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Rohitku123/README.md

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Hi, I'm Rohit Kumar

Risk Modelling | Risk Analytics |Masters in Quantitative Finance | Python & R | Msc Financial Engineering

I am passionate about quantitative modeling, stochastic processes, and risk analytics.
I created simulations, risk models, and financial analytics tools to help make data-driven decisions in finance.

Skills & Tools

Programming & Data Analysis: Python, R, Excel, Pandas, NumPy, SciPy, Matplotlib, Seaborn, Plotly
Financial Modelling: Monte Carlo Simulation, Geometric Brownian Motion, VaR, CVaR, Option Pricing, Portfolio Optimization
Risk Analytics: Market Risk, Credit Risk, Liquidity Risk, Stress Testing
Version Control & Tools: Git, GitHub, Jupyter Notebook, VS Code

Projects & Highlights

1. Monte Carlo GBM Simulation

Simulates thousands of potential stock price paths using Geometric Brownian Motion, calculates mean, median, std, percentiles, and probabilities, and visualizes outcomes dynamically.

GBM Simulation GIF
Repository Link

2. Credit Risk Modelling

Models Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD). Calculates expected credit loss for portfolios.

Credit Risk Dashboard
Repository Link

3. Market Risk / Value at Risk (VaR)

Implements Parametric, Historical, and Monte Carlo VaR, calculates Conditional VaR, and visualizes portfolio risk metrics.

VaR Simulation
Repository Link Repository Link

4. Option Pricing Models

Implements Black-Scholes, Binomial Tree, and Monte Carlo option pricing and calculates Greeks for sensitivity analysis.

Option Pricing
Repository Link

5. Multivariate-Time-Series-Analysis

Python modules for fetching data, Forcasting Time Series Analysis, and creating ARCH GARCH ARIMA/SARIMA-Model using R.

Time-Series-Analysis
Repository Link

Portfolio Highlights

  • Simulated MSFT stock paths over 6 months using GBM
  • Estimated drift (μ ≈ 17%) and volatility (σ ≈ 26%) from historical data
  • Monte Carlo simulation generated 10,000 paths, visualized dynamically
  • Extracted statistical metrics for risk assessment and investment planning

Key insight: Even with positive drift, volatility creates significant variation; probability of being above current price ≈ 64%, while short-term daily fluctuations can be negative ~49% of the time.

Certifications & Courses

  • Advanced Python for Finance / Quantitative Modelling
  • Data Science & Machine Learning
  • Risk Management FRM (Infosys_Certification course)

Contact

Fun Fact

I enjoy turning complex financial models into interactive simulations to understand risk in a visual and intuitive way.

Badges

Python GitHub Repo License Risk Analytics

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  1. VaR-CVaR-StressTest-BankPortfolio VaR-CVaR-StressTest-BankPortfolio Public

    Value at Risk (VaR), Conditional Value at Risk (CVaR), and Stress Testing using monte Carlo and parametric methods on a portfolio of four international banks:

    Jupyter Notebook 1

  2. -Monte-Carlo-Simulation-of-Stock-Prices-using-Geometric-Brownian-Motion-GBM- -Monte-Carlo-Simulation-of-Stock-Prices-using-Geometric-Brownian-Motion-GBM- Public

    Monte Carlo Simulation of Microsoft Stock using GBM SP and Python

    Jupyter Notebook 1

  3. VaR-Computation-and-Risk-Assessment VaR-Computation-and-Risk-Assessment Public

    VaR calculation using Parametric and Non Parametric Approach in Excel

  4. Multivariate-Time-Series-Analysis Multivariate-Time-Series-Analysis Public

    Timeseries Analysis using R

    R 1

  5. Credit-Risk-Modelling Credit-Risk-Modelling Public

    Jupyter Notebook

  6. Quant_resources Quant_resources Public

    Forked from Ricktho1/Quant_resources

    Drop your resources for quant and related to that here