QuantLib Python Cookbook
QuantLib Python Cookbook
Luigi Ballabio and Goutham Balaraman
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The authors have used good faith effort in preparation of this book, but make no expressed or implied warranty of any kind and disclaim without limitation all responsibility for errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of the use of the information or programs contained herein. Use of the information and instructions in this book is at your own risk.

The cover image is in the public domain and available from the New York Public Library. The cover font is Open Sans Condensed, released by Steve Matteson under the Apache License version 2.0.

A note on Python and C++

The choice of using the QuantLib Python bindings and Jupyter was due to their interactivity, which make it easier to demonstrate features, and to the fact that the platform provides out of the box excellent modules like matplotlib for graphing and pandas for data analysis.

This choice might seem to leave C++ users out in the cold. However, it’s easy enough to translate the Python code shown here into the corresponding C++ code. An example of such translation is shown in the appendix.

Code conventions used in this book

The recipes in this cookbook are written as Jupyter notebooks, and follow their structure: blocks of explanatory text, like the one you’re reading now, are mixed with cells containing Python code (inputs) and the results of executing it (outputs). The code and its output—if any—are marked by In [N] and Out [N], respectively, with N being the index of the cell. You can see an example in the computations below:

In [1]: def f(x, y):
            return x + 2*y

In [2]: a = 4
        b = 2
        f(a, b)

Out[2]: 8

By default, Jupyter displays the result of the last instruction as the output of a cell, like it did above; however, print statements can display further results.

In [3]: print(a)
        print(b)
        print(f(b, a))

Out[3]: 4
        2
        10

Jupyter also knows a few specific data types, such as Pandas data frames, and displays them in a more readable way:

In [4]: import pandas as pd
        pd.DataFrame({ 'foo': [1,2,3], 'bar': ['a','b','c'] })

Out[4]: 
  foo bar
0 1 a
1 2 b
2 3 c

The index of the cells shows the order of their execution. Jupyter doesn’t constrain it; however, in all of the recipes of this book the cells were executed in sequential order as displayed. All cells are executed in the global Python scope; this means that, as we execute the code in the recipes, all variables, functions and classes defined in a cell are available to the ones that follow.

Notebooks can also include plots, as in the following cell:

In [5]: %matplotlib inline
        import numpy as np
        import utils
        f, ax = utils.plot(figsize=(10,2))
        ax.plot([0, 0.25, 0.5, 0.75, 1.0], np.random.random(5))

Out[5]: [<matplotlib.lines.Line2D at 0x7fef5c24fda0>]

As you might have noted, the cell above also printed a textual representation of the object returned from the plot, since it’s the result of the last instruction in the cell. To prevent this, cells in the recipes might have a semicolon at the end, as in the next cell. This is just a quirk of the Jupyter display system, and it doesn’t have any particular significance; I’m mentioning it here just so that you dont’t get confused by it.

In [6]: f, ax = utils.plot(figsize=(10,2))
        ax.plot([0, 0.25, 0.5, 0.75, 1.0], np.random.random(5));

Finally, the utils module that I imported above is a short module containing convenience functions, mostly related to plots, for the notebooks in this collection. It’s not necessary to understand its implementation to follow the recipes, and therefore we won’t cover it here; but if you’re interested and want to look at it, it’s included in the zip archive that you can download from Leanpub if you purchased the book.

1. QuantLib basics

In this chapter we will introduce some of the basic concepts such as Date, Period, Calendar and Schedule. These are QuantLib constructs that are used throughout the library in creation of instruments, models, term structures etc.

In [1]: import QuantLib as ql
        import pandas as pd
Date Class

The Date object can be created using the constructor as Date(day, month, year). It would be worthwhile to pay attention to the fact that day is the first argument, followed by month and then the year. This is different from the Python datetime object instantiation.

In [2]: date = ql.Date(31, 3, 2015)
        print(date)

Out[2]: March 31st, 2015

The fields of the Date object can be accessed using the month(), dayOfMonth() and year() methods. The weekday() method can be used to fetch the day of the week.

In [3]: print("%d-%d-%d" %(date.month(), 
                           date.dayOfMonth(),
                           date.year()))

Out[3]: 3-31-2015

In [4]: date.weekday() == ql.Tuesday

Out[4]: True

The Date objects can also be used to perform arithmetic operations such as advancing by days, weeks, months etc. Periods such as weeks or months can be denoted using the Period class. Period object constructor signature is Period(num_periods, period_type). The num_periods is an integer and represents the number of periods. The period_type can be Weeks, Months and Years.

In [5]: type(date+1)

Out[5]: QuantLib.QuantLib.Date


In [6]: print("Add a day      : {0}".format(date + 1))
        print("Subtract a day : {0}".format(date - 1))
        print("Add a week     : {0}".format(date + ql.Period(1, ql.Weeks)))
        print("Add a month    : {0}".format(date + ql.Period(1, ql.Months)))
        print("Add a year     : {0}".format(date + ql.Period(1, ql.Years)))

Out[6]: Add a day      : April 1st, 2015
        Subtract a day : March 30th, 2015
        Add a week     : April 7th, 2015
        Add a month    : April 30th, 2015
        Add a year     : March 31st, 2016

One can also do logical operations using the Date object.

In [7]: print(date == ql.Date(31, 3, 2015))
        print(date > ql.Date(30, 3, 2015))
        print(date < ql.Date(1, 4, 2015))
        print(date != ql.Date(1, 4, 2015))

Out[7]: True
        True
        True
        True

The Date object is used in setting valuation dates, issuance and expiry dates of instruments. The Period object is used in setting tenors, such as that of coupon payments, or in constructing payment schedules.

Calendar Class

The Date arithmetic above did not take holidays into account. But valuation of different securities would require taking into account the holidays observed in a specific exchange or country. The Calendar class implements this functionality for all the major exchanges. Let us take a look at a few examples here.

In [8]: date = ql.Date(31, 3, 2015)
        us_calendar = ql.UnitedStates()
        italy_calendar = ql.Italy()
        
        period = ql.Period(60, ql.Days)
        raw_date = date + period
        us_date = us_calendar.advance(date, period)
        italy_date = italy_calendar.advance(date, period)
        
        print("Add 60 days: {0}".format(raw_date))
        print("Add 60 business days in US: {0}".format(us_date))
        print("Add 60 business days in Italy: {0}".format(italy_date))

Out[8]: Add 60 days: May 30th, 2015
        Add 60 business days in US: June 24th, 2015
        Add 60 business days in Italy: June 26th, 2015

The addHoliday and removeHoliday methods in the calendar can be used to add and remove holidays to the calendar respectively. If a calendar has any missing holidays or has a wrong holiday, then these methods come handy in fixing the errors. The businessDaysBetween method helps find out the number of business days between two dates per a given calendar. Let us use this method on the us_calendar and italy_calendar as a sanity check.

In [9]: us_busdays = us_calendar.businessDaysBetween(date, us_date)
        italy_busdays = italy_calendar.businessDaysBetween(date, italy_date)
        
        print("Business days US: {0}".format(us_busdays))
        print("Business days Italy: {0}".format(italy_busdays))

Out[9]: Business days US: 60
        Business days Italy: 60

In valuation of certain deals, more than one calendar’s holidays are observed. QuantLib has JointCalendar class that allows you to combine the holidays of two or more calendars. Let us take a look at a working example.

In [10]: joint_calendar = ql.JointCalendar(us_calendar, italy_calendar)
         
         joint_date = joint_calendar.advance(date, period)
         joint_busdays = joint_calendar.businessDaysBetween(date, joint_date)
         
         print("Add 60 business days in US-Italy: {0}".format(joint_date))
         print("Business days US-Italy: {0}".format(joint_busdays))

Out[10]: Add 60 business days in US-Italy: June 29th, 2015
         Business days US-Italy: 60
Schedule Class

The Schedule object is necessary in creating coupon schedules or call schedules. Schedule object constructors have the following signature:

Schedule(const Date& effectiveDate,
         const Date& terminationDate,
         const Period& tenor,
         const Calendar& calendar,
         BusinessDayConvention convention,
         BusinessDayConvention terminationDateConvention,
         DateGeneration::Rule rule,
         bool endOfMonth,
         const Date& firstDate = Date(),
         const Date& nextToLastDate = Date())

and

Schedule(const std::vector<Date>&,
         const Calendar& calendar,
         BusinessDayConvention rollingConvention)

In [11]: effective_date = ql.Date(1, 1, 2015)
         termination_date = ql.Date(1, 1, 2016)
         tenor = ql.Period(ql.Monthly)
         calendar = ql.UnitedStates()
         business_convention = ql.Following
         termination_business_convention = ql.Following
         date_generation = ql.DateGeneration.Forward
         end_of_month = False
         
         schedule = ql.Schedule(effective_date,
                                termination_date,
                                tenor,
                                calendar,
                                business_convention,
                                termination_business_convention,
                                date_generation,
                                end_of_month)
         
         pd.DataFrame({'date': list(schedule)})

Out[11]: 
  date
0 January 2nd, 2015
1 February 2nd, 2015
2 March 2nd, 2015
3 April 1st, 2015
4 May 1st, 2015
5 June 1st, 2015
6 July 1st, 2015
7 August 3rd, 2015
8 September 1st, 2015
9 October 1st, 2015
10 November 2nd, 2015
11 December 1st, 2015
12 January 4th, 2016

Here we have generated a Schedule object that will contain dates between effective_date and termination_date with the tenor specifying the Period to be Monthly. The calendar object is used for determining holidays. Here we have chosen the convention to be the day following holidays. That is why we see that holidays are excluded in the list of dates.

