volatility-modeling
There are 6 repositories under volatility-modeling topic.
- bitcoin_volatility_forecasting
chibui191 / bitcoin_volatility_forecasting
GARCH and Multivariate LSTM forecasting models for Bitcoin realized volatility with potential applications in crypto options trading, hedging, portfolio management, and risk management
Language:Jupyter Notebook 202 ArturSepp / StochVolModels
Implement pricing analytics and Monte Carlo simulations for stochastic volatility models including log-normal SV model, Heston
Language:Python 115marcdemers / py_vollib_vectorized
A vectorized implementation of py_vollib, that supports numpy arrays and pandas Series and DataFrames.
Language:Python 111- jupyter-notebooks
frontmark / jupyter-notebooks
Market Data & Derivatives Pricing Tutorial based on Jupyter notebooks
Language:Jupyter Notebook 37 qlero / vix_index_modelization
Implementation with a Jupyter Notebook of the VIX index modelization provided in its CBOE white paper.
Language:Jupyter Notebook 21JackJacquier / SABR-Implied-Volatility
SABR Implied volatility asymptotics
Language:Jupyter Notebook 20csatzky / forecasting-realized-volatility-using-supervised-learning
Traditionally, volatility is modeled using parametric models. This project focuses on predicting EUR/USD volatility using more flexible, machine-learning methods.
Language:R 19MajorLift / volatility-modeling-python-datasci
Undergraduate thesis, Seoul National University Dept. of Economics — "Modeling Volatility and Risk Spillover Between the Financial Markets of US and China Using GARCH Value-at-Risk Forecasting and Granger Causality."
Language:HTML 18white07S / Pricing-Models
Implementation of option pricing models using Numba that performs better. This entire project has utilized as little libraries as possible, even though certain models have their own Machine Learning Model with assessment and performance.
Language:Python 13aaronsmith1234 / volatilipy
Python wrappers around QuantLib and Pandas to easily generate volatility surfaces
Language:Python 12- Language:R 12
gaelwjl / ML_Optimal_Trading
Collection of numerical methods for high frequency data, in Python notebooks
Language:Jupyter Notebook 12- Language:Python 12
kedarghule / Stock-Portfolio-Diversification-Using-Clustering-and-Volatility-Prediction
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.
Language:Jupyter Notebook 10imsanjoykb / IBOVESPA-volatility-forecasting
IBOVESPA volatility forecasting
Language:R 8yatshunlee / CAViaR
Measure market risk by CAViaR model
Language:Jupyter Notebook 8SakethAleti / PresAddress-SoFiE2021
Code for the paper "Realized Semi(Co)Variation: Signs that All Volatilities are Not Created Equal"
Language:Jupyter Notebook 5boyla950 / predicting-the-pound
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.
Language:Python 4hobinkwak / Volatility-Spillover
computes Volatility Spillover between Cryptocurrency (BTC/USD) and S&P 500 index
Language:Python 4Quant-TradingCO / Volatility-and-options
In this repo you will find some tools related to pricing and risk measurement of options. You can find tools to calculate the price of an option like de Black-Scholes or Heston Model, or to get implied volatilities.
Language:Jupyter Notebook 4sam14032000 / market_linkages_mgarch-bekk
Study on volatility transmission and protuberance among developed and developing stock markets using multivariate GARCH
Language:Jupyter Notebook 4garthmortensen / finance
Contains financial studies work, including capital markets, corporate finance and other topics.
Language:MATLAB 3hkalager / FDR_buckets
This repository includes the scripts to replicate the results of my paper entitled "A False Discovery Rate Approach to Optimal Volatility Forecasting Model Selection".
Language:MATLAB 3EstrixDS / thesis_rnn_financial_prediction
Repository for code used in my bachelor-thesis with the title: "Analyse der Prediction-Power von Recurrent Neural Networks am Beispiel von Finanzmarktdaten"
Language:Jupyter Notebook 2ggstream12 / GARCH-model-in-R
GARCH models to forecast time-varying volatility and value-at-risk in R
Language:R 2ibaris / finance-tda
Topological Tail Dependence: Evidence from Forecasting Realized Volatility
Language:Jupyter Notebook 2israelcastilloh / mx-stocks-analyzer
Dashboard for return, volatility and correlation analysis for the NAFTRAC IPC. Mexican Stock Exchange (BMV).
Language:Python 2jolly-io / Stock_Markets_Assymetric_Volatility_Spillover_Effects
I investigate the Asymmetric Volatility Spillover Effects within and across six major International stock markets. United States, Canada, France, Germany, Italy & Japan
Language:R 2Maurolp15 / JMB_Portfolio_Project
Analyze a personal portfolio of stock's past performance and forecast future performance to optimize daily positional adjustments to create a 20% monthly return
Language:Jupyter Notebook 2mikewenner / PyQuant_Lab
Quant finance notebooks from PQN course
Language:Jupyter Notebook 2- Language:Jupyter Notebook 1
lkamocsai / MATLAB_Toolbox
Toolbox for time series modelling
Language:MATLAB 1MartinMashalov / ImpliedVolatilityAnalysis
Predicting asset prices' directional movements based on implied volatility of price action. This experiment was performed on SPX index fund with VIX as implied volatility reference.
Language:Python 1radusbriciu / Realised-Volatility-Calculator
The workings for a very interesting exercise from the Econometrics of Financial Markets module of the MSc Quantitative Finance 2023/24 course at Bayes Business School (formerly Cass).
Language:Jupyter Notebook 1- Language:Python 1
xli253 / Bitcoin-Futures-price-forecasting-and-volatility-surface
Implementing Bitcoin futures' strike prices and time-to-maturity to construct a volatility surface for potential profit opportunities. Utilizing time series and the GARCH model for volatility forecasting and Long Short-Term Memory (LSTM) for bitcoin futures' price forecasting in Python.
Language:Jupyter Notebook 1
