Single-Stock Case Study: NVIDIA (NVDA)

Level: Medium
Goal: Univariate forecasting and volatility exploration for a single stock.

Dataset

• Source: NVIDIA (NVDA) Historical Stock Price Data – Kaggle
• Link: https://www.kaggle.com/datasets/elnazalikarami/nvidia-corporation-stock-historical-quotes

Download Instructions

1. Open the dataset link above.
2. Click "Download".
3. Extract to data/.
4. Use the main CSV (e.g. HistoricalData_....csv).

Data Loading

import pandas as pd

df = pd.read_csv("data/NVDA_HistoricalData.csv")  # adjust filename
df["Date"] = pd.to_datetime(df["Date"])
df = df.set_index("Date").sort_index()

Implementation Steps

1. Data Exploration

• Load NVIDIA stock price data
• Inspect OHLC (Open, High, Low, Close) and Volume data
• Focus on Close price for forecasting
• Calculate returns (simple and log returns)
• Examine trading volume patterns

2. Exploratory Data Analysis (EDA)

• Plot stock price over time
• Plot returns distribution
• Analyze volatility clustering
• Calculate and visualize rolling statistics (mean, std)
• Perform time series decomposition
• Analyze ACF/PACF of prices and returns

3. Volatility Analysis

• Calculate realized volatility (rolling standard deviation of returns)
• Identify volatility regimes (high vs low volatility periods)
• Analyze volatility clustering (GARCH-like patterns)
• Plot volatility over time

4. Stationarity Analysis

• Test stock prices for stationarity (typically non-stationary)
• Test returns for stationarity (typically stationary)
• Apply differencing to prices if needed
• Consider log transformation

5. Model Building

• Price Forecasting:
  • ARIMA models on prices or log prices
  • Consider trend models
• Returns Modeling (optional):
  • ARMA models on returns
  • GARCH models for volatility (if time permits)
• Model Selection:
  • Use AIC/BIC for comparison
  • Validate on hold-out set

6. Model Evaluation

• Split data temporally (e.g., last 20% as test)
• Generate price forecasts
• Calculate accuracy metrics (MAE, RMSE, MAPE)
• Analyze forecast errors
• Visualize forecasts with confidence intervals

7. Volatility Forecasting (Advanced)

• If implementing GARCH:
  • Model volatility dynamics
  • Forecast future volatility
  • Compare with realized volatility

Expected Deliverables

1. EDA Report:

   • Stock price and returns plots
   • Volatility analysis
   • Decomposition plots
   • ACF/PACF analysis

2. Model Results:

   • Selected model with parameters
   • Price forecast accuracy
   • Volatility analysis (if applicable)
   • Forecast plots

3. Code:

   • Complete Python notebook
   • Functions for price/returns analysis
   • Volatility calculation utilities

Tips

• Stock prices are typically non-stationary; use returns or differencing
• NVIDIA is a tech stock with high volatility - expect large price swings
• Consider external events (earnings, product launches) that affect prices
• Volatility clustering is common in stock returns
• Use log returns for better statistical properties
• Consider multiple forecast horizons (1-day, 1-week, 1-month)
• Document any major events (splits, earnings) that affect the series