Quantitative Research Equity Derivatives

$200,000 - $285,000/Yr

JPMorgan Chase - New York, NY

Full-time - Mid Level

Remote - New York, NY

Credit Intermediation and Related Activities

About the position

The Quantitative Research Equity Derivatives position at JPMorgan Chase & Co. involves applying advanced quantitative methods to develop and maintain models for pricing, hedging, and risk management of equity derivatives. The role requires collaboration with trading teams to analyze risks and trading opportunities, implement high-performance computing solutions, and ensure the accuracy of quantitative methodologies.

Responsibilities

• Apply stochastic process, finite difference and Monte-Carlo methods, probability theory, and other quantitative methods to design, implement, and maintain quantitative models for equity derivatives.
• Design efficient numerical algorithms and implement high performance computing solutions.
• Implement risk measurement and valuation models in trading software and systems.
• Analyze and interpret statistical data to identify driving factors, major risks, and trading opportunities in the equity exotics market.
• Explain model behavior to traders and assist them in using quantitative tools.
• Identify major sources of risk in portfolios and conduct scenario analyses.
• Provide back test and analysis in trading strategies.
• Support the equity exotics trading desk with day-to-day risk decomposition and P&L explanation.
• Resolve pricing failures and booking issues.
• Evaluate quantitative methodologies and assess their appropriateness for valuation and risk management.

Requirements

• Master's degree in Financial Engineering, Engineering, Mathematics, Statistics, Physics, or related field.
• One year of experience in Quantitative Research Equity Derivatives or related occupation.
• Experience in probability theory and statistics.
• Knowledge of stochastic process and stochastic calculus.
• Proficiency in numerical methods including Monte-Carlo, PDE and Tree engines, Euler and Milstein schemes, and random number generation.
• Familiarity with linear and non-linear optimization methods.
• Application of data analytics and machine learning techniques to derivatives pricing.
• Valuation and modeling of exotic derivative options and equity exotic products.
• Experience in delivering tools to trading and structuring teams.
• Building models that facilitate the trading desks' hedging practices.
• Proficiency in C++ and Python, including design patterns, data structures, and containers.

Benefits

• Telecommuting permitted up to 20% of the week
• Competitive salary ranging from $200,000 to $285,000 per year