the financial crisis, suggesting that an extreme volatility period requires models that can adapt quickly to turmoil …

more than a single regime, have performed substantially better than standard methods in terms of volatility and Value … individual models, we evaluate the use of forecast combinations strategies. In our empirical application, procedures that are …

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 …

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 …

We forecast the realized and median realized volatility of agricultural commodities using variants of the Heterogeneous …-sample analysis shows that the variants of the HAR model which decompose volatility measures into their continuous path and jump … volatility decomposition or relative transformations of volatility, in the forecasting models …

Forecasting realized volatility in exchange rates is very important for both practitioners and academics. Our aim is to … realized volatility. We employ four widely traded currencies, namely GBP, CHF, YEN and EUR and we also construct a basket of … learning, dimensionality reduction, forecast combination and amalgamation approaches. Our results highlight the predictive …

We investigate the question of whether macroeconomic variables contain information about future stock volatility beyond … that contained in past volatility. We show that forecasts of GDP growth from the Federal Reserve's Survey of Professional … Forecasters predict volatility in a cross-section of 49 industry portfolios. The expectation of higher growth rates is associated …

We introduce a novel quantitative methodology to detect real estate bubbles and forecast their critical end time, which … possible turning points of the markets in BeiJing, ShangHai, ShenZhen, GuangZhou, TianJin and ChengDu and forecast the future …

This paper proposes a novel algorithm called Persistent Homology for Realized Volatility (PH-RV), which aims to … effectively incorporate persistent homology (PH) into neural network models to increase their forecast accuracy in predicting … realized volatility (RV). This paper also proposes a novel neural network model for multi-step forecasting that systematically …