The Schedule class can handle generation of dates with irregularity in schedule. The two extra parameters firstDate and nextToLastDate parameters along with a combination of forward or backward date generation rule can be used to generate short or long stub payments at the front or back end of the schedule. For example, the following combination of firstDate and backward generation rule creates a short stub in the front on the January 15, 2015.

In [12]: # short stub in the front
         effective_date = ql.Date(1, 1, 2015)
         termination_date = ql.Date(1, 1, 2016)
         first_date = ql.Date(15, 1, 2015)
         schedule = ql.Schedule(effective_date,
                                termination_date,
                                tenor,
                                calendar,
                                business_convention,
                                termination_business_convention,
                                ql.DateGeneration.Backward,
                                end_of_month,
                                first_date)
         
         pd.DataFrame({'date': list(schedule)})

Out[12]: 
  date
0 January 2nd, 2015
1 January 15th, 2015
2 February 2nd, 2015
3 March 2nd, 2015
4 April 1st, 2015
5 May 1st, 2015
6 June 1st, 2015
7 July 1st, 2015
8 August 3rd, 2015
9 September 1st, 2015
10 October 1st, 2015
11 November 2nd, 2015
12 December 1st, 2015
13 January 4th, 2016

Using the nextToLastDate parameter along with the forward date generation rule creates a short stub at the back end of the schedule.

In [13]: # short stub at the back
         effective_date = ql.Date(1, 1, 2015)
         termination_date = ql.Date(1, 1, 2016)
         penultimate_date = ql.Date(15, 12, 2015)
         schedule = ql.Schedule(effective_date,
                                termination_date,
                                tenor,
                                calendar,
                                business_convention,
                                termination_business_convention,
                                ql.DateGeneration.Forward,
                                end_of_month,
                                ql.Date(),
                                penultimate_date)
         
         pd.DataFrame({'date': list(schedule)})

Out[13]: 
  date
0 January 2nd, 2015
1 February 2nd, 2015
2 March 2nd, 2015
3 April 1st, 2015
4 May 1st, 2015
5 June 1st, 2015
6 July 1st, 2015
7 August 3rd, 2015
8 September 1st, 2015
9 October 1st, 2015
10 November 2nd, 2015
11 December 1st, 2015
12 December 15th, 2015
13 January 4th, 2016

Using the backward generation rule along with the firstDate allows us to create a long stub in the front. Below the first two dates are longer in duration than the rest of the dates.

In [14]: # long stub in the front
         first_date = ql.Date(1, 2, 2015)
         effective_date = ql.Date(15, 12, 2014)
         termination_date = ql.Date(1, 1, 2016)
         schedule = ql.Schedule(effective_date,
                                termination_date,
                                tenor,
                                calendar,
                                business_convention,
                                termination_business_convention,
                                ql.DateGeneration.Backward,
                                end_of_month, 
                                first_date)
         
         pd.DataFrame({'date': list(schedule)})

Out[14]: 
  date
0 December 15th, 2014
1 February 2nd, 2015
2 March 2nd, 2015
3 April 1st, 2015
4 May 1st, 2015
5 June 1st, 2015
6 July 1st, 2015
7 August 3rd, 2015
8 September 1st, 2015
9 October 1st, 2015
10 November 2nd, 2015
11 December 1st, 2015
12 January 4th, 2016

Similarly the usage of nextToLastDate parameter along with forward date generation rule can be used to generate long stub at the back of the schedule.

In [15]: # long stub at the back
         effective_date = ql.Date(1, 1, 2015)
         penultimate_date = ql.Date(1, 12, 2015)
         termination_date = ql.Date(15, 1, 2016)
         schedule = ql.Schedule(effective_date,
                                termination_date,
                                tenor,
                                calendar,
                                business_convention,
                                termination_business_convention,
                                ql.DateGeneration.Forward,
                                end_of_month,
                                ql.Date(),
                                penultimate_date)
         
         pd.DataFrame({'date': list(schedule)})

Out[15]: 
  date
0 January 2nd, 2015
1 February 2nd, 2015
2 March 2nd, 2015
3 April 1st, 2015
4 May 1st, 2015
5 June 1st, 2015
6 July 1st, 2015
7 August 3rd, 2015
8 September 1st, 2015
9 October 1st, 2015
10 November 2nd, 2015
11 December 1st, 2015
12 January 15th, 2016

Below the Schedule is generated from a list of dates.

In [16]: dates = [ql.Date(2,1,2015), ql.Date(2, 2,2015),
                  ql.Date(2,3,2015), ql.Date(1,4,2015),
                  ql.Date(1,5,2015), ql.Date(1,6,2015),
                  ql.Date(1,7,2015), ql.Date(3,8,2015),
                  ql.Date(1,9,2015), ql.Date(1,10,2015),
                  ql.Date(2,11,2015), ql.Date(1,12,2015),
                  ql.Date(4,1,2016)]
         rolling_convention = ql.Following
         
         schedule = ql.Schedule(dates, calendar,
                                rolling_convention)
         
         pd.DataFrame({'date': list(schedule)})

Out[16]: 
  date
0 January 2nd, 2015
1 February 2nd, 2015
2 March 2nd, 2015
3 April 1st, 2015
4 May 1st, 2015
5 June 1st, 2015
6 July 1st, 2015
7 August 3rd, 2015
8 September 1st, 2015
9 October 1st, 2015
10 November 2nd, 2015
11 December 1st, 2015
12 January 4th, 2016
Interest Rate

The InterestRate class can be used to store the interest rate with the compounding type, day count and the frequency of compounding. Below we show how to create an interest rate of 5.0% compounded annually, using Actual/Actual day count convention.

In [17]: annual_rate = 0.05
         day_count = ql.ActualActual()
         compound_type = ql.Compounded
         frequency = ql.Annual
         
         interest_rate = ql.InterestRate(annual_rate, 
                                         day_count, 
                                         compound_type, 
                                         frequency)
         print(interest_rate)

Out[17]: 5.000000 % Actual/Actual (ISDA) Annual compounding

Lets say if you invest a dollar at the interest rate described by interest_rate, the compoundFactor method in the InterestRate object gives you how much your investment will be worth after any period. Below we show that the value returned by compound_factor for 2 years agrees with the expected compounding formula.

In [18]: t = 2.0
         print(interest_rate.compoundFactor(t))
         print((1+annual_rate)*(1.0+annual_rate))

Out[18]: 1.1025
         1.1025

The discountFactor method returns the reciprocal of the compoundFactor method. The discount factor is useful while calculating the present value of future cashflows.

In [19]: print(interest_rate.discountFactor(t))
         print(1.0/interest_rate.compoundFactor(t))

Out[19]: 0.9070294784580498
         0.9070294784580498

A given interest rate can be converted into other compounding types and compounding frequency using the equivalentRate method.

In [20]: new_frequency = ql.Semiannual
         new_interest_rate = interest_rate.equivalentRate(compound_type,
                                                          new_frequency, t)
         print(new_interest_rate)

Out[20]: 4.939015 % Actual/Actual (ISDA) Semiannual compounding

The discount factor for the two InterestRate objects, interest_rate and new_interest_rate are the same, as shown below.

In [21]: print(interest_rate.discountFactor(t))
         print(new_interest_rate.discountFactor(t))

Out[21]: 0.9070294784580498
         0.9070294784580495

The impliedRate method in the InterestRate class takes compound factor to return the implied rate. The impliedRate method is a static method in the InterestRate class and can be used without an instance of InterestRate. Internally the equivalentRate method invokes the impliedRate method in its calculations.

Conclusion

This chapter gave an introduction to the basics of QuantLib. Here we explained the Date, Schedule, Calendar and InterestRate classes.

2. Instruments and pricing engines

In this notebook, I’ll show how instruments and their available engines can monitor changes in their input data.

Setup

To begin, we import the QuantLib module and set up the global evaluation date.

In [1]: import QuantLib as ql

In [2]: today = ql.Date(7, ql.March, 2014)
        ql.Settings.instance().evaluationDate = today
The instrument

As a sample instrument, we’ll take a textbook example: a European option.

Building the option requires only the specification of its contract, so its payoff (it’s a call option with strike at 100) and its exercise, three months from today’s date. Market data will be selected and passed later, depending on the calculation methods.

In [3]: option = ql.EuropeanOption(ql.PlainVanillaPayoff(ql.Option.Call, 100.0),
                                   ql.EuropeanExercise(ql.Date(7, ql.June, 2014)))
First pricing method: analytic Black-Scholes formula

The different pricing methods are implemented as pricing engines holding the required market data. The first we’ll use is the one encapsulating the analytic Black-Scholes formula.

First, we collect the quoted market data. We’ll assume flat risk-free rate and volatility, so they can be expressed by SimpleQuote instances: those model numbers whose value can change and that can notify observers when this happens. The underlying value is at 100, the risk-free value at 1%, and the volatility at 20%.

