volatility-modeling

A vectorized implementation of py_vollib, that supports numpy arrays and pandas Series and DataFrames.

GARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hedging, portfolio management, and risk management

Market Data & Derivatives Pricing Tutorial based on Jupyter notebooks

Implementation with a Jupyter Notebook of the VIX index modelization provided in its CBOE white paper.

IBOVESPA volatility forecasting

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

SABR Implied volatility asymptotics

Collection of numerical methods for high frequency data, in Python notebooks

Python wrappers around QuantLib and Pandas to easily generate volatility surfaces

Study on volatility transmission and protuberance among developed and developing stock markets using multivariate GARCH

Dashboard for return, volatility and correlation analysis for the NAFTRAC IPC. Mexican Stock Exchange (BMV).

Equity Derivatives in C++

computes Volatility Spillover between Cryptocurrency (BTC/USD) and S&P 500 index

This repository includes the scripts to replicate the results of my WORKING paper entitled "A False Discovery Rate Approach to Optimal Volatility Forecasting Model Selection". Access the article here https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3737477

time series and volatility modeling

Modelling volatility and dependency. Risk Modelling using different approaches, such as with Extreme Value Theory and risk-factor mapping. Based on labs in Financial Risk Management at Liu.

Contains financial studies work, including capital markets, corporate finance and other topics.

Algoritmos en R para las volatilidades propuestas en el Capitulo 9 del libro Paul Wilmott Introduces Quantitative Finance.

Python & Finance

In this notebook, I've loaded historical Dollar-Yen exchange rate futures data. I've applied time series analysis and modeling to determine whether there is any predictable behavior.

This repo is about forecasting the Yen movements in order to know whether to be long or short.

Specify and fit GARCH models to forecast time-varying volatility and value-at-risk.

Trying to set up a blog

The project aims to profile stocks with similar weekly percentage returns using K-Means Clustering. The project calculates realized volatility for each stock and predicts realized volatility for each stock using classical volatility models and machine learning models and comparing their performance. This is a capstone project for CIVE 7100 Time Series and Geospatial Data Sciences.

Repository for code used in my bachelor-thesis with the title: "Analyse der Prediction-Power von Recurrent Neural Networks am Beispiel von Finanzmarktdaten"