Volatility Modeling and Forecasting in Financial Markets

Finance Tech Brief By HackerNoon

This story was originally published on HackerNoon at: https://hackernoon.com/volatility-modeling-and-forecasting-in-financial-markets.
Uncover the significance of implied volatility, and how historical data, coupled with sophisticated models, is reshaping the landscape of options trading.
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Options market pricing depends on the implied volatility and is priced based on how the volatility forecast would look like in the future. This story focuses primarily on predicting the volatility with time series models in S&P500 index based on historical data. We use the S&P500 data from Yahoo finance for the years 2000-2022. Implied volatility is forecasted based on ARIMA and GARCH models to capture the volatility shocks and the lag. Several exogenous variables such as bond yields, trading volumes and SP500 returns were used to train the model. Directional accuracy of the implied volatility is tested in the time series model with ARIMA (1,1,1) regression along with macroeconomic exogenous variables. A successful model was developed which predicted the directional change in VIX and if implemented would help us in pricing the options accurately.