New model predicts stock market volatility with long-term accuracy.
The article explores a model that combines leverage and long memory to predict stock market volatility. By using a mixture of normal distributions and an ARFIMA process, the researchers estimate volatility efficiently. They compare models using marginal likelihood and find that their method improves volatility forecasting for the S&P500 stock index.