Here is the List of keywords for the Quant role resume after analyzing 50+ Job Description for Equity derivative quant and Factor research Quant
Equity Derivative Quants at Investment Bank
Quantitative skills: Strong mathematical background, probability theory, statistics, numerical methods, stochastic calculus, and machine learning.
Programming skills: Python, C++, R, MATLAB, SQL
Analytical skills: Problem-solving, data analysis, hypothesis testing, research capabilities.
Technical Skills:
Equity derivatives: Options, futures, swaps, forwards, knock-in/knock-out options, barrier options, volatility derivatives.
Greek analysis: Delta, gamma, theta, vega, rho, and their interpretation for trading and risk management.
Models -Heston Model, Local volatility model, Model calibration, stochastic volatility models.
Numerical methods: Finite difference, finite element, Monte Carlo simulation for pricing and risk analysis.
Optimization: Linear programming, quadratic programming, constrained optimization for portfolio management and derivative structuring.
Specific Roles:
Front-office Quant: Pricing and risk analysis for derivative trading desks, developing trading strategies, backtesting and optimization of algorithms.
Structuring Quant: Designing and pricing customized derivative products for client needs, understanding client investment objectives and risk tolerance.
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Factor Research Quant
Statistical Modeling: Time series analysis, regression analysis, factor construction, anomaly detection, machine learning (e.g., regressions, trees, random forests, neural networks), dimensionality reduction techniques (e.g., PCA)
Econometrics: Empirical asset pricing, market microstructure, GMM estimation, VAR models, ARCH/GARCH models
Programming Languages: Python (libraries like Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow), R (libraries like dplyr, quantmod), C++, SQL
Data Management: Financial data wrangling, cleaning, and manipulation, Bloomberg experience
Specific Factor Research Skills:
Alternative data analysis: Analyzing non-traditional data sources (e.g., satellite imagery, web traffic, news sentiment) for alpha generation
Smart beta strategies: Factor investing, alpha factor identification and refinement, multi-factor models
Quantitative alpha analysis: Identifying and exploiting statistical anomalies in financial markets
Market microstructure considerations: Transaction cost analysis, impact of trading on prices, illiquidity adjustment
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