Forecasting stock price volaitlity using GARCH models

Financial Time Series Analysis

Time Series Forecasting with ARIMA GARCH

Artigo finalizado em 06/08/2021 como membro do Núcleo de Riscos & Derivativos, para o Clube de Finanças, Liga Acadêmica de Mercado Financeiro da UDESC & UFSC.

Time Series Analysis

Análise via modelos GARCHs para preços da PETR4 utilizando o R.

This repository contains an RMarkdown file that performs an econometric analysis of soybean prices using various statistical methods, including cointegration tests, ECM, and GARCH models.

UECM3243 Analysis on Tesla stock using GARCH model

This project performs a detailed time series analysis on NVIDIA Corporation's stock prices over a ten-year period (January 2012 to January 2022). Using R's statistical computing capabilities.

Ce Travail vise à reproduire les méthodes statistiques utilisées dans un article de recherche qui a exploré l’impact de COVID-19 sur la volatilité de l’indice boursier marocain (MASI).

Predictive analysis and GARCH model on stock returns. I demonstrate how to use the PACF (partial autocorrelation function) and ACF (autocorrelation function) on a non stationary time series.

MATH-342 Time Series course taken at EPFL during Spring 17-18.

Stock/Financial Time Series Analysis, Prediction and Forecasting using advanced Statistical methods and GARCH volatility-based models in R.

R을 이용한 경제 시계열 데이터 분석 / GARCH, Legendre models

I investigate the Asymmetric Volatility Spillover Effects within and across six major International stock markets. United States, Canada, France, Germany, Italy & Japan

Project in Statistics: Timeseries analysis (STAH14) at Lund University. The project it about Bitcoin price and returns, modelled using an AR-GARCH model.

In this project, this research generally investigates the financial time series such as the price & return of NASDAQ Composite Index using ARIMA and GARCH methods.

Traditionally, volatility is modeled using parametric models. This project focuses on predicting EUR/USD volatility using more flexible, machine-learning methods.

Portfolio level (un)conditional risk measure estimation for backtesting using Vine Copula and ARMA-GARCH models.