Pricing Call Options with QuantLib Across DataFrame Rows

by

What will you learn?

In this tutorial, you will learn how to efficiently price call options row-wise within a pandas DataFrame using QuantLib. Explore the seamless integration of financial models across data collections, enhancing your understanding of quantitative finance and Python programming.

Introduction to the Problem and Solution

QuantLib stands as a robust library in quantitative finance, extensively utilized for pricing diverse financial instruments, including options. In financial scenarios, there is often a need to assess multiple situations concurrently, such as pricing call options based on varying underlying assets or strike prices. Python’s pandas library plays a pivotal role in organizing these scenarios within a structured DataFrame.

The solution presented here involves iterating over rows in a pandas DataFrame and applying QuantLib’s option pricing models to each row. This tutorial elucidates setting up your environment, structuring the data effectively, and utilizing QuantLib functions proficiently within this framework. By adopting this approach, not only does it enhance code readability but also significantly boosts computational efficiency when handling extensive datasets.

Code

import pandas as pd
from QuantLib import *

# Sample DataFrame setup
data = {
    'OptionType': ['Call', 'Call'],
    'Maturity': ['2023-12-31', '2024-12-31'],
    'StrikePrice': [100, 110],
    'UnderlyingPrice': [105, 115],
    'DividendYield': [0.02, 0.02],
    'RiskFreeRate': [0.05, 0.05],
    'Volatility': [0.2, 0.25]
}

options_df = pd.DataFrame(data)

def price_option(row):
    maturity_date = Date.parseISO(row['Maturity'])
    settlement_date = today()

    # Setting evaluation date
    Settings.instance().evaluationDate = settlement_date

    payoff = PlainVanillaPayoff(Option.Call if row['OptionType'] == "Call" else Option.Put,
                                row['StrikePrice'])

    exercise = EuropeanExercise(maturity_date)

    spot_handle = QuoteHandle(SimpleQuote(row['UnderlyingPrice']))

    dividend_yield_handle = YieldTermStructureHandle(FlatForward(settlement_date,
                                                                  row['DividendYield'],
                                                                  Actual365Fixed()))

    risk_free_rate_handle = YieldTermStructureHandle(FlatForward(settlement_date,
                                                                  row['RiskFreeRate'],
                                                                  Actual365Fixed()))

    vol_handle = BlackVolTermStructureHandle(BlackConstantVol(settlement_date,
                                                               TARGET(),
                                                               row['Volatility'],
                                                               Actual365Fixed()))


# Copyright PHD

Explanation

In the provided code snippet:

  1. DataFrame Creation: Begin by creating a pandas DataFrame named options_df, housing columns like OptionType, Maturity, etc., representing distinct characteristics of call options.

  2. Function Definition: The function price_option is crafted to take each row from our DataFrame as input and compute the option price utilizing QuantLib’s functionalities.

    • Initially converts string dates into QuantLib’s Date objects.
    • Sets the evaluation date (current date) using Settings.instance().evaluationDate.
    • Creates an appropriate payoff object based on the option type (Call or Put).
    • Instantiates essential handles (spot_handle, dividend_yield_handle, etc.) required by the Black-Scholes model.

This framework facilitates iterating over each row of our DataFrame and effortlessly applying complex quantitative models.

  1. How does QuantLib determine option prices?

  2. QuantLib utilizes mathematical models like Black-Scholes for European options (and others for American and exotic options) that consider factors such as volatility, interest rates, dividends, and time-to-expiry.

  3. Can we use other types of options besides calls?

  4. Absolutely! By adjusting the payoff parameter in our custom function (price_option) from PlainVanillaPayoff(Option.Call…) to another value like Option.Put…, you can calculate prices for put options or even more intricate derivatives.

  5. Is it possible to parallelize this computation?

  6. Certainly! For larger datasets where performance becomes crucial, you could leverage Python�s multiprocessing capabilities or utilize libraries like Dask designed for efficient parallel computing on substantial datasets.

  7. How do I install QuantLib-Python?

  8. You can install it via pip: simply execute pip install Quantlib-Python. Note that having C++ dependencies might necessitate additional steps based on your operating system.

Conclusion

By harnessing Python’s sophisticated libraries like Pandas alongside potent quantitative tools such as Quantlib opens avenues for detailed financial analysis directly accessible through scripting languages without relying on specialized software packages�making tasks like pricing a collection of call options both achievable and efficient.

Leave a Comment