In [4]: u = ql.SimpleQuote(100.0)
        r = ql.SimpleQuote(0.01)
        sigma = ql.SimpleQuote(0.20)

In order to build the engine, the market data are encapsulated in a Black-Scholes process object. First we build flat curves for the risk-free rate and the volatility…

In [5]: riskFreeCurve = ql.FlatForward(0, ql.TARGET(),
                                       ql.QuoteHandle(r), ql.Actual360())
        volatility = ql.BlackConstantVol(0, ql.TARGET(),
                                         ql.QuoteHandle(sigma), ql.Actual360())

…then we instantiate the process with the underlying value and the curves we just built. The inputs are all stored into handles, so that we could change the quotes and curves used if we wanted. I’ll skip over this for the time being.

In [6]: process = ql.BlackScholesProcess(ql.QuoteHandle(u),
                                         ql.YieldTermStructureHandle(riskFreeCurve),
                                         ql.BlackVolTermStructureHandle(volatility))

Once we have the process, we can finally use it to build the engine…

In [7]: engine = ql.AnalyticEuropeanEngine(process)

…and once we have the engine, we can set it to the option and evaluate the latter.

In [8]: option.setPricingEngine(engine)

In [9]: print(option.NPV())

Out[9]: 4.155543462156206

Depending on the instrument and the engine, we can also ask for other results; in this case, we can ask for Greeks.

In [10]: print(option.delta())
         print(option.gamma())
         print(option.vega())

Out[10]: 0.5302223303784392
         0.03934493301271913
         20.109632428723106
Market changes

As I mentioned, market data are stored in Quote instances and thus can notify the option when any of them changes. We don’t have to do anything explicitly to tell the option to recalculate: once we set a new value to the underlying, we can simply ask the option for its NPV again and we’ll get the updated value.

In [11]: u.setValue(105.0)
         print(option.NPV())

Out[11]: 7.27556357927846

Just for showing off, we can use this to graph the option value depending on the underlying asset value. After a bit of graphic setup (don’t pay attention to the man behind the curtains)…

In [12]: %matplotlib inline
         import numpy as np
         from IPython.display import display
         import utils

…we can take an array of values from 80 to 120, set the underlying value to each of them, collect the corresponding option values, and plot the results.

In [13]: f, ax = utils.plot()
         xs = np.linspace(80.0, 120.0, 400)
         ys = []
         for x in xs:
             u.setValue(x)
             ys.append(option.NPV())
         ax.set_title('Option value')
         utils.highlight_x_axis(ax)
         ax.plot(xs, ys);

Other market data also affect the value, of course.

In [14]: u.setValue(105.0)
         r.setValue(0.01)
         sigma.setValue(0.20)

In [15]: print(option.NPV())

Out[15]: 7.27556357927846

We can see it when we change the risk-free rate…

In [16]: r.setValue(0.03)

In [17]: print(option.NPV())

Out[17]: 7.624029148527754

…or the volatility.

In [18]: sigma.setValue(0.25)

In [19]: print(option.NPV())

Out[19]: 8.531296969971573
Date changes

Just as it does when inputs are modified, the value also changes if we advance the evaluation date. Let’s look first at the value of the option when its underlying is worth 105 and there’s still three months to exercise…

In [20]: u.setValue(105.0)
         r.setValue(0.01)
         sigma.setValue(0.20)
         print(option.NPV())

Out[20]: 7.27556357927846

…and then move to a date two months before exercise.

In [21]: ql.Settings.instance().evaluationDate = ql.Date(7, ql.April, 2014)

Again, we don’t have to do anything explicitly: we just ask the option its value, and as expected it has decreased, as can also be seen by updating the plot.

In [22]: print(option.NPV())

Out[22]: 6.560073820974377

In [23]: ys = []
         for x in xs:
             u.setValue(x)
             ys.append(option.NPV())
         ax.plot(xs, ys, '--')
         display(f)

In the default library configuration, the returned value goes down to 0 when we reach the exercise date.

In [24]: ql.Settings.instance().evaluationDate = ql.Date(7, ql.June, 2014)

In [25]: print(option.NPV())

Out[25]: 0.0
Other pricing methods

The pricing-engine mechanism allows us to use different pricing methods. For comparison, I’ll first set the input data back to what they were previously and output the Black-Scholes price.

In [26]: ql.Settings.instance().evaluationDate = today
         u.setValue(105.0)
         r.setValue(0.01)
         sigma.setValue(0.20)

In [27]: print(option.NPV())

Out[27]: 7.27556357927846

Let’s say that we want to use a Heston model to price the option. What we have to do is to instantiate the corresponding class with the desired inputs…

In [28]: model = ql.HestonModel(
             ql.HestonProcess(
                 ql.YieldTermStructureHandle(riskFreeCurve),
                 ql.YieldTermStructureHandle(ql.FlatForward(0, ql.TARGET(),
                                                            0.0, ql.Actual360())),
                 ql.QuoteHandle(u),
                 0.04, 0.1, 0.01, 0.05, -0.75))

…pass it to the corresponding engine, and set the new engine to the option.

In [29]: engine = ql.AnalyticHestonEngine(model)
         option.setPricingEngine(engine)

Asking the option for its NPV will now return the value according to the new model.

In [30]: print(option.NPV())

Out[30]: 7.295356086978629
Lazy recalculation

One last thing. Up to now, we haven’t really seen evidence of notifications going around. After all, the instrument might just have recalculated its value every time, regardless of notifications. What I’m going to show, instead, is that the option doesn’t just recalculate every time anything changes; it also avoids recalculations when nothing has changed.

We’ll switch to a Monte Carlo engine, which takes a few seconds to run the required simulation.

In [31]: engine = ql.MCEuropeanEngine(process, "PseudoRandom",
                                      timeSteps=20,
                                      requiredSamples=250000)
         option.setPricingEngine(engine)

When we ask for the option value, we have to wait for the calculation to finish…

In [32]: %time print(option.NPV())

Out[32]: 7.2805592569497755
         CPU times: user 1.85 s, sys: 0 ns, total: 1.85 s
         Wall time: 1.83 s

…but a second call to the NPV method will be instantaneous when made before anything changes. In this case, the option didn’t calculate its value; it just returned the result that it cached from the previous call.

In [33]: %time print(option.NPV())

Out[33]: 7.2805592569497755
         CPU times: user 0 ns, sys: 0 ns, total: 0 ns
         Wall time: 1.38 ms

If we change anything (e.g., the underlying value)…

In [34]: u.setValue(104.0)

…the option is notified of the change, and the next call to NPV will again take a while.

In [35]: %time print(option.NPV())

Out[35]: 6.582898868982841
         CPU times: user 1.78 s, sys: 0 ns, total: 1.78 s
         Wall time: 1.77 s

3. EONIA curve bootstrapping

In the next notebooks, I’ll reproduce the results of the paper by F. M. Ametrano and M. Bianchetti, Everything You Always Wanted to Know About Multiple Interest Rate Curve Bootstrapping but Were Afraid to Ask (April 2, 2013). The paper is available at SSRN: http://ssrn.com/abstract=2219548.

In [1]: %matplotlib inline
        import math
        import utils

In [2]: import QuantLib as ql

In [3]: today = ql.Date(11, ql.December, 2012)
        ql.Settings.instance().evaluationDate = today
First try

We start by instantiating helpers for all the rates used in the bootstrapping process, as reported in figure 25 of the paper.

The first three instruments are three 1-day deposit that give us discounting between today and the day after spot. They are modeled by three instances of the DepositRateHelper class with a tenor of 1 day and a number of fixing days going from 0 (for the deposit starting today) to 2 (for the deposit starting on the spot date).

In [4]: helpers = [
            ql.DepositRateHelper(ql.QuoteHandle(ql.SimpleQuote(rate/100)),
                                 ql.Period(1,ql.Days), fixingDays,
                                 ql.TARGET(), ql.Following,
                                 False, ql.Actual360())
            for rate, fixingDays in [(0.04, 0), (0.04, 1), (0.04, 2)]
        ]

Then, we have a series of OIS quotes for the first month. They are modeled by instances of the OISRateHelper class with varying tenors. They also require an instance of the Eonia class, which doesn’t need a forecast curve and can be shared between the helpers.

In [5]: eonia = ql.Eonia()

In [6]: helpers += [
            ql.OISRateHelper(2, ql.Period(*tenor),
                             ql.QuoteHandle(ql.SimpleQuote(rate/100)), eonia)
            for rate, tenor in [(0.070, (1,ql.Weeks)), (0.069, (2,ql.Weeks)),
                                (0.078, (3,ql.Weeks)), (0.074, (1,ql.Months))]
        ]

Next, five OIS forwards on ECB dates. For these, we need to instantiate the DatedOISRateHelper class and specify start and end dates explicitly.

In [7]: helpers += [
            ql.DatedOISRateHelper(start_date, end_date,
                                  ql.QuoteHandle(ql.SimpleQuote(rate/100)), eonia)
            for rate, start_date, end_date in [
                ( 0.046, ql.Date(16,ql.January,2013), ql.Date(13,ql.February,2013)),
                ( 0.016, ql.Date(13,ql.February,2013), ql.Date(13,ql.March,2013)),
                (-0.007, ql.Date(13,ql.March,2013), ql.Date(10,ql.April,2013)),
                (-0.013, ql.Date(10,ql.April,2013), ql.Date(8,ql.May,2013)),
                (-0.014, ql.Date(8,ql.May,2013), ql.Date(12,ql.June,2013))]
        ]

Finally, we add OIS quotes up to 30 years.

