Building a Machine Learning-based Volatility Prediction Model

Project information

“Volatility is the backbone of finance in the sense that it not only provides an information signal to investors, but it also is an input to various financial models” (Karasan, n.d) 4.

Volatility can be seen as uncertainty or risk in the financial market. High-volatility stocks have prices that fluctuate greater and more frequently, and higher stock market volatility often translates to greater investment risks. As a result, modelling and forecasting volatility becomes an important strategy when navigating the stock market.

This project will explore the different models of volatility prediction, first, using GARCH(p,q) model, and then LSTM-GARCH hybrid models. This study therefore considers the volatility of the data_ml stock index returns.

neural networks time-series R-Studio

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