AI-Driven Financial Markets

Developed cutting-edge artificial intelligence tools to enhance financial market modeling. Focus areas included predictive analytics, reinforcement learning for trading decisions, deep learning for volatility clustering, and stress testing using real-time news and sentiment data pipelines.

Quant Equity Selection Model

Built a supervised learning framework that dynamically selects equities based on sector rotation signals, Index Rebalancing Engine, macroeconomic indicators,Tail Risk Hedging, and risk-adjusted return forecasts. The model uses ensemble methods and Bayesian optimization for hyperparameter tuning to ensure robust portfolio construction.

Heston Model Calibration for Volatility

Implemented a calibration engine for the Heston stochastic volatility model, using least squares fitting to implied volatilities from options markets. Conducted parameter stability analysis, error surface visualizations, and comparative backtests with Black-Scholes and SABR models.

Credit Valuation Adjustment (CVA) Computation

Designed a robust CVA engine utilizing Monte Carlo simulations with exposure paths and default probabilities. Incorporated credit spreads, wrong-way risk, and collateral agreements to model bilateral CVA and analyze counterparty credit risk profiles across asset classes.

Hull-White Short-Rate Model Calibration

Calibrated the Hull-White interest rate model using yield curve data and option market quotes. Integrated QuantLib for simulation of future interest rate paths and sensitivity analysis under various market shocks, enabling use in ALM and risk-neutral pricing frameworks.

FRTB Implementation

Developed full-stack analytics for the Fundamental Review of the Trading Book (FRTB), including sensitivities-based standardized approach (SBA), risk factor eligibility tests (RFET), and desk-level capital attribution. Validated internal models and supported regulatory submissions.

Green Finance & Climate Risk Modeling

Contributed to climate finance research through development of models linking emissions to asset exposures. Integrated EEIO data, physical risk scenarios, and climate-adjusted discounting. Work included submission to academic journals on sustainable finance innovations.

Quantitative Trading Strategies

Designed and tested alpha-generating trading strategies combining time-series momentum, machine learning classification signals,Stat Arbitarge, and Kelly criterion-based capital allocation. Deployed backtesting platform with walk-forward validation and multi-asset optimization.

Monte Carlo Simulation Engine for Credit Loss Forecasting

Built a Monte Carlo simulation engine to project credit losses under IFRS 9 using ARIMA-forecasted macroeconomic variables and dynamic exposure paths. Incorporated LGD/EAD modeling, lifetime PD, and stress scenario overlays for economic capital analysis.

Hybrid Scope 1/2/3 GHG Estimator

Developed a comprehensive GHG emissions estimator combining EEIO analysis, process-based LCA, and Monte Carlo methods. Mapped financed emissions for financial portfolios and generated uncertainty bands for strategic planning under regulatory and climate risk guidelines.

Bermudan Swaption Pricing Platform

Engineered a flexible simulation platform for pricing and hedging Bermudan swaptions. Leveraged QuantLib with support for G2++ and Hull-White models, incorporating American-style optionality, volatility surfaces, DV01/Vega analytics, and hedge backtesting strategies.

Validation Framework for FRTB & CVA

Led end-to-end model validation for market and counterparty credit risk models. Included P&L attribution, benchmarking, backtesting, and regulatory documentation for FRTB sensitivities, stressed ES, and CVA exposure models across global trading desks.

CDS Spread Calibration with Merton Model

Built a Merton structural model for estimating default probabilities and calibrating CDS term structures. Combined historical volatility estimation, firm balance sheet analysis, and ARIMA-based forecasts for forward CDS curve projections and pricing.

Liquidity Risk and IRRBB Simulator

Developed a simulation-based ALM engine modeling behavioral deposit runoff, interest rate shocks, and NII sensitivity. Used this framework to assess interest rate risk in the banking book (IRRBB) and perform liquidity stress testing under adverse curve shifts.