Python for Finance: Analyze Big Financial Data

Further Reading

The major references used in this chapter are:

Black, Fischer and Myron Scholes (1973): “The Pricing of Options and Corporate

Liabilities.” Journal of Political Economy, Vol. 81, No. 3, pp. 638-659.

Hilpisch, Yves (2015): Derivatives Analytics with Python. Wiley Finance, Chichester,

England. http://www.derivatives-analytics-with-python.com.

Hilpisch, Yves (2013): “Efficient Data and Financial Analytics with Python.”

Software Developer’s Journal, No. 13, pp. 56-65.

http://hilpisch.com/YH_Efficient_Analytics_Article.pdf.

Merton, Robert (1973): “Theory of Rational Option Pricing.” Bell Journal of

Economics and Management Science, Vol. 4, pp. 141-183.

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Chapter 19 also deals with options based on the VSTOXX volatility index; it calibrates an option pricing model to

market quotes and values American, nontraded options given the calibrated model.

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As we are only considering a single day’s worth of futures and options quotes, the MATURITY column of the

futures_data object would have delivered the information a bit more easily since there are no duplicates.

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Note that you can always look up attributes and methods of unknown objects by using the Python built-in function

dir, like with dir(group_data).

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Although not needed here, all approaches store complete simulation paths in-memory. For the valuation of standard

European options this is not necessary, as the corresponding example in Chapter 1 shows. However, for the valuation of

American options or for certain risk management purposes, whole paths are needed.

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These Monte Carlo examples and implementation approaches also appear in the article Hilpisch (2013).

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For details, refer to the book by Hilpisch (2015).