, 1, 2022)
ql.Settings.instance().evaluationDate = calculation_date
# Bond parameters
face_value = 100
coupon_rate = 0.05
coupon_type = ql.Annual
bond_maturity = 10
# Create a flat yield term structure
interest_rate = ql.SimpleQuote(0.03)
rate_handle = ql.QuoteHandle(interest_rate)
day_count = ql.ActualActual()
term_structure = ql.FlatForward(calculation_date, rate_handle, day_count)
ts_handle = ql.YieldTermStructureHandle(term_structure)
# Construct the bond schedule
issue_date = calculation_date
maturity_date = issue_date + ql.Period(bond_maturity, ql.Years)
schedule = ql.Schedule(issue_date, maturity_date, ql.Period(coupon_type),
ql.TARGET(), ql.Unadjusted, ql.Unadjusted,
ql.DateGeneration.Backward, False)
# Construct the bond
bond = ql.FixedRateBond(0, face_value, schedule, [coupon_rate], day_count)
# Get the bond's present value
bond_engine = ql.DiscountingBondEngine(ts_handle)
bond.setPricingEngine(bond_engine)
print("Bond's present value:", bond.NPV())
import QuantLib as ql
# Setting the evaluation date
calculation_date = ql.Date(1, 1, 2022)
ql.Settings.instance().evaluationDate = calculation_date
# Bond parameters
face_value = 100
coupon_rate = 0.05
coupon_type = ql.Annual
bond_maturity = 10
# Create a flat yield term structure
interest_rate = ql.SimpleQuote(0.03)
rate_handle = ql.QuoteHandle(interest_rate)
day_count = ql.ActualActual()
term_structure = ql.FlatForward(calculation_date, rate_handle, day_count)
ts_handle = ql.YieldTermStructureHandle(term_structure)
# Construct the bond schedule
issue_date = calculation_date
maturity_date = issue_date + ql.Period(bond_maturity, ql.Years)
schedule = ql.Schedule(issue_date, maturity_date, ql.Period(coupon_type),
ql.TARGET(), ql.Unadjusted, ql.Unadjusted,
ql.DateGeneration.Backward, False)
# Construct the bond
bond = ql.FixedRateBond(0, face_value, schedule, [coupon_rate], day_count)
# Get the bond's present value
bond_engine = ql.DiscountingBondEngine(ts_handle)
bond.setPricingEngine(bond_engine)
print("Bond's present value:", bond.NPV())
I'm looking for a QuantLib code that simulates stock prices by using GBM (Geometric Brownian Motion),
for example daily prices for a year.
I found the following old code from 2013, but I am not sure whether it's correct:
https://mhittesdorf.wordpress.com/2...-asset-prices-with-geometric-brownian-motion/
I'm not sure whether this Box-Muller method for getting normal distribution is correct.
Why is it not simply using the built-in (in C++11 and higher) std::normal_distribution instead?
What do you folks think about this?
Do you have got a better GBM code in QuantLib?
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It's indeed not easy to use this library
Maybe over-engineered.Yes, a GBM generator in QuantLib. GBM usually is part of any such financial library.
As said, I need a GBM example code in QuantLib. I'm not sure wthether the above linked GBM code is correct as it's very old, therefore asking some QuantLib experts here for their expertise.
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You don't need QuantLib for that. In fact, you can easily simulate stock prices with an underlying GBM engine within excel.
https://investexcel.net/geometric-brownian-motion-excel/
https://investexcel.net/geometric-brownian-motion-excel/
from what I see you create the process and generate paths using the process.
https://github.com/lballabio/QuantLib/tree/master/ql/processes
agree with MW, this can be done in anything. a few lines of python
https://github.com/lballabio/QuantLib/tree/master/ql/processes
agree with MW, this can be done in anything. a few lines of python