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Quantitative Analyst

Harrison, New Jersey, United States
February 07, 2019

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Rohitashav Agarwal

Harrison, NJ-*****



Financial services professional, with expertise in data processing, quantitative analysis and model development in a variety of situations related to finance, including risk management, portfolio construction and optimization, and pricing models. EDUCATION

Rutgers Business School Newark, USA

Master of Quantitative Finance August 2017-December 2018 Coursework: Derivatives, Fixed Income, Financial modeling, Object-oriented programming, Stochastic Calculus, Econometrics National Institute of Technology Silchar, India

Bachelor of Civil Engineering July 2013-May 2017


Modeling Skills: OLS, Logistic model, Fixed/Random effect, Multinomial logistic, GLM,VAR, ARIMA, ARCH, GARCH Programming Skills: Python (Pandas, numpy, dask, cvxopt), R (ggplot2, dplyr, nlme, zoo), MATLAB, SQL EXPERIENCE

ETFication, Inc. New York, USA

Intern, Financial Engineer Fall 2018

• Developed multifunctional efficient frontier in Python, using libraries CVXOPT, Pandas, NumPy based on Markowitz theory for the platform that allows users to optimize their portfolios by inputting risk level, expected return level and weight constraints.

• Improved communication between founder and remote team in Indian ensuring thorough translation. Verisk Analytics New Jersey, USA

Quantitative Risk Intern (Credit Risk) ` Summer 2018 Part of team that developed Probability of default model for CECL requirements issued by FASB to estimate bank’s losses

• Aggregated Loan-Level dataset by merging origination and performance attributes of 37 million Loans using AWS

• Prepared structured dataset from the sample and added MSA level Macroeconomic Factors to it.

• Analyzed correlations and distributions of independent variables and transformed/Standardized them for feature engineering

• Followed CECL developments and understood implications of different model methodology while developing Multinomial Logistic Model to estimate PD Time series and calculate lifetime expected loss using DCF methodology

• Responsible for developing end-to-end CECL documentation by working closely with credit, modeling and finance professionals

• Effectively communicated inherent model risks, limitations, findings and conclusions to Executives and Designated Professor. Au Finance Limited Jaipur, India

Intern, NBFC Funding Department Summer-2016

• Determined loan rate and the term structure of the NBFC’s based on analysis of irregularities in P/L and balance sheet, key changes in financial ratios in the last four years, and the due diligence report

• Performed in-depth financial and operational due diligence on client companies.

• Analyzed company’s operating model drivers, CIBIL scores of management profile, litigation liabilities, and recent news coverage ACADEMIC PROJECTS

Implementation and calibration of the Heston Stochastic Volatility Model in MATLAB Fall 2018

• Priced vanilla options through the Characteristic Function of Heston Model (Closed-form analytical solution)

• Calibration of five unknown parameters in Heston Model to Market Prices by optimization scheme

• Implemented a trust-region-reflective minimization algorithm so that the difference between model and market prices falls on average within the observed bid-ask spreads

On biases in the measurement of foreign exchange risk premiums (Testing Bekaert and Hodrick (1993) Hypothesis) Fall 2018

• Tested hypothesis that the forward rate is an unbiased predictor of the future spot rate using the weekly dataset of yen/pound against the dollar- spot ex. rate, 30-day forward ex. rate, the spot rate on the delivery date on a 30-day forward contract from 1975-1989.

• Performed unconditional test using the asymptotic distribution of the sample mean of the forward premium

• Tested market efficiency hypothesis using regression tests on the developed model and rejected it decisively. Peer to Peer Lending Default Probability Predication `` Spring 2018

• Visualized relationship between Defaults and input factors using Tableau to understand the outcome of the model

• Built a logistic model to predict default using independent variables like interest rate, debt-income ratio, grade, annual income

• Trained and Tested time series models(ARIMA) for consumer price index, unemployment rate, and fed fund rate.

• Incorporated macroeconomic indicators time series in the PD model for better AIC values Understanding Current Expected Credit Loss Accounting standard Spring 2018

• Developed Vector Autoregressive model to forecast the United states economy and analyses impulse responses

• Estimated Probability of Default for the consumer loan data(97000 consumer loans) using a logistic model

• Stress tested default rate considering Baseline, Adverse, Severe condition of the economy and stock market

• Presented key differences between CECL and IFRS 9, Requirement and challenges for CECL modeling

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