Why use Python for Data Science?

You can use several different languages for data science, but Python is one of the most popular. Nearly any language is capable of analyzing data, but some languages and libraries are designed with certain expectations; for instance, the NumPy library provides tools for processing matrices so that you don’t have to write a matrix library on your own.

Python, as a language, has a few advantages over many others. First, it is famous for being relatively easy to read. While Python code may not make sense to someone completely unfamiliar with computer programming, it tends to be easier to parse than, say C or C++. That means Python is easier for other people to reuse, because they can read your code and understand what it claims to do, and they may even be able to add to it. Furthermore, Python has several strong purpose-built libraries geared specifically toward data science. Because existing Python data science libraries already provide many of the things data scientists often need to do, Python has earned a rightful place as a leading language in the field.

All other benefits of Python apply, such as the convenience of the pip package manager, the robust venv virtual environment interface, an interactive shell, and so on.

Continue reading “Why use Python for Data Science?”

Data science

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data,[1][2] and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data.Data science is a “concept to unify statistics, data analysis, informatics, and their related methods” in order to “understand and analyze actual phenomena” with data.[3] It uses techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, information science, and domain knowledge. Turing Award winner Jim Gray imagined data science as a “fourth paradigm” of science (empirical, theoretical, computational, and now data-driven) and asserted that “everything about science is changing because of the impact of information technology” and the data deluge.[4][5] Continue reading “Data science”

Lattice model (finance)

Binomial Lattice with CRR formulae

In finance, a lattice model[1] is a technique applied to the valuation of derivatives, where a discrete time model is required. For equity options, a typical example would be pricing an American option, where a decision as to option exercise is required at “all” times (any time) before and including maturity. A continuous model, on the other hand, such as Black–Scholes, would only allow for the valuation of European options, where exercise is on the option’s maturity date. For interest rate derivatives lattices are additionally useful in that they address many of the issues encountered with continuous models, such as pull to par.[2] The method is also used for valuing certain exotic options, where because of path dependence in the payoff, Monte Carlo methods for option pricing fail to account for optimal decisions to terminate the derivative by early exercise,[3] though methods now exist for solving this problem. Continue reading “Lattice model (finance)”

Asset pricing

Asset pricing models
Regime
Asset class
Equilibrium
pricing
Risk neutral
pricing
Equities(and foreign exchange and commodities; interest rates for risk neutral pricing)
  • Capital asset pricing model
  • Consumption-based CAPM
  • Intertemporal CAPM
  • Single-index model
  • Multiple factor models
    • Fama–French three-factor model
    • Carhart four-factor model
  • Arbitrage pricing theory
  • Black–Scholes
  • Black
  • Garman–Kohlhagen
  • Heston
  • CEV
  • SABR
Bonds, other interest rate instruments
  • Vasicek
  • Rendleman–Bartter
  • Cox–Ingersoll–Ross
  • Ho–Lee
  • Hull–White
  • Black–Derman–Toy
  • Black–Karasinski
  • Kalotay–Williams–Fabozzi
  • Longstaff–Schwartz
  • Chen
  • Rendleman–Bartter
  • Heath–Jarrow–Morton
    • Cheyette
  • Brace–Gatarek–Musiela
  • LIBOR market model
This article is theory focused: for the corporate finance usage see Valuation (finance); for the valuation of derivatives and interest rate / fixed income instruments see Mathematical finance.

In financial economics, asset pricing refers to a formal treatment and development of two main pricing principles,[1] outlined below, together with the resultant models. There have been many models developed for different situations, but correspondingly, these stem from general equilibrium asset pricing or rational asset pricing,[2] the latter corresponding to risk neutral pricing. Continue reading “Asset pricing”

Euro short-term rate (€STR) (Ofer Abarbanel online library)

Euro short-term rate (€STR) is a reference rate for the currency euro. The €STR is calculated by the European Central Bank (ECB) and is based on the money market statistical reporting of the Eurosystem. The working group on euro risk-free rates has recommended €STR as a replacement for the EMMI Euro Overnight Index Average (EONIA) as the Euro risk-free rate for all products and contracts. Continue reading “Euro short-term rate (€STR) (Ofer Abarbanel online library)”