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Python for Finance

Python for Finance

By : Yuxing Yan
3.9 (22)
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Python for Finance

Python for Finance

3.9 (22)
By: Yuxing Yan

Overview of this book

A hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python. Python for Finance is perfect for graduate students, practitioners, and application developers who wish to learn how to utilize Python to handle their financial needs. Basic knowledge of Python will be helpful but knowledge of programming is necessary.
Table of Contents (14 chapters)
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13
Index

Summary

In this chapter, we introduced different types of loops. Then, we demonstrated how to estimate the implied volatility based on a European option (Black-Scholes-Merton option model) and on an American option. We discussed the for loop and the while loop, and their applications. For a given set of input values, such as current stock price, the exercise price, the time to maturity, the continuously compounded risk-free rate, and a call price (or put price), we showed how to estimate a stock's implied volatility. In terms of efficiency, we explained the binary search method and compared it with other approaches when estimating an implied volatility. In addition, we demonstrated how to download option data, such as put-call ratio, from Yahoo! Finance and the CBOE web page.

In the next chapter, we will focus on applications of Monte Carlo simulations on option pricing. Using random numbers drawn from a normal distribution, we could mimic the movements of a stock for a given set of...

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