There are 1 repository under quantitative-methods topic.
A framework for systemic risk valuation and analysis.
A framework for historical volatility estimation and analysis.
Open souce quantitative finance models and algorithms with tutorials
A framework for estimating Basel IV capital requirements.
A framework for detecting misreported returns in hedge funds.
An Excel integration of OpenGamma Strata.
R package for quantitative ethnobotany
Aggregate data and sample code for the paper: Novelty and Cultural Evolution in Modern Popular Music
Materials and resources for the Python Hackathon hosted by the QCBio - UCLA
A .NET implementation of Initial Margin models.
FRE 6083, 9733, 6831, 6711
Return On Invested Capital - This repository contains files that demonstrate Quantitative systematic investment strategy using Return On Invested Capital as a single factor strategy.
Source code and slides for the course Topics in Macroeconomics (ECON5098) taught at the University of Glasgow.
I constructed a simulation study to evaluate the statistical performance of two equivalence-based tests and compared it to the common, but inappropriate, method of concluding no effect by failing to reject the null hypothesis of the traditional test. I further propose two R functions to supply researchers with open-access and easy-to-use tools that they can flexibly adopt in their own research.
This repository contains presentations and other files used by Vikas Rawal in the class. Most files are in orgmode format but can be seen directly on github. Scroll down to see the content.
Introductory lectures in statistics and econometrics
A prototype model checker for CTL over constraint semirings
This project developed recommendations for home improvement projects to increase the sale value of homes, based on inferential modeling and quantitative analysis.
A python script made for quantitative analysis of text. It returns the number of times one or more words -specified by the user- appear in the text, as well as the most common words..
This software creates quantitative based calculations with time series results in a graphical output format
Quantitative Methods course final project. 11th semester.
Comprehensive exploration of the Bike Sharing Problem using advanced Machine Learning for demand forecasting and Mathematical Linear Optimization for strategic bike allocation. Includes detailed Jupyter notebooks covering model development, analysis, and optimization solutions.
Interactive graphs associated with Fischer et al. 2020. Scientific Reports.