I am a Quantitative Risk Analyst with 6+ years of experience in asset management and banking, specializing in market risk modeling, derivatives analytics, and volatility forecasting. My work combines statistical modeling, computational methods, and applied risk research across multi-asset portfolios.

In quantitative risk, my focus is on building and validating risk frameworks, Historical VaR, CVaR, Expected Shortfall, and Monte Carlo simulation with strong emphasis on backtesting, model governance, and robustness across changing market regimes. I build interpretable and production ready risk models that translate empirical research into practical risk monitoring and investment decision-making systems.

Alongside risk modeling, I work extensively in derivatives analytics, including options pricing, Greeks, volatility surface construction, and structured product analysis across equities, FX, and fixed income. I also apply machine learning techniques, XGBoost, LSTM, and feature engineering to risk estimation, volatility forecasting, and signal research.

My long-term goal is to advance quantitative risk research, developing more robust and adaptive risk frameworks that perform reliably across market regimes, asset classes, and evolving regulatory environments.

My quantitative research spans market risk modeling and derivatives analytics across the full model lifecycle:

• Market risk framework development — VaR, CVaR, Expected Shortfall, stress testing
• Volatility modeling — GARCH, HAR-RV, rolling volatility, regime analysis
• Derivatives analytics — options pricing, Greeks, implied vs realized volatility
• Model validation — SR 11-7 conceptual soundness, backtesting, out-of-sample testing
• Machine learning for risk — XGBoost, LSTM, walk-forward validation
• Automated risk monitoring — drawdown detection, exposure thresholds, reporting pipelines

I primarily work across equities, FX, fixed income, and options, focusing on model robustness, interpretability, and production-ready risk systems.

Built Historical VaR, Parametric VaR, and Monte Carlo-based Expected Shortfall frameworks with full backtesting workflows — VaR exception analysis, breach reporting, and model limitation documentation aligned with internal governance standards.

Developed GARCH, EGARCH, and HAR-RV volatility forecasting models with rolling estimation windows. Evaluated short-horizon predictive accuracy and model stability across different market regimes and asset classes.

Built Monte Carlo engines for portfolio risk simulation and derivatives pricing — European and path-dependent options, variance reduction techniques (antithetic variates, control variates), and convergence analysis.

Modeled option payoffs and Greeks (Delta, Gamma, Vega, Theta, Rho). Constructed and calibrated implied volatility surfaces for vanilla and structured derivatives. Analyzed implied vs realized volatility spreads across market conditions.

Designed and executed stress testing frameworks under interest rate, liquidity, credit spread, and macroeconomic shock scenarios across fixed income and multi-asset portfolios. Identified key risk sensitivities and tail exposures.

Validated statistical and machine learning models against SR 11-7 guidelines — conceptual soundness reviews, input data quality assessment, backtesting, model drift analysis, and out-of-sample performance evaluation across different market conditions.

Built fixed income risk analytics covering duration, convexity, DV01, and key rate duration. Modeled portfolio sensitivity under parallel and non-parallel yield curve shifts and interest rate stress scenarios.

Applied XGBoost and LSTM models to financial time-series for risk signal evaluation and volatility forecasting. Focused on feature engineering, walk-forward validation, and avoiding overfitting in predictive risk models.

Developed Python-based pipelines to process raw market data and compute returns, volatility, PnL attribution, drawdown metrics, and factor sensitivities across multi-asset portfolios.

Built portfolio risk decomposition frameworks covering marginal VaR, component VaR, cross-asset correlations, and factor sensitivities. Analyzed concentration risk and diversification benefits under normal and stressed conditions.

Programming: Python · R · SQL · Linux

Risk & Quant Methods: VaR · CVaR · Expected Shortfall · Monte Carlo Simulation · GARCH · HAR-RV · Stress Testing · Scenario Analysis · Backtesting · SR 11-7 Model Validation · Stochastic Processes · Econometrics

Libraries & Frameworks: NumPy · Pandas · SciPy · Statsmodels · Scikit-learn · XGBoost · Matplotlib · Plotly

Market Data Platforms: Bloomberg Terminal · FactSet

Tools: Git · PySpark · ETL Pipelines

• MS Financial Economics — University of Wisconsin–Madison
• Chartered Accountant (CA) — ICAI, India
• FRM Candidate — GARP
• CMSA — Corporate Finance Institute
• CFA Investment Foundations® — CFA Institute

Nathan S. Brand Award 2025 — Excellence in Finance & Investment Banking Presentation
McKinsey & Co. Leadership Program
United Nations Delegate