In this paper, we are interested in exploring the role of price impact, derived from the order book, in modeling and predicting stock volatility. This is motivated by the microstructure literature that focuses on the mechanics of price formation and its relevance to market quality. Using a...

This study employs machine learning models to forecast and comprehend the implied volatility of China ETF50. We develop a hybrid model named LSTM-ML, leveraging historical implied volatility, moneyness, and time-to-maturity as input features. The LSTM component captures dynamic hidden...