Rounak Bastola

ANALYST

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Skills

Production ML Data Pipelines Model Ops Model Calibration & Predictive Engines Risk & Financial Forecasting Production Machine Learning Neural Networks & Deep Learning Predictive & Regression Modeling Forecast Optimization NLP Automation Financial Forecasting & Valuation Modeling Data Pipelines & ETL Workflows Data Aggregation & Structured Data Management Compliance Auditing & Data Validation Python Scripting SQL Querying & MySQL Optimization Power BI & JavaScript Dashboards Option Pricing & Derivative Modeling Exotic Instruments & CDS Analysis Monte Carlo & Stochastic Simulation Ornstein-Uhlenbeck, Hull-White, and CIR Frameworks Valuation Engines (CAPM, DDM, and Empirical Models) Behavioral Modeling AI-Driven Decision Systems M&A Due Diligence & Peer Benchmarking Portfolio Analysis & Strategic Reporting Cost & Performance KPI Analysis Bloomberg & Capital IQ Integration Trade Systems Development KPI & Report Automation VBA Automation & Excel Modeling PHP Development Excel Workflow Optimization Financial Reporting & Strategic KPIs Compliance Auditing & Quality Assurance Automated Reporting Systems
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Value-at-Risk image

Value-at-Risk

Python R GARCH Historical Simulation Quantitative Finance

This study conducts a comprehensive comparison of eleven Value-at-Risk (VaR) models to evaluate their effectiveness in estimating market risk for 473 S&P 500 stocks from 1962–2023, encompassing over 3.2 million data points.

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