I’m a quantitative economist with an M.A. in Economics (STEM Designated) from Hunter College, specializing in financial econometrics, time-series forecasting, and statistical analysis. I leverage Python to conduct rigorous data analysis, build predictive models, and synthesize complex financial datasets into actionable insights.
My research focuses on volatility modeling and machine learning applications in financial markets, with particular expertise in LSTM neural networks for forecasting and robust ML methodologies for high-kurtosis time series data. I bridge the gap between quantitative theory and practical application, utilizing a deep understanding of market dynamics to support equity valuation, risk assessment, and portfolio strategy.
Tutoring Services
I offer private tutoring in Econometrics, Calculus, and Macroeconomics through Wyzant. I tutor undergraduate students in econometrics and macroeconomics, helping them master OLS estimation, time-series analysis, and statistical modeling.
Education
M.A., Economics (STEM)
Data Science Bootcamp
B.S., MathematicsExperience
- Tutored undergraduate students in complex econometric and statistical concepts, improving their analytical reasoning and problem-solving skills in quantitative economics
- Performed rigorous analysis of on-chain data and protocol infrastructure to inform discretionary investment theses for Bitcoin options and broad market index futures
- Actively managed a portfolio of complex financial derivatives, gaining hands-on experience in portfolio construction and risk management
- Championed development of a critical internal GUI, translating complex operational workflow into an elegant product solution that enhanced efficiency by 75%
- Wrote detailed technical PRDs to guide engineering team through product development lifecycle
- Spearheaded complete UI/UX overhaul of platform's core service, collaborating with cross-functional teams to replace manual process with intuitive interface
Research Interests
Tail Risk Investing
Investigating tail risk hedging strategies for Bitcoin and digital assets, examining how options-based approaches can protect against extreme drawdowns in cryptocurrency portfolios.
Commodities Pricing
Modeling crude oil prices as a function of geopolitical risk indicators, incorporating sanctions, OPEC decisions, and supply disruption events into quantitative pricing frameworks.
Macroeconomic Research
Analyzing the interlinked dynamics of CPI, GDP growth, global trade flows, and energy markets to build unified forecasting models that capture cross-market spillover effects.
Prediction Market Modeling
Developing QRF-Bias models to identify and exploit systematic biases in weather prediction markets, applying quantile regression forests to calibrate probabilistic forecasts against market prices.