In [8]: helpers += [
            ql.OISRateHelper(2, ql.Period(*tenor),
                             ql.QuoteHandle(ql.SimpleQuote(rate/100)), eonia)
            for rate, tenor in [(0.002, (15,ql.Months)), (0.008, (18,ql.Months)),
                                (0.021, (21,ql.Months)), (0.036, (2,ql.Years)),
                                (0.127, (3,ql.Years)), (0.274, (4,ql.Years)),
                                (0.456, (5,ql.Years)), (0.647, (6,ql.Years)),
                                (0.827, (7,ql.Years)), (0.996, (8,ql.Years)),
                                (1.147, (9,ql.Years)), (1.280, (10,ql.Years)),
                                (1.404, (11,ql.Years)), (1.516, (12,ql.Years)),
                                (1.764, (15,ql.Years)), (1.939, (20,ql.Years)),
                                (2.003, (25,ql.Years)), (2.038, (30,ql.Years))]
        ]

The curve is an instance of PiecewiseLogCubicDiscount (corresponding to the PiecewiseYieldCurve<Discount,LogCubic> class in C++; I won’t repeat the argument for this choice made in section 4.5 of the paper). We let the reference date of the curve move with the global evaluation date, by specifying it as 0 days after the latter on the TARGET calendar. The day counter chosen is not of much consequence, as it is only used internally to convert dates into times. Also, we enable extrapolation beyond the maturity of the last helper; that is mostly for convenience as we retrieve rates to plot the curve near its far end.

In [9]: eonia_curve_c = ql.PiecewiseLogCubicDiscount(0, ql.TARGET(),
                                                     helpers, ql.Actual365Fixed())
        eonia_curve_c.enableExtrapolation()

To compare the curve with the one shown in figure 26 of the paper, we can retrieve daily overnight rates over its first two years and plot them:

In [10]: today = eonia_curve_c.referenceDate()
         end = today + ql.Period(2,ql.Years)
         dates = [ ql.Date(serial) for serial in range(today.serialNumber(),
                                                       end.serialNumber()+1) ]
         rates_c = [ eonia_curve_c.forwardRate(d, ql.TARGET().advance(d,1,ql.Days),
                                               ql.Actual360(), ql.Simple).rate()
                     for d in dates ]

In [11]: _, ax = utils.plot()
         utils.highlight_x_axis(ax)
         utils.plot_curve(ax, dates, [(rates_c,'-')], format_rates=True)

However, we still have work to do. Out plot above shows a rather large bump at the end of 2012 which is not present in the paper. To remove it, we need to model properly the turn-of-year effect.

Turn-of-year jumps

As explained in section 4.8 of the paper, the turn-of-year effect is a jump in interest rates due to an increased demand for liquidity at the end of the year. The jump is embedded in any quoted rates that straddles the end of the year and must be treated separately; the YieldTermStructure class allows this by taking any number of jumps, modeled as additional discount factors, and applying them at the specified dates.

Our problem is to estimate the size of the jump. To simplify analysis, we turn to flat forward rates instead of log-cubic discounts; thus, we instantiate a PiecewiseFlatForward curve (corresponding to PiecewiseYieldCurve<ForwardRate,BackwardFlat> in C++).

In [12]: eonia_curve_ff = ql.PiecewiseFlatForward(0, ql.TARGET(),
                                                  helpers, ql.Actual365Fixed())
         eonia_curve_ff.enableExtrapolation()

To show the jump more clearly, I’ll restrict the plot to the first 6 months:

In [13]: end = today + ql.Period(6,ql.Months)
         dates = [ ql.Date(serial) for serial in range(today.serialNumber(),
                                                       end.serialNumber()+1) ]
         rates_ff = [ eonia_curve_ff.forwardRate(d, ql.TARGET().advance(d,1,ql.Days),
                                                 ql.Actual360(), ql.Simple).rate()
                      for d in dates ]

In [14]: _, ax = utils.plot()
         utils.highlight_x_axis(ax)
         utils.plot_curve(ax, dates, [(rates_ff,'-')], format_rates=True)

As we see, the forward ending at the beginning of January 2013 is out of line. In order to estimate the jump, we need to estimate a “clean” forward that doesn’t include it.

A possible estimate (although not the only one) can be obtained by interpolating the forwards around the one we want to replace. To do so, we extract the values of the forwards rates and their corresponding dates.

In [15]: nodes = list(eonia_curve_ff.nodes())

If we look at the first few nodes, we can clearly see that the seventh is out of line.

In [16]: nodes[:9]

Out[16]: [(Date(11,12,2012), 0.00040555533025081675),
          (Date(12,12,2012), 0.00040555533025081675),
          (Date(13,12,2012), 0.00040555533047721286),
          (Date(14,12,2012), 0.00040555533047721286),
          (Date(20,12,2012), 0.0007604110692568178),
          (Date(27,12,2012), 0.0006894305026004767),
          (Date(3,1,2013), 0.0009732981324671213),
          (Date(14,1,2013), 0.0006728161005748453),
          (Date(13,2,2013), 0.000466380545910482)]

To create a curve that doesn’t include the jump, we replace the relevant forward rate with a simple average of the ones that precede and follow…

In [17]: nodes[6] = (nodes[6][0], (nodes[5][1]+nodes[7][1])/2.0)
         nodes[:9]

Out[17]: [(Date(11,12,2012), 0.00040555533025081675),
          (Date(12,12,2012), 0.00040555533025081675),
          (Date(13,12,2012), 0.00040555533047721286),
          (Date(14,12,2012), 0.00040555533047721286),
          (Date(20,12,2012), 0.0007604110692568178),
          (Date(27,12,2012), 0.0006894305026004767),
          (Date(3,1,2013), 0.000681123301587661),
          (Date(14,1,2013), 0.0006728161005748453),
          (Date(13,2,2013), 0.000466380545910482)]

…and instantiate a ForwardCurve with the modified nodes.

In [18]: temp_dates, temp_rates = zip(*nodes)
         temp_curve = ql.ForwardCurve(temp_dates, temp_rates,
                                      eonia_curve_ff.dayCounter())

For illustration, we can extract daily overnight nodes from the doctored curve and plot them alongside the old ones:

In [19]: temp_rates = [ temp_curve.forwardRate(d, ql.TARGET().advance(d,1,ql.Days),
                                               ql.Actual360(), ql.Simple).rate()
                        for d in dates ]

In [20]: _, ax = utils.plot()
         utils.highlight_x_axis(ax)
         utils.plot_curve(ax, dates, [(temp_rates,'-'), (rates_ff,'--')],
                          format_rates=True)

Now we can estimate the size of the jump. As the paper hints, it’s more an art than a science. I’ve been able to reproduce closely the results of the paper by extracting from the two curves the forward rate over the two weeks around the end of the year:

In [21]: d1 = ql.Date(31,ql.December,2012) - ql.Period(1,ql.Weeks)
         d2 = ql.Date(31,ql.December,2012) + ql.Period(1,ql.Weeks)

In [22]: F = eonia_curve_ff.forwardRate(d1, d2, ql.Actual360(), ql.Simple).rate()
         F_1 = temp_curve.forwardRate(d1, d2, ql.Actual360(), ql.Simple).rate()
         print(utils.format_rate(F,digits=3))
         print(utils.format_rate(F_1,digits=3))

Out[22]: 0.082 %
         0.067 %

We want to attribute the whole jump to the last day of the year, so we rescale it according to

$$ (F-F_1) \cdot t_{12} = J \cdot t_J $$

where \(t_{12}\) is the time between the two dates and \(t_J\) is the time between the start and end date of the end-of-year overnight deposit. This gives us a jump quite close to the value of 10.2 basis points reported in the paper.

In [23]: t12 = eonia_curve_ff.dayCounter().yearFraction(d1,d2)
         t_j = eonia_curve_ff.dayCounter().yearFraction(ql.Date(31,ql.December,2012),
                                                        ql.Date(2,ql.January,2013))
         J = (F-F_1)*t12/t_j
         print(utils.format_rate(J,digits=3))

Out[23]: 0.101 %

As I mentioned previously, the jump can be added to the curve as a corresponding discount factor \(1/(1+J \cdot t_J)\) on the last day of the year. The information can be passed to the curve constructor, giving us a new instance:

In [24]: B = 1.0/(1.0+J*t_j)
         jumps = [ql.QuoteHandle(ql.SimpleQuote(B))]
         jump_dates = [ql.Date(31,ql.December,2012)]
         eonia_curve_j = ql.PiecewiseFlatForward(0, ql.TARGET(),
                                                 helpers, ql.Actual365Fixed(),
                                                 jumps, jump_dates)

Retrieving daily overnight rates from the new curve and plotting them, we can see the jump quite clearly:

In [25]: rates_j = [ eonia_curve_j.forwardRate(d, ql.TARGET().advance(d,1,ql.Days),
                                               ql.Actual360(), ql.Simple).rate()
                     for d in dates ]

In [26]: _, ax = utils.plot()
         utils.highlight_x_axis(ax)
         utils.plot_curve(ax, dates, [(rates_ff,'-'), (rates_j,'o')],
                          format_rates=True)

We can now go back to log-cubic discounts and add the jump.

