ARMA-GARCH

MSGARCH R Package

MSc Finance dissertation project at Newcastle University. This project focused on forecasting the volatility of exchange rates involving the Great British Pound using EWMA, GARCH-type and Implied Volatility models.

This repository of codes includes in the R and Python programs used in the six chapters of my published book titled "Analysis and Forecasting of Financial Time Series: Selected Cases". The book is published by Cambridge Scholars Publishing, New Casle upon Tyne, United Kindoam, in 2022.

GJR-GARCH models with exogenous variance regressors

Simulation and estimation of ARCH and GARCH processes, used to model the time-varying standard deviation (volatility) of asset returns, with conditional distributions such as the normal, Laplace, and Student t.

This project implements an advanced quantitative pipeline to forecast Bitcoin (BTC-USD) Realized Volatility for December 2025.

GARCH estimation using the arch package

Fit GARCH-in-mean models to daily asset class returns using the Python arch package

This project showcases an advanced GARCH implementation in Python, APARCH(1,1). It determines the parameters best defining a stock's returns variance, and then uses these in a Monte Carlo simulation to simulate future returns with asymmetric volatility clustering.

Backtesting Median Shortfall and VaR

GARCH simulation using only generic Python libraries