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.

Intraday volatility estimation using High-Frequency Financial Data

Compare realized volatility estimators for intraday 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.

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.

Historical RV15 forecasts for six U.S. stocks. Option state improves both families; the mixed B2 block improves the linear model only. Source-time assumptions, placebo and final-window limits remain material. Public aggregate checks are not an independent licensed-data replication.

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

Leakage-safe EUR/USD realized-volatility estimation and forecasting with HAR-RV, walk-forward evaluation, and statistical model comparison.

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.

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

Tests whether option, news, attention and macro data beat HAR at forecasting SPY realized volatility, with LSTM, boosted trees and LASSO, Diebold-Mariano, MCS and Holm tests, then trades the forecasts in volatility targeting and delta-hedged straddles.

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

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