By Yves Hilpisch
Leverage Python for expert-level volatility and variance by-product trading
Listed Volatility and Variance Derivatives is a complete remedy of all facets of those more and more renowned derivatives items, and has the excellence of being either the 1st to hide eu volatility and variance items supplied by means of Eurex and the 1st to provide Python code for imposing finished quantitative analyses of those monetary items. when you are looking to start immediately, the ebook is followed by way of a committed website and a Github repository that incorporates the entire code from the booklet for simple replication and use, in addition to a hosted model of all of the code for instant execution.
Python is quick making inroads into monetary modelling and derivatives analytics, and up to date advancements let Python to be as quick as natural C++ or C whereas consisting in most cases of purely 10% of the code traces linked to the compiled languages. this whole advisor deals infrequent perception into using Python to adopt advanced quantitative analyses of indexed volatility and variance derivatives.
- Learn easy methods to use Python for facts and monetary research, and reproduce stylised evidence on volatility and variance markets
- Gain an figuring out of the elemental options of modelling volatility and variance and the model-free replication of variance
- Familiarise your self with micro constitution parts of the markets for indexed volatility and variance derivatives
- Reproduce all effects and photos with IPython/Jupyter Notebooks and Python codes that accompany the book
Listed Volatility and Variance Derivatives is the whole consultant to Python-based quantitative research of those Eurex derivatives products.
Read or Download Listed Volatility and Variance Derivatives: A Python-based Guide PDF
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Extra resources for Listed Volatility and Variance Derivatives: A Python-based Guide
Org). org). The constructor function to be used is HDFStore(). h5 /df frame Data retrieval is as simple as writing. 3 Financial Analytics Examples When it comes to financial data, there are useful data retrieval functions available that wrap both the Yahoo! Finance and Google Finance financial data APIs. 6 KB Let us combine the respective Close columns into a single DataFrame object. There are multiple ways to accomplish this goal. 4 KB Introduction to Python 49 In a case like this, the approach via dictionary objects might be the best and most intuitive way.
In other words, these options span the space of twice continuously differentiable payoffs. 3 LOG CONTRACTS So-called log contracts are a kind of derivative instrument that plays an important role in the valuation of variance swaps. Recall that a long position in a variance contract pays at maturity the difference between the realized variance over the life time of the swap and an up-front fixed variance strike. S Consider the payoff g(ST ) = log FT = log ST − log F. Then g′ (ST ) = 1 ST g′′ (ST ) = − 1 ST2 Valuing this contract by making use of the option spanning approach yields ( ) F ∞ S | 1 1 F E log T || St = log + − 2 P(St , K, T)dK + − 2 C(St , K, T)dK ∫F F| F ∫0 K K F ∞ dK dK P(St , K, T) 2 − C(St , K, T) 2 =− ∫ ∫0 K K F As a consequence, the log contract can be replicated by (infinite strips of) European put and call options on the underlying.
710605 pandas is well integrated with the matplotlib library which makes it really convenient to plot data stored in DataFrame objects. 6). 6 Line plot from pandas DataFrame. 7 Histograms from pandas DataFrame. 7). In both cases, pandas takes care of the handling of the single columns and automatically generates single lines (with respective legend entries) and generates respective sub-plots with three different histograms. 2 Input-Output Operations Another strength of pandas is the exporting and importing of data to and from diverse data storage formats.