Quants

Quants, short for quantitative analysts, are professionals who apply mathematical, statistical, and computational methods to financial markets. They design, implement, and validate models used to price securities, assess risk, and guide trading and investment decisions.

Common domains include derivatives pricing, fixed income, equities, commodities, and risk management. Quants work in investment banks, hedge funds, asset managers, and proprietary trading firms, often alongside traders, risk managers, and software developers.

Core methods include stochastic calculus, time-series analysis, machine learning, Monte Carlo simulation, and optimization. They calibrate models to market data, backtest, and implement risk controls and governance.

Popular models and techniques include Black-Scholes, stochastic volatility models, interest-rate models, term-structure models, copulas for credit risk, value-at-risk, and stress testing.

Specializations include pricing quants, risk quants, research quants, execution quants, and quantitative developers (q-devs). The field is data-intensive and highly collaborative with traders and technologists.

Education is typically advanced degrees in mathematics, physics, engineering, or statistics, with strong programming skills in Python, R, C++, or MATLAB.

The rise of quants began in the late 20th century with the development of financial engineering and algorithmic trading; model risk and regulation have since become central concerns for practitioners and supervisors.