The realized volatility (RV) financial time series is non-linear, volatile, and noisy. It is not
easy to accurately forecast RV with a single forecasting model. This paper adopts a hybrid …

Over the past two decades, artificial intelligence (AI) has experienced rapid development
and is being used in a wide range of sectors and activities, including finance. In the …

In this systematic literature review, we examine the existing studies predicting realized
volatility and implied volatility indices using artificial intelligence and machine learning. We …

We inspect how accurate machine learning (ML) is at forecasting realized variance of the
Dow Jones Industrial Average index constituents. We compare several ML algorithms …

This paper investigates the application of neural basis expansion analysis with exogenous
variables (NBEATSx) in the prediction of daily stock realized volatility for various time steps …

This paper investigates how geopolitical risks influence the prediction performance on the
US stock market volatility with machine learning models. Further, it compares the predictive …

Unstructured multimedia data (text and audio) provides unprecedented opportunities to
derive actionable decision-making in the financial industry, in areas such as portfolio and …

Abstract Machine learning approaches have become very popular in many fields in this big
data age. This paper considers the problem of forecasting realized volatility with machine …

Since the breakdown of the Bretton Woods system in the early 1970s, the foreign exchange
(FX) market has become an important focus of both academic and practical research. There …

We apply machine learning models to forecast intraday realized volatility (RV), by exploiting
commonality in intraday volatility via pooling stock data together, and by incorporating a …