While the predictability of excess stock returns is detected by traditional predictive regressions as statistically small, the direction-of-change and volatility of returns exhibit a substantially larger degree of dependence over time. We capitalize on this observation and decompose the returns...

A set of multivariate GARCH models is estimated and its empirical validity is compared from the calculation of the Value at Risk. Data used are the daily returns of the nominal exchange rate of the Colombian peso vis-a-vis the American dollar, euro, sterling and Japanese yen for the period...

We evaluate the performance of several linear and nonlinear machine learning models in forecasting the realized volatility (RV) of ten global stock market indices in the period from January 2000 to December 2021. We train models using a dataset which includes past values of the RV and additional...

We show that uncertainty of monetary policy (MPU) commands a risk premium in the US Treasury bond market. Using the news based MPU measure in Baker, Bloom, and Davis (2016) to capture monetary policy uncertainty, we find that MPU forecasts significantly and positively future monthly Treasury...

We investigate the impact of China's economic policy uncertainty (EPU) on the time series variation of Chinese stock market expected returns. Using the news based measure in Baker, Bloom, and Davis (2016), we find that EPU predicts negatively future stock market return at various horizons. This...

This paper presents the first comparison of the accuracy of density forecasts for stock prices. Six sets of forecasts are evaluated for DJIA stocks, across four forecast horizons. Two forecasts are risk-neutral densities implied by the Black-Scholes and Heston models. The third set are...

In this paper, we use factor-augmented HAR-type models to predict the daily integrated volatility of asset returns. Our approach is based on a proposed two-step dimension reduction procedure designed to extract latent common volatility factors from a large dimensional and high-frequency returns...

We introduce the class of FIR-GARCH models in this paper. FIR-GARCH models provide a parsimonious joint model for low-frequency returns and realized measures, and are sufficiently flexible to capture long memory as well as asymmetries related to leverage effects. We analyze the performances of...

In this paper, we explore how to build and test models for forecasting 1-year and 10-year returns for U.S. stocks as inputs to a strategic asset allocation process. We find that a model that forecasts value and growth indices separately best serves as a predictor for returns in the U.S. equity...

We study the predictability of stock returns using an iterative model-building approach known as quantile boosting. Examining alternative return quantiles that represent normal, bull and bear markets via recursive quantile regressions, we trace the predictive value of extensively studied...