In [27]: eonia_curve = ql.PiecewiseLogCubicDiscount(0, ql.TARGET(),
                                                    helpers, ql.Actual365Fixed(),
                                                    jumps, jump_dates)
         eonia_curve.enableExtrapolation()

In [28]: rates_c = [ eonia_curve_c.forwardRate(d, ql.TARGET().advance(d,1,ql.Days),
                                               ql.Actual360(), ql.Simple).rate()
                     for d in dates ]
         rates = [ eonia_curve.forwardRate(d, ql.TARGET().advance(d,1,ql.Days),
                                           ql.Actual360(), ql.Simple).rate()
                   for d in dates ]

In [29]: _, ax = utils.plot()
         utils.highlight_x_axis(ax)
         utils.plot_curve(ax, dates, [(rates_c,'-'), (rates,'o')],
                          format_rates=True)

As you can see, the large bump is gone now. The two plots in figure 26 can be reproduced as follows (omitting the jump at the end of 2013 for brevity, and the flat forwards for clarity):

In [30]: dates = [ today+ql.Period(i,ql.Days) for i in range(0, 365*2+1) ]
         rates = [ eonia_curve.forwardRate(d, ql.TARGET().advance(d,1,ql.Days),
                                           ql.Actual360(), ql.Simple).rate()
                   for d in dates ]
         _, ax = utils.plot()
         utils.highlight_x_axis(ax)
         utils.plot_curve(ax, dates, [(rates,'.')], ymin=-0.001, ymax=0.002,
                          format_rates=True)
In [31]: dates = [ today+ql.Period(i,ql.Months) for i in range(0, 12*60+1) ]
         rates = [ eonia_curve.forwardRate(d, ql.TARGET().advance(d,1,ql.Days),
                                           ql.Actual360(), ql.Simple).rate()
                   for d in dates ]
         _, ax = utils.plot()
         utils.plot_curve(ax, dates, [(rates,'-')], ymin=0.0, ymax=0.035,
                          format_rates=True)

A final word of warning: as you saw, the estimate of the jumps is not an exact science, so it’s best to check it manually and not to leave it to an automated procedure.

Moreover, jumps nowadays might be present at the end of each month, as reported for instance in Paolo Mazzocchi’s presentation at the QuantLib User Meeting 2014. This, too, suggests particular care in building the Eonia curve.

4. Constructing a yield curve

In this chapter we will go over the construction of treasury yield curve. Let’s start by importing QuantLib and other necessary libraries.

In [1]: import QuantLib as ql
        from pandas import DataFrame
        import numpy as np
        import utils
        %matplotlib inline

This is an example based on Exhibit 5-5 given in Frank Fabozzi’s Bond Markets, Analysis and Strategies, Sixth Edition.

In [2]: depo_maturities = [ql.Period(6,ql.Months), ql.Period(12, ql.Months)]
        depo_rates = [5.25, 5.5]
        
        # Bond rates
        bond_maturities = [ql.Period(6*i, ql.Months) for i in range(3,21)]
        bond_rates = [5.75, 6.0, 6.25, 6.5, 6.75, 6.80, 7.00, 7.1, 7.15, 
                      7.2, 7.3, 7.35, 7.4, 7.5, 7.6, 7.6, 7.7, 7.8]
        
        maturities = depo_maturities+bond_maturities
        rates = depo_rates+bond_rates
        DataFrame(list(zip(maturities, rates)), 
                  columns=["Maturities","Curve"],
                  index=['']*len(rates))

Out[2]: 
  Maturities Curve
  6M 5.25
  1Y 5.50
  1Y6M 5.75
  2Y 6.00
  2Y6M 6.25
  3Y 6.50
  3Y6M 6.75
  4Y 6.80
  4Y6M 7.00
  5Y 7.10
  5Y6M 7.15
  6Y 7.20
  6Y6M 7.30
  7Y 7.35
  7Y6M 7.40
  8Y 7.50
  8Y6M 7.60
  9Y 7.60
  9Y6M 7.70
  10Y 7.80

Below we declare some constants and conventions used here. For the sake of simplicity, we assume that some of the constants are the same for deposit rates and bond rates.

In [3]: calc_date = ql.Date(15, 1, 2015)
        ql.Settings.instance().evaluationDate = calc_date
        
        calendar = ql.UnitedStates()
        business_convention = ql.Unadjusted 
        day_count = ql.Thirty360()
        end_of_month = True
        settlement_days = 0
        face_amount = 100
        coupon_frequency = ql.Period(ql.Semiannual)
        settlement_days = 0

The basic idea of bootstrapping is to use the deposit rates and bond rates to create individual rate helpers. Then use the combination of the two helpers to construct the yield curve. As a first step, we create the deposit rate helpers as shown below.

In [4]: depo_helpers = [
            ql.DepositRateHelper(ql.QuoteHandle(ql.SimpleQuote(r/100.0)),
                                 m,
                                 settlement_days,
                                 calendar,
                                 business_convention,
                                 end_of_month,
                                 day_count)
            for r, m in zip(depo_rates, depo_maturities)
        ]

The rest of the points are coupon bonds. We assume that the YTM given for the bonds are all par rates. So we have bonds with coupon rate same as the YTM. Using this information, we construct the fixed rate bond helpers below.

In [5]: bond_helpers = []
        for r, m in zip(bond_rates, bond_maturities):
            termination_date = calc_date + m
            schedule = ql.Schedule(calc_date,
                                   termination_date, 
                                   coupon_frequency, 
                                   calendar,
                                   business_convention, 
                                   business_convention, 
                                   ql.DateGeneration.Backward, 
                                   end_of_month)
            
            bond_helper = ql.FixedRateBondHelper(
                ql.QuoteHandle(ql.SimpleQuote(face_amount)),
                settlement_days,
                face_amount,
                schedule,
                [r/100.0],
                day_count,
                business_convention)
            bond_helpers.append(bond_helper)

The union of the two helpers is what we use in bootstrapping shown below.

In [6]: rate_helpers = depo_helpers + bond_helpers

The get_spot_rates is a convenient wrapper function that we will use to get the spot rates on a monthly interval.

In [7]: def get_spot_rates(
                yieldcurve, day_count, 
                calendar=ql.UnitedStates(), months=121):
            spots = []
            tenors = []
            ref_date = yieldcurve.referenceDate()
            calc_date = ref_date
            for month in range(0, months):
                yrs = month/12.0
                d = calendar.advance(ref_date, ql.Period(month, ql.Months))
                compounding = ql.Compounded
                freq = ql.Semiannual
                zero_rate = yieldcurve.zeroRate(yrs, compounding, freq)
                tenors.append(yrs)
                eq_rate = zero_rate.equivalentRate(
                    day_count,compounding,freq,calc_date,d).rate()
                spots.append(100*eq_rate)
            return DataFrame(list(zip(tenors, spots)),
                             columns=["Maturities","Curve"],
                             index=['']*len(tenors))

The bootstrapping process is fairly generic in QuantLib. You can chose what variable you are bootstrapping, and what is the interpolation method used in the bootstrapping. There are multiple piecewise interpolation methods that can be used for this process. The PiecewiseLogCubicDiscount will construct a piece wise yield curve using LogCubic interpolation of the Discount factor. Similarly PiecewiseLinearZero will use Linear interpolation of Zero rates. PiecewiseCubicZero will interpolate the Zero rates using a Cubic interpolation method.

In [8]: yc_logcubicdiscount = ql.PiecewiseLogCubicDiscount(calc_date,
                                                           rate_helpers,
                                                           day_count)

The zero rates from the tail end of the PiecewiseLogCubicDiscount bootstrapping is shown below.

In [9]: splcd = get_spot_rates(yc_logcubicdiscount, day_count)
        splcd.tail()

Out[9]: 
  Maturities Curve
  9.666667 7.981384
  9.750000 8.005292
  9.833333 8.028145
  9.916667 8.050187
  10.000000 8.071649

The yield curves using the PiecewiseLinearZero and PiecewiseCubicZero is shown below. The tail end of the zero rates obtained from PiecewiseLinearZero bootstrapping is also shown below. The numbers can be compared with that of the PiecewiseLogCubicDiscount shown above.

In [10]: yc_linearzero = ql.PiecewiseLinearZero(
             calc_date,rate_helpers,day_count
         )
         yc_cubiczero = ql.PiecewiseCubicZero(
             calc_date,rate_helpers,day_count
         )
         
         splz = get_spot_rates(yc_linearzero, day_count)
         spcz = get_spot_rates(yc_cubiczero, day_count)
         splz.tail()

Out[10]: 
  Maturities Curve
  9.666667 7.976804
  9.750000 8.000511
  9.833333 8.024221
  9.916667 8.047934
  10.000000 8.071649

All three are plotted below to give you an overall perspective of the three methods.

