Vikalp Thukral. Quantitative researcher working at the intersection of derivatives, stochastic modeling, and real‑time market data.
I'm a Master of Financial Engineering candidate at UCLA Anderson, currently researching neural‑network‑accelerated Monte Carlo for path‑dependent options and building interactive tools that make complex quantitative models actually usable — including a live, browser‑rendered implied volatility surface for any US‑listed equity.
Builder first,
researcher always.
I came to quantitative finance through an unusual door — six years of large‑scale data and product engineering at American Express, where I owned the Enterprise Data Warehouse and rebuilt legacy PySpark pipelines that cut overnight runtimes from 24 hours to under four. That foundation in systems and scale shaped how I think about models: they only matter if they're fast, reproducible, and usable by someone other than the author.
At UCLA Anderson MFE I've focused on the rigorous side of that equation — stochastic calculus, derivatives pricing, computational methods, and econometrics — and I'm currently a quantitative researcher working on neural‑network‑weighted Monte Carlo for path‑dependent options and backtesting alpha signals via WorldQuant BRAIN. In 2025 my team placed Top 4 of 30 in the IAQF International Student Competition, modelling bubble dynamics with GARCH‑DCC and LPPLS.
Outside of coursework I ship things: an in‑house options pricing library (binomial, trinomial, Crank‑Nicolson PDE solvers, variance‑reduced Monte Carlo), a Fama‑French five‑factor replication suite, a cash‑flow waterfall engine for securitized loans, and a live 3D volatility surface you can visit below. I care about the craft of making quantitative research legible.
Volatility Surface
An interactive 3D implied‑volatility surface visualizer for any US‑listed equity. Pulls live options chains from Yahoo Finance through a custom FastAPI backend, cleans the chain, and renders a smoothed Plotly mesh with four Z‑axis metrics, camera presets, and side‑by‑side term‑structure and skew‑slice mini‑charts.
Research, pricing,
and market modelling.
View all projects →
Geeks for Greeks — Bubble Dynamics
GARCH‑DCC volatility modelling and LPPLS critical‑time bubble detection across multi‑asset regimes. Read the competition paper.
Read Paper ↗Financial Risk Management Suite
Eight projects spanning Component VaR, GARCH backtesting, Stulz multi‑asset options, CDS hazard bootstrapping, and the Merton structural credit model.
View Repo ↗Volatility Surface Explorer
Real‑time 3D implied vol surface with 10 color palettes, four Z‑metrics, smoothing, and side‑by‑side term structure & skew slice charts.
Launch App ↗Six years across data, product, and research.
Quantitative Researcher
Researching neural‑network‑weighted Monte Carlo to accelerate pricing of path‑dependent exotic options. Designing and backtesting alpha signals via WorldQuant BRAIN.
Quantitative Analyst
Built Python/SQL ETL for large structured‑finance portfolios. Engineered cash‑flow waterfall models for SPV securitized loans with dynamic loss curves.
Associate — Data & Product Management
Product owner for the Enterprise Data Warehouse; spearheaded ingestion and production updates for 1B+ rows of customer financial data. Refactored legacy PySpark/HIVE jobs from 24h → 3‑4h runtimes.
Associate — Data Analytics
Root‑cause analysis and rapid production resolution for data‑quality issues in Agile Scrum teams. Built Excel/VBA and Tableau dashboards for enterprise data health.
Let's talk
about markets.
Open to full‑time quantitative research and trading roles starting early 2026. Always happy to chat about derivatives pricing, volatility modelling, or anything that lives at the edge of code and markets.