[PL] My master thesis from PUEB

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

Time Series Analysis in Finance

A web-based and machine-learning fostered prototype tool to find your best financial investment portfolio

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

Detailed implementation of various time series analysis models and concepts on real datasets.

This is a project which uses Data Science, Machine learning to predict the stock movements, minimize the risk and maximise gains of portfolio using fama-french factors and many other models.Also the sentiment towards stocks are also monitored using sentiment analysis. Garch Model is used to predict the volatility and movements for intraday trading.

The aim of this project is to help stocktraders determine suitable stock to enter by helping them keep track of its daily volatility and returns. The user selects a particular stock option which is automatically gotten from an API and stored in a sqlite database. using Garch(1,1) model to forecast volatility. fastapi and dash is used for deployment

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

Welcome to the repository for my conference paper on stock market analysis and predictive models. In this paper, I explore various models to analyze and predict stock market trends. I have employed a combination of traditional time series models and modern machine learning techniques to provide insights into stock price movements.

Implied volatility is a key aspect when it comes to derivatives pricing. With the growing influence of machine learning in finance, I have investigated the use of LSTMs to forecast 1-day forward Implied Volatility.

Code for the case studies and theoretical visualizations for the master thesis 'Estimation and Backtesting of the Expected Shortfall and Value at Risk using Vine Copulas'

Forecasting stock price volaitlity using GARCH models

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.

Apply GARCH (1,1) model into forecasting S&P500. The topic is harder than though so it's still under construction but I'm working on it.

Curso ministrado por mim na Financial Risk Academy (FRA) sobre Introdução ao Risco de Mercado com Python

Predicting Market Volatility