In [11]: fig, ax = utils.plot()
         ax.plot(splcd["Maturities"],splcd["Curve"], '.',
                 label="LogCubicDiscount")
         ax.plot(splz["Maturities"],splz["Curve"],'--', 
                 label="LinearZero")
         ax.plot(spcz["Maturities"],spcz["Curve"],
                 label="CubicZero")
         ax.set_xlabel("Months", size=12)
         ax.set_ylabel("Zero Rate", size=12)
         ax.set_xlim(0.5,10)
         ax.set_ylim([5.25,8])
         ax.legend(loc=0);
Conclusion

In this chapter we saw how to construct yield curves by bootstrapping bond quotes.

5. Dangerous day-count conventions

(Based on a question by Min Gao on the QuantLib mailing list. Thanks!)

In [1]: import QuantLib as ql

In [2]: today = ql.Date(22,1,2018)
        ql.Settings.instance().evaluationDate = today

In [3]: %matplotlib inline
        import utils
The problem

Talking about term structures in Implementing QuantLib, I suggest to use simple day-count conventions such as Actual/360 or Actual/365 to initialize curves. That’s because the convention is used internally to convert dates into times, and we want the conversion to be as regular as possible. For instance, we’d like distances between dates to be additive: given three dates \(d_1\), \(d_2\) and \(d_3\), we would expect that \(T(d_1,d_2) + T(d_2,d_3) = T(d_1,d_3)\), where \(T\) denotes the time between dates.

Unfortunately, that’s not always the case for some day counters. The property holds for most dates…

In [4]: dc = ql.Thirty360(ql.Thirty360.USA)

In [5]: d1 = ql.Date(1, ql.January, 2018)
        d2 = ql.Date(15, ql.January, 2018)
        d3 = ql.Date(31, ql.January, 2018)

In [6]: print(dc.yearFraction(d1,d2) + dc.yearFraction(d2,d3))
        print(dc.yearFraction(d1,d3))

Out[6]: 0.08333333333333334
        0.08333333333333333

…but doesn’t for some.

In [7]: d1 = ql.Date(1, ql.January, 2018)
        d2 = ql.Date(30, ql.January, 2018)
        d3 = ql.Date(31, ql.January, 2018)

In [8]: print(dc.yearFraction(d1,d2) + dc.yearFraction(d2,d3))
        print(dc.yearFraction(d1,d3))

Out[8]: 0.08055555555555556
        0.08333333333333333

That’s because some day-count conventions were designed to calculate the duration of a coupon, not the distance between any two given dates. They have particular formulas and exceptions that make coupons more regular; but those exceptions also cause some pairs of dates to have strange properties. For instance, there might be no distance at all between some particular distinct dates:

In [9]: d1 = ql.Date(30, ql.January, 2018)
        d2 = ql.Date(31, ql.January, 2018)
        
        print(dc.yearFraction(d1,d2))

Out[9]: 0.0

The 30/360 convention is not the worst offender, either. Min Gao’s question came from using for the term structure the same convention used for the bond being priced, that is, ISMA actual/actual. This day counter is supposed to be given a reference period, as well as the two dates whose distance one needs to measure; failing to do so will result in the wrong results…

In [10]: d1 = ql.Date(1, ql.January, 2018)
         d2 = ql.Date(15, ql.January, 2018)
         
         reference_period = (ql.Date(1, ql.January, 2018), ql.Date(1, ql.July, 2018))

In [11]: dc = ql.ActualActual(ql.ActualActual.ISMA)
         
         print(dc.yearFraction(d1, d2, *reference_period))
         print(dc.yearFraction(d1, d2))

Out[11]: 0.03867403314917127
         0.038356164383561646

…and sometimes, in spectacularly wrong results. Here is what happens if we plot the year fraction since January 1st, 2018 as a function of the date over that same year.

In [12]: d1 = ql.Date(1, ql.January, 2018)
         dates = [ (d1 + i) for i in range(366) ]
         times = [ dc.yearFraction(d1, d) for d in dates ]

In [13]: fig, ax = utils.plot()
         ax.xaxis.set_major_formatter(utils.date_formatter())
         ax.plot_date([ utils.to_datetime(d) for d in dates ], times,'-');

Of course, that’s no way to convert dates into times. Using this day-count convention inside a coupon is ok, of course. Using it inside a term structure, which doesn’t have any concept of a reference period, leads to very strange behaviors.

In [14]: curve = ql.FlatForward(today, 0.01, ql.ActualActual(ql.ActualActual.ISMA))

In [15]: dates = [ (today + i) for i in range(366) ]
         discounts = [ curve.discount(d) for d in dates ]
         fig, ax = utils.plot()
         ax.xaxis.set_major_formatter(utils.date_formatter())
         ax.plot_date([ utils.to_datetime(d) for d in dates ], discounts,'-');
Any solutions?

Not really, at this time. Work is underway to store a schedule inside an ISMA actual/actual day counter and use it to retrieve the correct reference period, but that’s not fully working yet. In the meantime, what I can suggest is to use the specified day-count conventions for coupons; but, unless something prevents it, use a simple day-count convention such as actual/360 or actual/365 for term structures.

6. Valuing European and American options

I have written about option pricing earlier. The introduction to option pricing gave an overview of the theory behind option pricing. The post on introduction to binomial trees outlined the binomial tree method to price options.

In this post, we will use QuantLib and the Python extension to illustrate a simple example. Here we are going to price a European option using the Black-Scholes-Merton formula. We will price them again using the Binomial tree and understand the agreement between the two.

In [1]: import QuantLib as ql
        import utils
        %matplotlib inline
European Option

Let us consider a European call option for AAPL with a strike price of 130 maturing on 15th Jan, 2016. Let the spot price be 127.62. The volatility of the underlying stock is know to be 20%, and has a dividend yield of 1.63%. Let’s value this option as of 8th May, 2015.

In [2]: maturity_date = ql.Date(15, 1, 2016)
        spot_price = 127.62
        strike_price = 130
        volatility = 0.20 # the historical vols for a year
        dividend_rate =  0.0163
        option_type = ql.Option.Call
        
        risk_free_rate = 0.001
        day_count = ql.Actual365Fixed()
        calendar = ql.UnitedStates()
        
        calculation_date = ql.Date(8, 5, 2015)
        ql.Settings.instance().evaluationDate = calculation_date

We construct the European option here.

In [3]: payoff = ql.PlainVanillaPayoff(option_type, strike_price)
        exercise = ql.EuropeanExercise(maturity_date)
        european_option = ql.VanillaOption(payoff, exercise)

The Black-Scholes-Merton process is constructed here.

In [4]: spot_handle = ql.QuoteHandle(
            ql.SimpleQuote(spot_price)
        )
        flat_ts = ql.YieldTermStructureHandle(
            ql.FlatForward(calculation_date, 
                           risk_free_rate, 
                           day_count)
        )
        dividend_yield = ql.YieldTermStructureHandle(
            ql.FlatForward(calculation_date, 
                           dividend_rate, 
                           day_count)
        )
        flat_vol_ts = ql.BlackVolTermStructureHandle(
            ql.BlackConstantVol(calculation_date, 
                                calendar, 
                                volatility, 
                                day_count)
        )
        bsm_process = ql.BlackScholesMertonProcess(spot_handle, 
                                                   dividend_yield, 
                                                   flat_ts, 
                                                   flat_vol_ts)

Lets compute the theoretical price using the AnalyticEuropeanEngine.

In [5]: european_option.setPricingEngine(ql.AnalyticEuropeanEngine(bsm_process))
        bs_price = european_option.NPV()
        print("The theoretical price is %lf" % bs_price)

Out[5]: The theoretical price is 6.749272

Lets compute the price using the binomial-tree approach.

In [6]: def binomial_price(option, bsm_process, steps):
            binomial_engine = ql.BinomialVanillaEngine(bsm_process, "crr", steps)
            option.setPricingEngine(binomial_engine)
            return option.NPV()
        
        steps = range(2, 200, 1)
        prices = [binomial_price(european_option, bsm_process, step) for step in steps]

In the plot below, we show the convergence of binomial-tree approach by comparing its price with the BSM price.

In [7]: fig, ax = utils.plot()
        ax.plot(steps, prices, label="Binomial Tree Price", lw=2, alpha=0.6)
        ax.plot([0,200],[bs_price, bs_price], "--", label="BSM Price", lw=2, alpha=0.6)
        ax.set_xlabel("Steps", size=14)
        ax.set_ylabel("Price", size=14)
        ax.set_title("Binomial Tree Price For Varying Steps", size=14)
        ax.legend();
American Option

The above exercise was pedagogical, and introduces one to pricing using the binomial tree approach and compared with Black-Scholes. As a next step, we will use the Binomial pricing to value American options.

The construction of an American option is similar to the construction of European option discussed above. The one main difference is the use of AmericanExercise instead of EuropeanExercise use above.

In [8]: payoff = ql.PlainVanillaPayoff(option_type, strike_price)
        settlement = calculation_date
        am_exercise = ql.AmericanExercise(settlement, maturity_date)
        american_option = ql.VanillaOption(payoff, am_exercise)

Once we have constructed the american_option object, we can price them using the Binomial trees as done above. We use the same function we constructed above.

