, 60, and 300 seconds), forecast horizons (1, 5, 22, and 66 days) and the use of standard and robust-to-noise volatility … forecasting the volatility of equity prices, using high-frequency data from 2000 to 2016. We consider the SPY and 20 stocks that … real-time forecasts than the HAR-RV model, although no single extended model dominates. In general, standard volatility …

In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in … of covariates as well as the smoothing parameters via cross-validation. We find that volatility forecastability is much …

-switching models, and forecast combination to predict the dynamics in the S&P 500. First, we aggregate the weekly information of 115 …

An accurate forecast of intraday volume is a key aspect of algorithmic trading. This manuscript proposes a state …-space model to forecast intraday trading volume via the Kalman filter and derives closed-form expectation-maximization (EM …

This paper considers forecast averaging when the same model is used but estimation is carried out over different … estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or … estimation windows leads to a lower bias and to a lower root mean square forecast error for all but the smallest of breaks …

This paper considers forecast averaging when the same model is used but estimation is carried out over different … estimation windows. It develops theoretical results for random walks when their drift and/or volatility are subject to one or … estimation windows leads to a lower bias and to a lower root mean square forecast error for all but the smallest of breaks …

economic emergencies. This paper aims to forecast the short to medium-term incidence of COVID-19 epidemic through the medium of … an autoregressive integrated moving average (ARIMA) model, applied to Italy, Russia, and the USA The analysis is ….worldometers.info/coronavirus/). The best ARIMA models were Italy (4,2,4), Russia (1,2,1), and the USA (6,2,3). The results showed that: i) ARIMA models …

bad) stock market volatility, we show that incorporating the information in lagged industry returns can help improve out …-of sample forecasts of aggregate stock market volatility. While the predictive contribution of industry level returns is not … crisis, highlighting the informational value of real economic activity on stock market volatility dynamics. Finally, we show …

I apply the model with unobserved components and stochastic volatility (UC-SV) to forecast the Russian consumer price …