realized-volatility

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

Estimation of realized quantities

R code and Realized Volatility (RV) series set for fitting NN-based-HAR models to multinational RV series.

Compare realized volatility estimators for intraday data

Intraday volatility estimation using High-Frequency Financial Data

Official code - M2VN(Multi-Modal Learning Network for Volatility Forecasting)

Utilities for fetching, reading, validating, caching, consolidating, and splicing intraday OHLCV price files from multiple vendors.

Replication of "Variance Risk Premia in the Interest Rate Swap market" paper (2016) by Desi Volker PhD

C++17 realised-volatility forecasting pipeline: Bloomberg BLPAPI → SQLite → feature engineering → Chronos-2 inference via ONNX Runtime, benchmarked against HAR-RV and persistence with Diebold-Mariano tests.

Does recent options trading help forecast how much a stock will move in the next fifteen minutes? Six U.S. stocks, two years of intraday data, chronological evaluation under rules written before the calculation. Option prices help both models; order flow adds a small gain in the linear model only. Prospective replication pending.

Implied volatility surface fitting, SVI calibration, variance swap pricing, arbitrage detection, and greeks surfaces in Python. Uses the FlashAlpha API.

Forecasting realized volatility for 5 US sector ETFs using statistical models (ARIMA, GARCH, EGARCH), machine learning (Ridge, XGBoost, SVR), and deep learning (LSTM) on 20 years of daily data.

R package to estimate and forecast the HAR (Heterogeneous Autoregressive) model and its extensions.

Tick-by-tick futures microstructure engine.

Dynamic Delta-Hedging Strategy on Real Market Data (NVDA).Dynamic Delta-Hedging Strategy on Real Market Data (NVDA). Analysis of the impact of volatility estimators (Garman-Klass vs Close-to-Close) on the performance of a self-financing replicating portfolio

Calculation of stock realized variance based on trade data on WRDS cloud

Transformer for FX realized-volatility forecasting. Each hourly block encodes the joint market state (10 forex pairs + 14 macros + events + HAR features) into a single context vector; 24-horizon output for one target symbol.

GARCH estimation with BFGS

Realized volatility analytics dashboard for financial market analysis using Python and Streamlit.

HAR-RV volatility forecasting baseline with ML overlay — USDZAR/JSE Top 40