Why marketing mix modeling is still hard to get right

Open-source tools have made MMM more accessible, but reliable results still depend on clean data, thoughtful modeling, and ...
GitHub

This repo describes python code and input data files for "Forecasting stock return distributions around the globe with quantile neural networks".

torch 2.2.2 PyTorch framework for neural networks. The documented GPU setup targets the official CUDA 12.1 wheel. FF3.CSV Ken French Data Library Full file included Fama-French 3-factor returns. Only ...
GitHub

Systematic Trading Research Platform

A modular Python / FastAPI / React platform for systematic strategy research: signal generation, regime classification, risk-aware portfolio construction, execution-cost-aware backtesting, and an ...
Benzinga.com

A Comprehensive Guide to Effective Volatility Prediction using GARCH-X

Volatility forecasting is a key component of modern finance, used in asset allocation, risk management, and options pricing. Investors and traders rely on precise volatility models to optimize ...
Scientific Research Publishing

Integrating Volatility Models within State Space Frameworks for Commodity Return Analysis ()

The study applies a Kalman filter (KF) to Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models to create a hybrid model, to estimate the parameters of the GARCH model in the ...
IEEE

Statistical Model Approach of Electricity Price Forecasting for Indian Electricity Market

Abstract: The cost of renewable power price is coming down with increased modest methods in the electricity market. When it is to benefit both the producers and consumers of electricity, a next-day ...
Scientific Research Publishing

An Option Valuation Formula for Stochastic Volatility Driven by GARCH Processes ()

We have developed a practical and elegant closed-form option pricing formula for general GARCH models using a risk-neutral argument. To estimate the parameters, we propose a procedure and utilize ...