MSGARCH R Package

predict the future volatility of the NASDAQ index using various econometric and machine learning models, including ARCH, GARCH, EGARCH, SVM, and ANN

Calculation of Value at Risk using Generalized normal distribution, EGARCH and GARCH + EVT

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.

DCC-EGARCH with ARCH in Mean for two cases: "standard" and "one half"

In our fourth semester in ISI, Kolkata, we did this project titled "Econometric Analysis on NASDAQ 100". The aim of the project was to implement Econometric tools to extract insights from NASDAQ 100.