Alexander Rom - MFE Student

About Me

I'm a Financial Engineering graduate student at USC with a background in econometrics and computer science. I focus on quantitative modeling in macro-finance, especially rates, credit, and volatility, and I build research and tools that turn market and economic data into interpretable models and decision-ready outputs. This site is a curated portfolio of my projects, papers, and ongoing research.

Modeling Philosophy

Most market history sits inside "normal" ranges, but the outcomes that matter show up when regimes shift and assumptions break. I'm interested in the hidden structure beneath that chaos. I believe functions define how the world operates, from the geometry of the space we live in to the dynamics of financial markets, and my goal is to uncover those underlying relationships and turn them into quantitative models that simplify complexity and reveal opportunity. In practice, that means building models that remain reliable under regime changes, volatility clustering, and asymmetric risk, and favoring interpretable structure, strong diagnostics, and robustness over fragile "perfect fits."

Research & Projects

Mapping Market Stress Concentration via Put–Call Ratios

See Details →

Derivatives · Options Microstructure · Market Structure · Python

Builds signal-generating diagnostic tooling to highlight where market stress and downside hedging pressure may be concentrating, by decomposing index option activity into flow, positioning, and term-structure components across expirations

Time-Series Analysis of Treasury Bond ETF

See Details →

Time Series · ARIMAX · TAR Regimes · GARCH · Forecasting

Modeled daily TLT returns (2004–2024) with regime-switching ARIMAX (Fed-rate states) and GARCH volatility, showing strong flight-to-safety dynamics (VIX↑, equities↓) and improved fit/forecasting versus a baseline ARIMA.

Impact of Banking Competition on Household Loan Rates in the Euro Area

See Details →

Panel Data · Hausman–Taylor · Fixed Effects · Banking / Credit Markets

Panel regression across 13 Euro-area countries (2014–2020) testing how local banking competition (branches per 100k adults) relates to household loan rates using Fixed Effects and Hausman–Taylor.

Determinants of U.S. Credit Card Delinquency Rates

See Details →

Time Series · OLS Regression · Nonlinear Effects · Newey–West · Household Finance

Models U.S. credit-card delinquency (2000Q3–2023Q2) using quarterly FRED data and an OLS/Newey–West time-series regression to quantify how credit-card APRs, financial conditions, and unemployment (with a COVID regime interaction) explain default risk over time.

Experience

Insider Ownership Index

Quantitative Research Consultant

Los Angeles, CA Sep. 2025 - Present

Derived return-maximizing insider ownership level for S&P 500 firms (5- and 7-year forward returns) using cross-sectional and panel regressions; the resulting curve underlies Inside Ownership 100 index weights
Performed stress testing and scenario analysis on the index with optimal insider-ownership weights, and applied Fama–French factor regressions to evaluate return stability across market regimes and validate signal robustness (Python)

University of San Francisco

Teaching Assistant

San Francisco, CA Jan. 2024 - May 2025

Assisted professors across three courses (Economic Methods, Intermediate Microeconomics, Applied Econometrics); led weekly sections and office hours covering microeconomic theory, regression analysis, and optimization for 60+ students. Across semesters, average course performance increased by ~5% relative to the previous term. In Applied Econometrics, mentored 30 students through thesis-style empirical papers, supporting research design, model specification, robustness checks, and publication-ready writing.

Bridges and Barriers Advisory Services

Data Analyst Intern

San Francisco, CA May 2024 - Sep. 2024

Built Python pipelines to ingest, clean, and reconcile market/fundamental datasets for a hedge-fund client, including QA of financial statements and automated trade-ops reporting (raw executions → formatted trade tickets), cutting ~50+ hrs/month of manual work and saving clients ~$5.6K/month in operating costs.
Collaborated with clients, identifying inefficiencies in operational workflows; implemented quantitative automations that improved data accuracy, cut manual work, and enhanced investment analysis

Education

University of Southern California (Viterbi)

Aug 2025 – May 2027 (Expected)

M.S. Financial Engineering

Dean's Master's Scholarship (merit-based)

Relevant coursework: Optimization, Probability/Stochastic Processes, Derivatives Pricing, Machine Learning

University of San Francisco

Aug 2022 – May 2025

B.S. Economics (Financial Economics)
Minor: Computer Science

Relevant coursework: Real Analysis, Linear Algebra & Probability, Financial & Applied Econometrics, Micro & Macroeconomics, Statistics, Data Structures & Algorithms, Options & Futures

Fed Challenge (2023): Led 5-person team; presented policy recommendation to Federal Reserve judges