Risk Analyst Management

RISHITA TYAGI

New York, NY (willing to relocate) 862-***-**** ***********@*****.*** LinkedIn WORK EXPERIENCE

Quantitative Analyst AccelUp Cloud New York, NY Sep 2024 – Present Variance Covariance Value at Risk Model — Tail Risk Quantification & Back Testing

• Engineered and validated a Variance-Covariance Value-at-Risk (VaR) model to quantify potential portfolio losses at a 95% confidence interval, enhancing risk prediction accuracy by 20% through rigorous mean-variance optimization and statistical distribution analysis.

• Implemented Historical Simulation VaR methodology by analyzing multi-year market data to identify extreme loss scenarios, strengthening the firm’s tail-risk identification and scenario-analysis capabilities.

• Conducted comprehensive back-testing of VaR and Stressed VaR (SVaR) models using Basel III traffic-light thresholds and Kupiec proportion-of-failures tests, executing stress tests to ensure regulatory compliance and model robustness. Probability of Default Modeling and Scorecard Development Using Lending Club Loan Data

• Extract, transform and load (ETL) dataset from 2007-2015, featuring 900k rows and 75 columns. Performed data cleaning, feature engineering feature selection on the dataset and trained logistic regression model to predict the probability of default.

• Achieved ROC AUC of 0.702, with a Gini coefficient of 0.404 and a KS Statistic of 0.29, indicating robust model performance.

• Developed credit risk scorecard based on the regression model, effectively calculating the probability of default within a credit score spectrum of 300-850.

Pricing European Options Using Black-Scholes, Binomial Tree, and Monte Carlo Simulations

• Developed and implemented three quantitative pricing models—Black-Scholes analytical formula, multi-step Binomial Tree, and Monte Carlo simulation—to price European call and put options and perform cross-model accuracy validation.

• Critically evaluated Black-Scholes assumptions (constant volatility, log-normal returns, no arbitrage, continuous trading) and quantified the impact by relaxing these assumptions in Binomial Tree and Monte Carlo frameworks.

• Led a rigorous comparative analysis of model performance—measuring computational complexity, convergence speed, pricing accuracy, and scalability demonstrating Monte Carlo’s superiority for complex payoffs, Binomial Tree’s strength in path-dependent features, and Black-Scholes’ efficiency for standard option valuations.

Graduate Research Assistant Rutgers Business School Newark, NJ Jan 2024 – May 2024 Regression Analysis of GE Stock Price Using Technical Indicators

• Utilized econometric techniques in R-studio for linear regression and correlation analysis on GE stock price returns as the dependent variable with 25 technical indicators as independent variables.

• Assessed and validated regression assumptions such as linearity, independence, homoscedasticity, and normality of residuals using scatter plots, residual plots, Q-Q plots, and histograms, ensuring robust and reliable model parameters for accuracy.

• Improved model fit by systematically eliminating non-significant predictors, achieving a final R-squared of 0.95. Fixed Income Yield Curve in Excel

• Formulated forward curves for 3-month T-bills using bootstrapping in Excel, constructing yield curves based on bond yields in a rising rate environment for precise interest rate risk assessment and portfolio management.

• Applied DV01, PV01, and Duration hedging strategies to minimize the portfolio’s sensitivity to interest-rate changes and align exposures with risk-management objectives.

Presentation on SR 11-7 Framework for Model Risk Management

• Presented an overview of the SR 11-7 regulatory framework, outlining supervisory expectations and the need for structured Model Risk Management (MRM) in financial institutions.

• Explained the model lifecycle development, implementation, and use—focusing on conceptual soundness, data integrity, assumptions, limitations, and practical applications.

• Emphasized independent model validation, including benchmarking, back testing, and sensitivity analysis to ensure model accuracy and reliability.

• Covered ongoing performance monitoring, threshold breaches, and exception reporting to maintain model effectiveness over time.

• Highlighted model governance and risk controls, stressing policy frameworks, the role of the Model Risk Committee, regulatory compliance, and internal audit oversight.

Quantitative Research Analyst AnalytixInsight Toronto, CN May 2023 – Aug 2023

• Constructed and implemented machine learning models to analyze financial data, enhancing the capabilities of AnalytixInsight's platform, and improving valuation accuracy by 18%.

• Incorporated quantitative methods to optimize investment portfolios providing actionable insights to clients and refining investment strategies.

• Conducted risk assessments via statistical modeling & stress testing, increasing model robustness by 20%.

• Advised financial institutions to customize AnalytixInsight's solutions, ensuring alignment with client-specific investment strategies and regulatory requirements.

Market Risk Analyst Centricity Delhi, India Aug 2021 – Jul 2022

• Devised an AI-driven asset allocation model improving annualized portfolio returns by 12%, maintaining a volatility threshold below 8%.

• Led a team of analysts in enhancing alternative investment selection criteria, resulting in a 20% increase in risk-adjusted returns for UHNWI client portfolios.

• Designed a real-time trade execution strategy, reducing transaction costs by 5.5% through optimal trade scheduling algorithms.

• Presented quantitative research reports to Centricity’s investment committee, leading to the adoption of factor-based risk premia strategies across portfolios.

EDUCATION

Master of Science in Quantitative Finance Rutgers Business School Newark, NJ Aug 2022 – May 2024 Relevant coursework: Advanced Derivatives, Fixed Income Analysis, Computational Finance (Interest Rate Risk (IRR), Monte Carlo Simulation), Stochastic Calculus (Martingale, Ito’s Lemma, Markov Chains),Financial Engineering Practicum (Machine Learning, SR 11-7, SR 15-19 for Model Risk), Statistical Models, Financial Risk Management (VaR, Expected Shortfall, Stress Testing, Sensitivity Analysis, Duration, Convexity, Greeks (delta, theta, vega, gama, rho), Credit Risk Modeling (Counterparty Credit Risk, Credit Default Swaps, Regulatory Compliance (Basel III, Dodd-Frank, CCAR), Time Series Models (AR, MA, ARIMA, GARCH)

Bachelor’s in Engineering (Electronics and communication) Manipal University Jaipur, India Aug 2018 – May 2022 SKILLS & PROFICIENCIES

Technical Skills: Python (NumPy, pandas, matplotlib, QuantLib, yfinance, Jupyter Notebook), R, SQL, Bloomberg, MS Office, Advanced Excel Modeling: Option Pricing Models, Linear/Logistic Regression, CAPM, Fama French 3/5 Factor Model, BDT, Hull-White Model

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