arodman

arodman's Stars

• PacktPublishing/Machine-Learning-for-Algorithmic-Trading-Bots-with-Python

  Language:Jupyter Notebook393167

• PacktPublishing/Python-for-Algorithmic-Trading-Cookbook

  Python for Algorithmic Trading Cookbook, published by Packt

  Language:Jupyter Notebook496158

• TopherF22/PCA_Analysis

  Using PCA to deconstruct yield curves

  Language:Jupyter Notebook2

• KyleMFE/PCA

  PCA Applications in Yield Curve Structure

  Language:Jupyter Notebook51

• Qery-Data/Norway_Industry_Tracker

  Key economic indicators for different industries in Norway

  Language:Python1

• fipelle/replication-hasenzagl-et-al-2020

  Replication code for "A Model of the Fed's View on Inflation".

  Language:Jupyter Notebook813

• rangikagmg/Empirical-Analysis-and-Forecasting-of-yield-curves.

  Principal Component Analysis(PCA), Nelson-Siegel(NS) model, and Gaussian Regression Process(GPR) are used to fit and forecast the European yield curve with different maturities.

  Language:Jupyter Notebook52

• robcarrick/Visualisations-Australian-Economy

  Creating a 3D yield curve with Australian bond data.

  Language:MATLAB22

• supreeth8/Term_structure_modeling

  Yield curve Interpolation using cubic spline and nelson Seigel model

  Language:Jupyter Notebook153

• bernhard-pfann/pca-yield-curve-analytics

  Predictive yield curve modeling in reduced dimensionality

  Language:Jupyter Notebook4317

• letsgoexploring/fredpy

  A Python module for easily retrieving and manipulating data series from Federal Reserve Economic Data (FRED)

  Language:Python568

• tomasrubin/yield-curve-forecasting

  This repository provides the implementation of a handful of forecasting methods in yield curve modelling.

  Language:Jupyter Notebook2413

• epogrebnyak/data-ust

  US Treasuries Yield Curve Data

  Language:Python2313

• fabriziobasso/PCAapplied_and_European_Yield_Curve

  This paper aims to explore the time series’ proprieties of the features extracted by using the Principal Component Analysis (PCA) technique on the European AAA-rated Government Bond Yield curve. The PCA can greatly simplify the problem of modelling the yield curve by massively reducing its dimensionality to a small set of uncorrelated features. It finds several applications in finance and in the fixed income particularly from risk management to trade recommendation. After selecting a subset of Principal Components (PCs), this paper first analyzes their nature in comparison to the original rates and the implications in terms of information retained and lost. Then the time-series characteristics of each PC are studied and, when possible, Auto-Regressive Moving-Average (ARMA) models will be fitted on the data. One hundred observations of the original dataset are set aside as a test set to evaluate the predictive power of these models. Eventually, further analyses are performed on the PCs to evaluate the presence of heteroscedasticity and GARCH-ARCH models are fitted when possible. Tests are performed on the fitted coefficient to investigate the real nature of the conditional variance process.

  Language:Jupyter Notebook1713

• MacroDave/bimets

  MARTIN Macro Model in R

  Language:R92

• MacroDave/MARTIN

  RBA's MARTIN Macroeconometric Model of the Australian Economy

  Language:xBase228

• ahgperrin/PyCurve

  PyCurve : Python Yield Curve is a package created in order to interpolate yield curve, create parameterized curve and create stochastic simulation.

  Language:Python398

• skasim/yield-curve

  A Python/Jupyter notebook project to understand the Yield Curve and its potential for forecasting a recession

  Language:Jupyter Notebook4017

• satyam0236/Layoff_Analysis_2020-2024

  "Explore layoffs amidst economic uncertainty. Analyze industry impacts, geographic trends, and strategic insights with Jupyter Notebook. #DataAnalysis #EmploymentTrends"

  Language:Jupyter Notebook2

• jeckonov/Econometrics

  Filters (kalman, hodrick-prescott, moving average) together with comparison and sensitivity analysis (in notebook filters_with_parameters)+var analysis and granger causality test. Test for random walk (CE currencies using yfinance API)

  Language:Jupyter Notebook62

• matteocourthoud/Machine-Learning-for-Economic-Analysis

  Material for the exercise sessions of master course Machine Learning for Economic Analysis @UZH

  Language:Jupyter Notebook8436

• hug-mi/Economic_latent_models

  Bayesian models for estimating the neutral interest rate and NAIRU in Australia

  Language:R33

• MacroDave/NAIRU

  Estimating the NAIRU for Australia

  Language:R104

• ElisSVandyck/neutral_terminal_rate_models

  neutral_terminal_rate_models

  Language:R1

• TribThapa/AustralianEconomyAnalysis

  Machine learning implemented to predict change in Australia's GDP

  Language:Jupyter Notebook52

• mathsuser/Investment_Strategy_Backtesting

  This repository provides tools and tutorials for backtesting buy-and-hold investment strategies across asset classes (S&P 500 ETF, Gold ETF, Bitcoin, and Cash). It includes features for analyzing nominal and real (inflation-adjusted) returns, dividend reinvestment, and offers Jupyter notebooks for easy-to-follow analysis and visualizations.

  Language:Jupyter Notebook3

• kinghchan/rolling-correlation-gold-miners-US-treasury-yield

  This notebook plots the rolling correlation between two time series data as a time series data. Data is taken from investing.com. The data used here are: 1) VanEck Gold Miners ETF 2) US 10Yr Treasury Yield

  Language:Jupyter Notebook1

• mano001-ctrl/Fixed-Income-Tools

  Language:Jupyter Notebook2

• myinvestpilot/invest-alchemy

  Invest Alchemy is a trading assistant focused on ETF portfolios.

  Language:Python733121

• bank-of-england/InterpretableMLWorkflow

  Language:Python165