About

Abhinav Agrawal is a driven and passionate individual with a strong background in mathematics and finance. He is currently pursuing a Masters in Quantitative and Computational Finance at Georgia Tech, where he has developed a set of skills that include mathematical, programming, analytical, and finance skills. With an undergraduate degree in Computer Science Engineering, Abhinav has a solid quantitative foundation that he has leveraged in his professional journey so far. Abhinav has hands-on experience with Python and C++, using these programming languages to build data pipelines and machine learning models. He has also worked on building delta neutral strategy and statistical arbitrage models. Abhinav's analytical skills are extensive, having built analytical dashboards and predictive models for email marketing performance. Additionally, he has hands-on trading experience in different asset classes, including US Commodities, Indian Equity, and Cryptocurrencies. Currently working as a Quantitative Engineer at ICE, Abhinav has developed an Implied Distribution model for pricing CDS options by calibrating the tail parameters of the student t-test distribution of the spreads over simulated price scenarios. He has also optimized the price difference between the Clearing Participant submitted price and model calibrated price by building a parabolic spline optimizer to minimize the score with golden section search as a fall-through mechanism. Abhinav has worked on linear risk factor construction (top-day and return) using forward curves and implemented a price fill algorithm to fill in the settlement prices of unavailable contracts based on its relation to other contracts. He has also determined the Univariate Statistics and Multi Period Correlation (COPULA) of the distribution of the spreads on CDS. Abhinav is seeking a full-time role in quantitative finance for the Fall of 2023. He can be reached out at aagrawal372@gatech.edu or +14707755651.