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 ...
Scientific Research Publishing

Forecasting and Assessing the Impact of Financial Derivatives on Financial Risk ()

The excerpt from the 20th CPC National Congress report underscores the pressing need to address significant challenges in averting financial risks. It emphasizes the importance of fortifying the ...
Frontiers

A hybrid deep learning model with error correction for photovoltaic power forecasting

The penetration of photovoltaic (PV) power into modern power systems brings enormous economic and environmental benefits due to its cleanness and inexhaustibility. Therefore, accurate PV power ...
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 ...