ARMA-GARCH

I perform time series analysis of data from scratch. I also implement The Autoregressive (AR) Model, The Moving Average (MA) Model, The Autoregressive Moving Average (ARMA) Model, The Autoregressive Integrated Moving Average (ARIMA) Model, The ARCH Model, The GARCH model, Auto ARIMA, forecasting and exploring a business case.

Python code for pricing European and American options with examples for individual stock, index, and FX options denominated in USD and Euro. Jupyter notebooks for pricing options using free publicly available datasets.

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."

Explore TESLA stock price (time-series) using ARIMA & GARCH model.

Option pricing and Delta hedging performance comparison between Black and Scholes vs Artificial Neural Network

Custom Neuron Decision-Making and Visual Workflow Orchestration Quantitative

A dashboard for helping beginners identify trading opportunities through technical analysis, fundamental analysis, and possible future projections.

Model and replications scripts for the 2020 IMF Working Paper "Foreign Exchange Interventions Rules for Central Banks: A Risk-Based Framework"

Python-written project that utilizes Time Series analysis, along with a Linear Regression model, to forecast the price of the Japanese Yen vs. the US Dollar. ARMA, ARIMA, and GARCH forecasting models included, as well as decomposition using the Hodrick-Prescott filter. In-Sample and Out-of-Sample performance metrics used to evaluate Linear Regre…

For this project, I used Bitcoin's daily closing market price dataset from Jan 2012 to March 2021 Kaggle. This work's main objective includes explaining how to analyze a time series and forecast its values using ARIMA and GARCH models.

Calculation of Value at Risk using Generalized normal distribution, EGARCH and GARCH + EVT

A template for building an advanced Automated High-Frequency Trading (HFT) system. Note: For educational purposes only; customize before deploying in live markets.

Code for value-at-risk calculation and backtesting.

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

Identified the most appropriate Time-Series method to forecast drought in African countries, acting as a critical early warning for drought managements

Implementation of ARCH and GARCH models with MLOps pipeline on the AWS for deployment and management in a production environment.

Real-time volatility sensitive detection and correction for any sequential generative process. AutoTune, feedback learning, reflexive analysis, Monte Carlo SDE bands, Kalman filtering, GARCH variance modeling, signal detection, and domain anchoring.

Time series analysis and forecasting with statistical models, exponential smoothing, and curve-fitting techniques.

A Python implementation of a Bayesian GARCH(1,1) model using a Random Walk Metropolis MCMC sampler. Developed for the Simulations and Methods of Monte Carlo course at ENSAE Paris, featuring synthetic data simulation, real-world financial asset estimation, and full MCMC diagnostics.