Python for Finance: Analyze Big Financial Data
elle
(Elle)
#1
Further Reading
As for the preceding chapters on the DX derivatives analytics library, Glasserman (2004) is
a comprehensive resource for Monte Carlo simulation in the context of financial
engineering and applications. Hilpisch (2015) also provides Python-based
implementations of the most important Monte Carlo algorithms:
Glasserman, Paul (2004): Monte Carlo Methods in Financial Engineering. Springer,
New York.
Hilpisch, Yves (2015): Derivatives Analytics with Python. Wiley Finance, Chichester,
However, there is hardly any research available when it comes to the valuation of
(complex) portfolios of derivatives in a consistent, nonredundant fashion by Monte Carlo
simulation. A notable exception, at least from a conceptual point of view, is the brief
article by Albanese, Gimonet, and White (2010a). A bit more detailed is the white paper
by the same team of authors:
Albanese, Claudio, Guillaume Gimonet, and Steve White (2010a): “Towards a
Albanese, Claudio, Guillaume Gimonet, and Steve White (2010b): “Global Valuation
and Dynamic Risk Management.”
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In practice, the approach we choose here is sometimes called global valuation instead of instrument-specific
valuation. Cf. the article by Albanese, Gimonet, and White (2010a) in Risk Magazine.
