MSGARCH R Package

Simulate and estimate volatility by GARCH with/without leverage, riskmetriks. Compute Value-at-Risk and Test on VaR Violation

Scalable implementation of Lee / Mykland (2012), Ait-Sahalia / Jacod (2012) and Ait-Sahalia / Jacod / Li (2012) Jump tests for noisy high frequency data

Scalable implementation of Lee / Mykland (2012) and Ait-Sahalia / Jacod (2012) Jump tests for noisy high frequency data

Package for option pricing and volatility calibration for index (and FX) options

A technical analysis of price volatility in bitcoins for a over a year using 6 hour intervals

two-step quasi-calibration for modified SVI function

Code to reproduce paper Adrian, Duarte and Iyer (2023), “The Market Price of Risk and Macro-Financial Dynamics”

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

This project, from the University of St. Gallen, explores volatility indices, focusing on VSTOXX and MSCI World calculations using Python, and volatility derivatives modeling with R. It includes tools for financial analysis and trading strategies, referencing key literature.

optionsAnalytics code and pricing analysis.

Collection of code for detection and modeling of jumps (WIP)

The estimation of volatility using Brownian Motion from a Stochastic Process.

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

Fundamental analysis tool using IEX API

R shiny app that takes ticker symbols as inputs and returns the portfolio cumulative log return, cumulative return square and volatility regime changes.