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."

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

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

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.

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…

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

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

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

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

ARCH and GARCH models along with MLOps pipeline using AWS platform to deploy model in a production environment.

Developed a forecasting model Hybrid GARCH-ANN By employing Grid Search for NYSE Stock

This project uses the many time-series tools (Hodrick-Prescott Filter, ARMA, ARIMA and GARCH models, linear regression, etc.) to predict future movements in the value of the Japanese yen versus the U.S. dollar.

Using time series tools to predict future movements in the value of the Japanese yen versus the U.S. dollar.