Options Volatility Modeling Research

I trade index and single-stock options on Indian exchanges and I’m looking for a quant who can dig deep into volatility modeling with one clear goal: sharpen my live trading strategies. You will take raw options data (primarily vix NSE/BSE), build and calibrate advanced volatility models—think GARCH variants, stochastic-vol, local-vol, surface interpolation—and translate the results into actionable entry, exit and sizing rules. Experience handling large intraday datasets, cleaning corporate-action adjustments, and coding robust back-tests in Python / R is essential; C++ or Julia skills are a plus if you can speed up the heavier calculations. Key deliverables • A fully documented research notebook or repo that pulls, cleans and stores historical option chains and underlying spot prices • At least two comparative volatility models, calibrated to Indian market microstructure, with performance metrics against realised P&L and risk-adjusted returns • Back-test framework, parameter sweep scripts and visual reports that show where the strategy outperforms benchmarks • A concise summary paper explaining methodology, assumptions, and how each model feeds the trading logic Acceptance criteria • Code runs end-to-end on my sample dataset without manual tweaks • Results replicate within ±1 % on a fresh data slice • Documentation is clear enough for me to iterate without additional hand-holding I’ll be available for quick feedback loops and can provide API keys, existing data dumps and execution constraints up front so you can focus on producing solid, trade-ready research.

Project ID: 40423738