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Volatility Forecasting with LSTM

This project utilizes a Long Short-Term Memory (LSTM) neural network to forecast volatility in financial time-series data.

Overview

The model leverages the power of LSTMs, a type of recurrent neural network, to capture long-term dependencies in financial markets and predict future volatility.

Math Logic

  • LSTM for Volatility: A Long Short-Term Memory (LSTM) neural network is used to forecast volatility. LSTMs are a type of recurrent neural network well-suited for time-series data like financial markets, as they can learn patterns over extended periods.
  • Volatility Calculation: Volatility is calculated using a rolling window, meaning it's based on the average price fluctuations over a set past period (e.g., 150 days). This provides a dynamic measure of how much an asset's price varies.
  • Mean Squared Error: The model is trained to minimize Mean Squared Error (MSE). This means the algorithm tries to make the difference between its predicted volatility and the actual volatility as small as possible.

Software Algorithm

  • Python Tools: Common Python libraries for data science and machine learning are employed, such as:
    • pandas: For data manipulation and analysis.
    • NumPy: For numerical computations.
    • PyTorch: A deep learning framework for building and training neural networks.
    • Matplotlib: For creating visualizations of the data and results.
  • Data Preparation: Stock price data is loaded (likely from a CSV file or similar). This data is then processed to calculate the daily rate of change and the volatility time series, which are used as inputs for the model.
  • Model Training: The LSTM network is trained using an optimizer (like Adam) that adjusts the model's parameters to minimize the MSE loss. This process involves feeding the model historical data and iteratively improving its predictions.
  • Forecasting: Once trained, the model can forecast volatility for a future period (e.g., the next 30 days) based on the patterns it has learned from the historical data.
  • Visualization: The predicted volatility is plotted against the actual (historical) volatility to assess the model's performance and visualize the forecast.