In [9]: steps = range(2, 200, 1)
        prices = [binomial_price(american_option, bsm_process, step) for step in steps]

In [10]: fig, ax = utils.plot()
         ax.plot(steps, prices, label="Binomial Tree Price", lw=2, alpha=0.6)
         ax.plot([0,200],[bs_price, bs_price], "--", label="BSM Price", lw=2, alpha=0.6)
         ax.set_xlabel("Steps", size=14)
         ax.set_ylabel("Price", size=14)
         ax.set_title("Binomial Tree Price For Varying Steps", size=14)
         ax.legend();

Above, we plot the price of the American option as a function of steps used in the binomial tree, and compare with that of the Black-Scholes price for the European option with all other variables remaining the same. The binomial tree converges as the number of steps used in pricing increases. American option is valued more than the European BSM price because of the fact that it can be exercised anytime during the course of the option.

Conclusion

In this chapter we learnt about valuing European and American options using the binomial tree method.

7. Duration of floating-rate bonds

(Based on a question by Antonio Savoldi on the QuantLib mailing list. Thanks!)

In [1]: import QuantLib as ql
        from pandas import DataFrame

In [2]: today = ql.Date(8,ql.October,2014)
        ql.Settings.instance().evaluationDate = today
The problem

We want to calculate the modified duration of a floating-rate bond. First, we need an interest-rate curve to forecast its coupon rates: for illustration’s sake, let’s take a flat curve with a 0.2% rate.

In [3]: forecast_curve = ql.RelinkableYieldTermStructureHandle()
        forecast_curve.linkTo(ql.FlatForward(today, 0.002, ql.Actual360(),
                                             ql.Compounded, ql.Semiannual))

Then, we instantiate the index to be used. The bond has semiannual coupons, so we create a Euribor6M instance and we pass it the forecast curve. Also, we set a past fixing for the current coupon (which, having fixed in the past, can’t be forecast).

In [4]: index = ql.Euribor6M(forecast_curve)
        index.addFixing(ql.Date(6,ql.August,2014), 0.002)

The bond was issued a couple of months before the evaluation date and will run for 5 years with semiannual coupons.

In [5]: issueDate = ql.Date(8,ql.August,2014)
        maturityDate = ql.Date(8,ql.August,2019)
        
        schedule = ql.Schedule(issueDate, maturityDate,
                               ql.Period(ql.Semiannual), ql.TARGET(),
                               ql.Following, ql.Following,
                               ql.DateGeneration.Backward, False)
        
        bond = ql.FloatingRateBond(settlementDays = 3,
                                   faceAmount = 100,
                                   schedule = schedule,
                                   index = index,
                                   paymentDayCounter = ql.Actual360())

The cash flows are calculated based on the forecast curve. Here they are, together with their dates. As expected, they each pay around 0.1% of the notional.

In [6]: dates = [ c.date() for c in bond.cashflows() ]
        cfs = [ c.amount() for c in bond.cashflows() ]
        DataFrame(list(zip(dates, cfs)),
                  columns = ('date','amount'),
                  index = range(1,len(dates)+1))

Out[6]: 
  date amount
1 February 9th, 2015 0.102778
2 August 10th, 2015 0.101112
3 February 8th, 2016 0.101112
4 August 8th, 2016 0.101112
5 February 8th, 2017 0.102223
6 August 8th, 2017 0.100556
7 February 8th, 2018 0.102223
8 August 8th, 2018 0.100556
9 February 8th, 2019 0.102223
10 August 8th, 2019 0.100556
11 August 8th, 2019 100.000000

If we try to use the function provided for calculating bond durations, though, we run into a problem. When we pass it the bond and a 0.2% semiannual yield, the result we get is:

In [7]: y = ql.InterestRate(0.002, ql.Actual360(), ql.Compounded, ql.Semiannual)
        print(ql.BondFunctions.duration(bond, y, ql.Duration.Modified))

Out[7]: 4.8609591731332165

which is about the time to maturity. Shouldn’t we get the time to next coupon instead?

What happened?

The function above is too generic. It calculates the modified duration as \(\displaystyle{-\frac{1}{P}\frac{dP}{dy}}\); however, it doesn’t know what kind of bond it has been passed and what kind of cash flows are paid, so it can only consider the yield for discounting and not for forecasting. If you looked into the C++ code, you’d see that the bond price \(P\) above is calculated as the sum of the discounted cash flows, as in the following:

In [8]: y = ql.SimpleQuote(0.002)
        yield_curve = ql.FlatForward(bond.settlementDate(), ql.QuoteHandle(y),
                                     ql.Actual360(), ql.Compounded, ql.Semiannual)
        
        dates = [ c.date() for c in bond.cashflows() ]
        cfs = [ c.amount() for c in bond.cashflows() ]
        discounts = [ yield_curve.discount(d) for d in dates ]
        P = sum(cf*b for cf,b in zip(cfs,discounts))
        
        print(P)

Out[8]: 100.03665363580889

(Incidentally, we can see that this matches the calculation in the dirtyPrice method of the Bond class.)

In [9]: bond.setPricingEngine(
            ql.DiscountingBondEngine(ql.YieldTermStructureHandle(yield_curve)))
        print(bond.dirtyPrice())

Out[9]: 100.03665363580889

Finally, the derivative \(\displaystyle{\frac{dP}{dy}}\) in the duration formula in approximated as \(\displaystyle{\frac{P(y+dy)-P(y-dy)}{2 dy}}\), so that we get:

In [10]: dy = 1e-5
         
         y.setValue(0.002 + dy)
         cfs_p = [ c.amount() for c in bond.cashflows() ]
         discounts_p = [ yield_curve.discount(d) for d in dates ]
         P_p = sum(cf*b for cf,b in zip(cfs_p,discounts_p))
         print(P_p)
         
         y.setValue(0.002 - dy)
         cfs_m = [ c.amount() for c in bond.cashflows() ]
         discounts_m = [ yield_curve.discount(d) for d in dates ]
         P_m = sum(cf*b for cf,b in zip(cfs_m,discounts_m))
         print(P_m)
         
         y.setValue(0.002)

Out[10]: 100.03179102561501
         100.0415165074028

In [11]: print(-(1/P)*(P_p - P_m)/(2*dy))

Out[11]: 4.8609591756253225

which is the same figure returned by BondFunctions.duration.

The problem is that the above doesn’t use the yield curve for forecasting, so it’s not really considering the bond as a floating-rate bond. It’s using it as a fixed-rate bond, whose coupon rates happen to equal the current forecasts for the Euribor 6M fixings. This is clear if we look at the coupon amounts and discounts we stored during the calculation:

In [12]: DataFrame(list(zip(dates, cfs, discounts,
                            cfs_p, discounts_p, cfs_m, discounts_m)),
                   columns = ('date','amount','discounts',
                              'amount (+)','discounts (+)',
                              'amount (-)','discounts (-)',),
                   index = range(1,len(dates)+1))

Out[12]: 
  date amount discounts amount (+) discounts (+) amount (-) discounts (-)
1 February 9th, 2015 0.102778 0.999339 0.102778 0.999336 0.102778 0.999343
2 August 10th, 2015 0.101112 0.998330 0.101112 0.998322 0.101112 0.998338
3 February 8th, 2016 0.101112 0.997322 0.101112 0.997308 0.101112 0.997335
4 August 8th, 2016 0.101112 0.996314 0.101112 0.996296 0.101112 0.996333
5 February 8th, 2017 0.102223 0.995297 0.102223 0.995273 0.102223 0.995320
6 August 8th, 2017 0.100556 0.994297 0.100556 0.994269 0.100556 0.994325
7 February 8th, 2018 0.102223 0.993282 0.102223 0.993248 0.102223 0.993315
8 August 8th, 2018 0.100556 0.992284 0.100556 0.992245 0.100556 0.992322
9 February 8th, 2019 0.102223 0.991270 0.102223 0.991227 0.102223 0.991314
10 August 8th, 2019 0.100556 0.990275 0.100556 0.990226 0.100556 0.990323
11 August 8th, 2019 100.000000 0.990275 100.000000 0.990226 100.000000 0.990323

where you can see how the discount factors changed when the yield was modified, but the coupon amounts stayed the same.

The solution

Unfortunately, there’s no easy way to fix the BondFunctions.duration method so that it does the right thing. What we can do, instead, is to repeat the calculation above while setting up the bond and the curves so that the yield is used correctly. In particular, we have to link the forecast curve to the flat yield curve being modified…

In [13]: forecast_curve.linkTo(yield_curve)

…so that changing the yield will also affect the forecast rate of the coupons.

