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NMOF (version 2.2-2)

Numerical Methods and Optimization in Finance

Description

Functions, examples and data from the first and the second edition of "Numerical Methods and Optimization in Finance" by M. Gilli, D. Maringer and E. Schumann (2019, ISBN:978-0128150658). The package provides implementations of optimisation heuristics (Differential Evolution, Genetic Algorithms, Particle Swarm Optimisation, Simulated Annealing and Threshold Accepting), and other optimisation tools, such as grid search and greedy search. There are also functions for the valuation of financial instruments such as bonds and options, for portfolio selection and functions that help with stochastic simulations.

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Install

install.packages('NMOF')

Monthly Downloads

1,105

Version

2.2-2

License

GPL-3

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Repository

https://github.com/enricoschumann/NMOF

Homepage

http://enricoschumann.net/NMOF.htm

Maintainer

Enrico Schumann

Last Published

October 20th, 2020

Functions in NMOF (2.2-2)

Download Datasets from Kenneth French's Data Library

Constant-Proportion Portfolio Insurance

Local-Search Information

Internal NMOF functions

Stochastic Local Search

Numerical Methods and Optimization in Finance

Simple Moving Average

Computing Prices of European Calls with a Binomial Tree

Optimisation with a Genetic Algorithm

Threshold-Accepting Information

Optimisation with Threshold Accepting

Download Robert Shiller's Data

Diversification Ratio

Optimisation with Simulated Annealing

Optimisation with Differential Evolution

Zero Rates for Nelson--Siegel--Svensson Model

Factor Loadings for Nelson--Siegel and Nelson--Siegel--Svensson

Particle Swarm Optimisation

Mutual Fund Returns

Theoretical Valuation of Euro Bund Future

Simulated-Annealing Information

Price a Plain-Vanilla Call with the Characteristic Function

Price of a European Call under the Heston Model

German Government Bond Data

Option Pricing via Monte-Carlo Simulation

Resample with Specified Rank Correlation

Zero-Bracketing

Pricing Plain-Vanilla Bonds

Put-Call Parity

Minimum-Variance Portfolios

Minimum Conditional-Value-at-Risk (CVaR) Portfolios

Display Code Examples

Classical Test Functions for Unconstrained Optimisation

Full-rank Column Subset

Computing Mean--Variance Efficient Portfolios

Price of a European Call under Merton's Jump--Diffusion Model

Restart an Optimisation Algorithm

Prepare LaTeX Table with Quartile Plots

vanillaOptionEuropean

Pricing Plain-Vanilla Options (European and American)

Integration of Gauss-type

trackingPortfolio

Compute a Tracking Portfolio

xtContractValue

Contract Value of Australian Government Bond Future

Create a Random Returns

Repair an Indefinite Correlation Matrix

Partial Moments