Credit Risk, Machine Learning

HARSHIL ZAVERI

ad0fyw@r.postjobfree.com 732-***-**** LinkedIn

EDUCATION

NORTH CAROLINA STATE UNIVERSITY Raleigh, NC

Master in Financial Mathematics 12/23

GPA: 3.61/4.0

MUMBAI UNIVERSITY Mumbai, IND

Bachelor of Engineering in Computer Engineering 05/22 Minor: Financial Data Analytics- IBM

GPA: 3.6/4.0

TECHNICAL SKILLS AND CERTIFICATIONS

● Programming: Python, SQL, R, MATLAB, C/C++.

● Technologies: Tableau, Power BI, Hadoop, Excel (Macros, Pivot Tables, VLOOKUP), Word, PowerPoint.

● Certifications: Credit Risk Modeling, Financial Trading, Quantitative Risk Management in R (Data Camp), Financial Markets by Yale University (Coursera), ISB Trading Algorithms (Coursera). COURSEWORK HIGHLIGHTS

Credit Risk Modeling, Quantitative Trading, Monte Carlo Methods for Financial Math, Options and Derivatives Pricing, Fixed Income, Financial Risk Analysis, Financial Statistics and Data Science, Calculus I, II, & III, Mathematical Statistics, Probability & Distribution Theory, Big Data, Machine Learning, Data Structures and Algorithms. RELEVANT EXPERIENCE

JP MORGAN CHASE Remote, USA

Interest Rate Derivatives, Summer Project 05/23 – 08/23

● Studied and analyzed various interest rate derivative instruments, including Range Accrual Options, Snowball Options, and Bermudian Swaptions, to gain insights on their pricing methodologies and risk profiles.

● Used one factor, two factor and exogenous short rate models and market models like the Vasicek model, G2++, and Libor Market Model to predict the behavior of the interest rates.

● Utilized the Quantlib Library to implement complex interest rate derivative structures, enhancing proficiency in Python and Quantitative Modeling.

PSYBER TECHNOLOGIES Mumbai, IND

Data Analyst Intern 05/20 – 07/20

● Used Python and Tableau to convey complicated datasets to the non-technical employees and 5000 users.

● Implemented K-means Clustering to form clusters helping users identify their appropriate difficulty level based on the completion rate of the course, background knowledge of students, and other variables.

● Identified the market trends and helped the marketing team improve marketing campaigns by 40%. ACADEMIC PROJECTS

PROBABILITY OF DEFAULT MODEL 10/22 - 12/22

Department of Financial Mathematics, North Carolina State University

● Imported, cleaned, and sampled more than 1 million loan performance data records from Fannie Mae.

● Applied PCA to choose reverent variables and explained economics reasons.

● Compared the Logistic Regression and XGBoost Models using Precision, Accuracy and corresponding Area Under the Curve (AUC) and found that XGBoost outperforms Logistic Regression in all three evaluation indicators.

● Scored for non-standardized loan data by survival analysis to optimize the credit risk model. PAIRS TRADING 08/22 – 12/22

Department of Financial Mathematics, North Carolina State University.

● Filtered banking stocks in NYSE based on co-integration and paired stocks by correlation.

● Using Dicky-Fuller test to check for the stationarity of the spread and Bollinger bands to set the trading signals.

● Simulated the model on diversified portfolio and realized CAGR of 23% and Maximum Drawdown of 28%. STOCK MARKET PRICES AND RETURNS FORECASTING 01/22 - 04/22 Department of Computer Engineering, Mumbai University

● Explored Machine Learning, Time Series and Deep Learning Methods to predict the movement and change in the stock price value and compare the model using their RMSE values.

● Used Macroeconomic factors such as VIX, GOLD Rate, TIPS and others to predict the value of the stock price.

● Identified that LSTM Model as the better performing model compared to Linear Regression, ARIMA and KNN modeling based on mean squared error and directional accuracy.

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