JR Concepcion

MS Financial Engineering

Concentration: Quant Risk

BS in Finance, BA in Economics, Minors in Mathematics & Statistics

Concentration: Fintech, Research & Data Analysis

• Validate yearly and quarterly reinsurance data within business deadlines to Generali's central office.
• Built Excel trackers and interactive dashboards to centralize account data, allowing account managers to monitor reinsurance positions and report deadlines at a glance.
• Collaborate with a manager to automate monthly and quarterly reports utilizing VBA and Salesforce.

• Provided guest lectures and training workshops for William Paterson students.
• Provided one-on-one tutoring to over 75 students in Python, R, and Bloomberg.

• Developed a Python-based desktop app to scrape and display up to 2000 SEC filings with automated data retrieval and filtering for a team of traders.
• Packaged the app as a standalone executable with Py2Exe, integrating financial data from a live database.

• Implemented a Crank-Nicolson finite difference scheme with PSOR to price American options, enforcing the early exercise constraint.
• Developed and tested single-stock/option rebalancing simulations, modeling delta and delta-gamma hedging strategies to quantify hedge effectiveness under rebalancing.
• Formulated the rebalancing problem as an impulse control problem, establishing the theoretical framework for optimal intervention timing subject to transaction costs.
• Presented project at Quaint Quant Conference, SMU Cox School of Business.

• Rebalance the index with an updated registry of Pennsylvania companies, an updated divisor, and adjusted market caps utilizing CBOE market data.
• Automated the daily index value calculation and visualizations utilizing Streamlit, an SQL Database, and GitHub Cron Jobs.
• Lead transition to storing all values in a SQL database to optimize data management and storage for future groups.

• Developed greeks-package, a Python library for calculating first-, second-, and third-order Black-Scholes Greeks for European options using NumPy/SciPy.
• Enabled efficient option chain downloading from Yahoo Finance for multiple tickers in a singular pull.
• Created interactive 3D visualization tools with Plotly for Greeks surfaces, implied volatility, and open interest, optimized for performance with vectorized operations.

• Developed fin-eda, a Python library delivering a full quantitative tearsheet for any stock ticker or price series, computing 60+ metrics across returns, risk, drawdowns, volatility, and liquidity.
• Engineered benchmark analytics including annualized capture ratios, Jensen-style excess returns, information ratio, and beta/correlation with automatic SPY data fetching via yfinance.
• Designed for accessibility, enabling non-technical users to generate quantitative results with a single function call.