In [14]: y.setValue(0.002 + dy)
         P_p = bond.dirtyPrice()
         cfs_p = [ c.amount() for c in bond.cashflows() ]
         discounts_p = [ yield_curve.discount(d) for d in dates ]
         print(P_p)
         
         y.setValue(0.002 - dy)
         P_m = bond.dirtyPrice()
         cfs_m = [ c.amount() for c in bond.cashflows() ]
         discounts_m = [ yield_curve.discount(d) for d in dates ]
         print(P_m)
         
         y.setValue(0.002)

Out[14]: 100.03632329080955
         100.03698398354918

Now the coupon amounts change with the yield (except, of course, the first coupon, whose amount was already fixed)…

In [15]: DataFrame(list(zip(dates, cfs, discounts, cfs_p,
                            discounts_p, cfs_m, discounts_m)),
                   columns = ('date','amount','discounts',
                              'amount (+)','discounts (+)',
                              'amount (-)','discounts (-)',),
                   index = range(1,len(dates)+1))

Out[15]: 
  date amount discounts amount (+) discounts (+) amount (-) discounts (-)
1 February 9th, 2015 0.102778 0.999339 0.102778 0.999336 0.102778 0.999343
2 August 10th, 2015 0.101112 0.998330 0.101617 0.998322 0.100606 0.998338
3 February 8th, 2016 0.101112 0.997322 0.101617 0.997308 0.100606 0.997335
4 August 8th, 2016 0.101112 0.996314 0.101617 0.996296 0.100606 0.996333
5 February 8th, 2017 0.102223 0.995297 0.102734 0.995273 0.101712 0.995320
6 August 8th, 2017 0.100556 0.994297 0.101059 0.994269 0.100053 0.994325
7 February 8th, 2018 0.102223 0.993282 0.102734 0.993248 0.101712 0.993315
8 August 8th, 2018 0.100556 0.992284 0.101059 0.992245 0.100053 0.992322
9 February 8th, 2019 0.102223 0.991270 0.102734 0.991227 0.101712 0.991314
10 August 8th, 2019 0.100556 0.990275 0.101059 0.990226 0.100053 0.990323
11 August 8th, 2019 100.000000 0.990275 100.000000 0.990226 100.000000 0.990323

…and the duration is calculated correctly, thus approximating the four months to the next coupon.

In [16]: print(-(1/P)*(P_p - P_m)/(2*dy))

Out[16]: 0.33022533022465994

This also holds if the discounting curve is dependent, but not the same as the forecast curve; e.g., as in the case of an added credit spread:

In [17]: discount_curve = ql.ZeroSpreadedTermStructure(
             forecast_curve,
             ql.QuoteHandle(ql.SimpleQuote(0.001)))
         bond.setPricingEngine(
             ql.DiscountingBondEngine(ql.YieldTermStructureHandle(discount_curve)))

This causes the price to decrease due to the increased discount factors…

In [18]: P = bond.dirtyPrice()
         cfs = [ c.amount() for c in bond.cashflows() ]
         discounts = [ discount_curve.discount(d) for d in dates ]
         print(P)

Out[18]: 99.55107926688962

…but the coupon amounts are still the same.

In [19]: DataFrame(list(zip(dates, cfs, discounts)),
                   columns = ('date','amount','discount'),
                   index = range(1,len(dates)+1))

Out[19]: 
  date amount discount
1 February 9th, 2015 0.102778 0.999009
2 August 10th, 2015 0.101112 0.997496
3 February 8th, 2016 0.101112 0.995984
4 August 8th, 2016 0.101112 0.994475
5 February 8th, 2017 0.102223 0.992952
6 August 8th, 2017 0.100556 0.991456
7 February 8th, 2018 0.102223 0.989938
8 August 8th, 2018 0.100556 0.988446
9 February 8th, 2019 0.102223 0.986932
10 August 8th, 2019 0.100556 0.985445
11 August 8th, 2019 100.000000 0.985445

The price derivative is calculated in the same way as above…

In [20]: y.setValue(0.002 + dy)
         P_p = bond.dirtyPrice()
         print(P_p)
         
         y.setValue(0.002 - dy)
         P_m = bond.dirtyPrice()
         print(P_m)
         
         y.setValue(0.002)

Out[20]: 99.55075966035385
         99.55139887578544

In [21]: print(-(1/P)*(P_p - P_m)/(2*dy))

Out[21]: 0.3210489711903113

…and yields a similar result.

Translating QuantLib Python examples to C++

It’s easy enough to translate the Python code shown in this book into the corresponding C++ code. As an example, I’ll go through a bit of code from the notebook on instruments and pricing engines.

In [1]: import QuantLib as ql

This line imports the QuantLib module and provides a shorter alias that can be used to qualify the classes and functions it contains. The C++ equivalent would be:

#include <ql/quantlib.hpp>

namespace ql = QuantLib;

In C++, however, I wouldn’t include the global quantlib.hpp header, which would inflate your compilation times; instead, you can include the specific headers for the classes you’ll use.

Moreover, in C++ is not as discouraged as in Python to import the whole contents of a namespace in a source file, that is, to use

using namespace QuantLib;

However, the above should not be used in header files; ask the nearest C++ guru if you’re not sure why.

In [2]: today = ql.Date(7, ql.March, 2014)
        ql.Settings.instance().evaluationDate = today

The code above has a couple of caveats. The first line is easy enough to translate; you’ll have to declare the type to the variable (or use auto if you’re compiling in C++11 mode). The second line is trickier. To begin with, the syntax to call static methods differs in Python and C++, so you’ll have to replace the dot before instance by a double colon (the same goes for the namespace qualifications). Then, evaluationDate is a property in Python but a method in C++; it was changed in the Python module to be more idiomatic, since it’s not that usual in Python to assign to the result of a method. Luckily, you won’t find many such cases. The translated code is:

ql::Date today(7, ql::March, 2014);
ql::Settings::instance().evaluationDate() = today;

Next:

In [3]: option = ql.EuropeanOption(ql.PlainVanillaPayoff(ql.Option.Call, 100.0),
                                   ql.EuropeanExercise(ql.Date(7, ql.June, 2014)))

Again, you’ll have to declare the type of the variable. Furthermore, the constructor of EuropeanOption takes its arguments by pointer, or more precisely, by boost::shared_ptr. This is hidden in Python, since there’s no concept of pointer in the language; the SWIG wrappers take care of exporting boost::shared_ptr<T> simply as T. The corresponding C++ code (note also the double colon in Option::Call):

ql::EuropeanOption option(
    boost::make_shared<ql::PlainVanillaPayoff>(ql::Option::Call, 100.0),
    boost::make_shared<ql::EuropeanExercise(ql::Date(7, ql::June, 2014)));

(A note: in the remainder of the example, I’ll omit the boost:: and ql:: namespaces for brevity.)

In [4]: u = ql.SimpleQuote(100.0)
        r = ql.SimpleQuote(0.01)
        sigma = ql.SimpleQuote(0.20)

Quotes, too, are stored and passed around as shared_ptr instances; this is the case for most polymorphic classes (when in doubt, you can look at the C++ headers and check the signatures of the functions you want to call). The above becomes:

shared_ptr<SimpleQuote> u = make_shared<SimpleQuote>(100.0);
shared_ptr<SimpleQuote> r = make_shared<SimpleQuote>(0.01);
shared_ptr<SimpleQuote> sigma = make_shared<SimpleQuote>(0.20);

Depending on what you need to do with them, the variables might also be declared as shared_ptr<Quote>. I used the above, since I’ll need to call a method of SimpleQuote in a later part of the code.

In [5]: riskFreeCurve = ql.FlatForward(0, ql.TARGET(),
                                       ql.QuoteHandle(r), ql.Actual360())
        volatility = ql.BlackConstantVol(0, ql.TARGET(),
                                         ql.QuoteHandle(sigma), ql.Actual360())

The Handle template class couldn’t be exported as such, because Python doesn’t have templates. Thus, the SWIG wrappers have to declare separate classes QuoteHandle, YieldTermStructureHandle and so on. In C++, you can go back to the original syntax.

shared_ptr<YieldTermStructure> riskFreeCurve =
    make_shared<FlatForward>(0, TARGET(),
                             Handle<Quote>(r), Actual360());
shared_ptr<BlackVolTermStructure> volatility =
    make_shared<BlackConstantVol>(0, TARGET(),
                                  Handle<Quote>(sigma), Actual360());

Next,

In [6]: process = ql.BlackScholesProcess(ql.QuoteHandle(u),
                                         ql.YieldTermStructureHandle(riskFreeCurve),
                                         ql.BlackVolTermStructureHandle(volatility))

turns into

shared_ptr<BlackScholesProcess> process =
    make_shared<BlackConstantVol>(
        Handle<Quote>(u),
        Handle<YieldTermStructure>(riskFreeCurve),
        Handle<BlackVolTermStructure>(volatility));

and

In [7]: engine = ql.AnalyticEuropeanEngine(process)

into

shared_ptr<PricingEngine> engine =
    make_shared<AnalyticEuropeanEngine>(process);

So far, we’ve been calling constructors. Method invocation works the same in Python and C++, except that in C++ we might be calling methods through a pointer. Therefore,

In [8]: option.setPricingEngine(engine)

In [9]: print(option.NPV())

In [10]: print(option.delta())
         print(option.gamma())
         print(option.vega())

where option is an object in C++, becomes

option.setPricingEngine(engine);

cout << option.NPV() << endl;

cout << option.delta() << endl;
cout << option.gamma() << endl;
cout << option.vega() << endl;

whereas

In [11]: u.setValue(105.0)

since u is a (smart) pointer, turns into

u->setValue(105.0);

Of course, the direct translation I’ve been doing only applies to the QuantLib code; I’m not able to point you to libraries that replace the graphing functionality in matplotlib, or the data-analysis facilities in pandas, or the parallel math functions in numpy. However, I hope that the above can still enable you to extract value from this cookbook, even if you’re programming in C++.