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

Every risk model built on observed history underestimates the true maximum. This is not a calibration problem. It is a structural property of optimal estimation — and it can now be proved. After every ...

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

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 ...

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 ...

This repository includes the scripts to replicate the results of my paper entitled "A False Discovery Rate Approach to Optimal Volatility Forecasting Model Selection". The MCB for variable selection ...

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 ...