📈This repo contains detailed notes and multiple projects implemented in Python related to AI and Finance. Follow the blog here: https://purvasingh.medium.com
Interactive Brokers Fundamental data for humans
Implements different approaches to tactical and strategic asset allocation
Portfolio Construction Functions under the Basic Mean_Variance Model, the Factor Model and the Black_Litterman Model.
R package for fitting high-dimensional multivariate linear mixed effect models
Work with trained factor models in Python
Repository for the AugmentedPCA Python package.
DRIP Asset Allocation is a collection of model libraries for MPT framework, Black Litterman Strategy Incorporator, Holdings Constraint, and Transaction Costs.
An R package for Factor Model Asset Pricing
Implementation of the FNETS methodology proposed in Barigozzi, Cho and Owens (2024) for network estimation and forecasting of high-dimensional time series
Julia package for simulating and estimating multi-level/hierarchical dynamic factor models (HDFMs).
R codes and dataset for the estimation of the high-dimensional state space model proposed in the paper "A dynamic factor model approach to incorporate Big Data in state space models for official statistics" with Franz Palm, Stephan Smeekes and Jan van den Brakel.
Code for implementing Factor Analysis with BLEssing of dimensionality (FABLE).
A toolkit for asset pricing research
Jupyter notebooks implementing Finance projects
A repo to explore quantitative finance models, libraries and tooling.
sparseGFM implements sparse generalized factor models for dimension reduction and variable selection in high-dimensional continuous, count, and binary data. Stable release available on CRAN (https://cran.r-project.org/package=sparseGFM); development version hosted on GitHub.
Estimation Commodity Pricing Factor Models
Index and Factor Construction with Implied Covariance Process
An empirical analysis of European markets. This thesis compares the perceived dependence of stock and market returns, as measured by the frequency of comovement following Ungeheuer and Weber (2020), with the traditional interpretation of market dependency measured by Sharpe’s beta (1964).
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