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Financial modeling is the task of building an abstract representation (a model) of a real world financial situation. [1] This is a mathematical model designed to represent (a simplified version of) the performance of a financial asset or portfolio of a business, project , or any other investment.
R is a programming language for statistical computing and data visualization. It has been adopted in the fields of data mining, bioinformatics and data analysis. [9] The core R language is augmented by a large number of extension packages, containing reusable code, documentation, and sample data. R software is open-source and free software.
Typical programming languages used in computational economics research include C++, MATLAB, Julia, Python, R and Stata. Among these programming languages, C++ as a compiled language performs the fastest, while Python as an interpreted language is the slowest. MATLAB, Julia, and R achieve a balance between performance and interpretability.
The emergence of user friendly and low-cost computer software with which microsimulation models can be created. Examples are R, Java, and Python, each of which can be classified as Free and open source software. Improving data collection activities by governments, corporations, and non-profit organisations. Improving data accessibility.
Rmetrics is a free and open-source software project designed for teaching computational finance.Rmetrics is based primarily on the statistical R programming language, but does contain contributions in other programming languages, such as Fortran, C, and C++.
ML.NET is a free-software machine-learning library for the C# programming language. [4] [5] NAG Library is an extensive software library of highly optimized numerical-analysis routines for various programming environments. O-Matrix is a proprietary licensed matrix programming language for mathematics, engineering, science, and financial analysis.
Financial models with long-tailed distributions and volatility clustering have been introduced to overcome problems with the realism of classical financial models. These classical models of financial time series typically assume homoskedasticity and normality and as such cannot explain stylized phenomena such as skewness, heavy tails, and volatility clustering of the empirical asset returns in ...
One approach to affine term structure modeling is to enforce an arbitrage-free condition on the proposed model. In a series of papers, [2] [3] [4] a proposed dynamic yield curve model was developed using an arbitrage-free version of the famous Nelson-Siegel model, [5] which the authors label AFNS. To derive the AFNS model, the authors make ...