Measurement and forecasting of volatility and income correlation are achieved by non-parametric methods using high-frequency price data. Due to accurate calculations of conditional volatility and correlation forecasting, it is possible to correctly identify financial derivatives and make risk decisions and relative asset allocation decisions. This article systematises the methods for modelling the volatility of financial asset returns, considers the theoretical foundations of the generalised autoregressive conditional heteroscedasticity model, and predicts and analyses the volatility of US stock indices and stocks using high-frequency volatility estimates (realised volatility indicators). The stock indices studied are the Dow Jones Industrial Average (DJI), Standard and Poor’s 500 (SP500), and the Nasdaq Composite Index (NASDAQCOMP). Stocks analysed include stocks in Microsoft, Bank of America, and Coca-Cola. The results of the study support conclusions regarding the effectiveness of volatility estimators within two Bank of America volatility forecasting models, the superiority of the HAR-RV model for trading options in a specific market, and the best model for Microsoft. Thus, systematic analysis of news information is useful for predicting the volatility of returns on financial assets, but its effectiveness depends on the individual company. Future studies should explore the usefulness of the systematic analysis of news information in predicting the volatility of returns on financial assets in other markets and for other asset classes.