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Python for Finance

You're reading from   Python for Finance If your interest is finance and trading, then using Python to build a financial calculator makes absolute sense. As does this book which is a hands-on guide covering everything from option theory to time series.

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Product type Paperback
Published in Apr 2014
Publisher
ISBN-13 9781783284375
Length 408 pages
Edition 1st Edition
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Author (1):
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Yuxing Yan Yuxing Yan
Author Profile Icon Yuxing Yan
Yuxing Yan
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Table of Contents (14) Chapters Close

Preface 1. Introduction and Installation of Python 2. Using Python as an Ordinary Calculator FREE CHAPTER 3. Using Python as a Financial Calculator 4. 13 Lines of Python to Price a Call Option 5. Introduction to Modules 6. Introduction to NumPy and SciPy 7. Visual Finance via Matplotlib 8. Statistical Analysis of Time Series 9. The Black-Scholes-Merton Option Model 10. Python Loops and Implied Volatility 11. Monte Carlo Simulation and Options 12. Volatility Measures and GARCH Index

Using the Sobol sequence to improve the efficiency

When applying the Monte Carlo simulation to solve various finance related problems, we need to generate a certain number of random numbers. When the accuracy is very high, we have to draw a huge amount of such random numbers. For example, when pricing options, we use very small interval or a large number of steps to increase the number of decimal places of our final option prices. Thus, the efficiency of our Monte Carlo simulation would be a vital issue in terms of computational time and costs. This is especially true if we have a thousand options to price. One way to increase the efficiency is to apply a correct or better algorithm, that is, optimize our code. Another way is to use some special types of random number generators, such as the Sobol sequence.

Sobol sequences belong to the so-called low-discrepancy sequences, which satisfy the properties of random numbers but are distributed more evenly. Thus, they are usually called quasi...

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