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Mrinal-g/README.md

Mrinal Gupta

Quantitative Finance | Risk Modeling | Data Science | Python

MS in Financial Engineering with projects in options pricing, transaction-cost-aware portfolio optimization, credit risk, and market risk.

Featured Work

  • Options pricing and hedging with SVI volatility surface, CRR and LSM pricing, delta hedging, and Streamlit dashboard
  • Dynamic trading with predictable returns and transaction costs using Gârleanu–Pedersen and PPO reinforcement learning
  • Credit risk modeling covering PD, LGD, EAD, expected loss, and IFRS 9-style staging
  • ETF market risk modeling with VaR, expected shortfall, backtesting, Basel traffic-light validation, and stress testing

Technical Stack

Python, pandas, NumPy, scikit-learn, XGBoost, statsmodels, CVXPY, PyTorch, Stable-Baselines3, SQL, R

Links

Pinned Loading

  1. dynamic-trading-predictable-returns dynamic-trading-predictable-returns Public

    Transaction-cost-aware portfolio optimization using the Gârleanu–Pedersen framework, VAR-based signal dynamics, and PPO reinforcement learning on factor-driven portfolios.

  2. options-pricing-hedging options-pricing-hedging Public

    Production-style options pricing and hedging system using live option chains, SVI volatility surface calibration, CRR and LSM pricing, delta hedging, and Streamlit dashboard.

    Jupyter Notebook

  3. credit-risk-modeling credit-risk-modeling Public

    End-to-end credit risk pipeline for PD, LGD, EAD, expected loss, IFRS 9-style staging, and stress testing on LendingClub loan data.

    Jupyter Notebook

  4. etf-market-risk-modeling etf-market-risk-modeling Public

    ETF portfolio market risk workflow covering VaR, expected shortfall, rolling backtesting, Basel traffic-light testing, and stress scenarios.

    Jupyter Notebook