We use intraday data to compute weekly realized variance, skewness, and kurtosis for equity returns and study the realized moments' time-series and cross-sectional properties. We investigate if this week's realized moments are informative for the cross-section of next week's stock returns. We...

This paper examines whether deep/machine learning can help find any statistical and/or economic evidence of out-of-sample bond return predictability when real-time, instead of fully-revised, macro variables are taken as predictors. First, when using pure real-time macro information alone, we...

In this paper we investigate the predictive power of cross-sectional volatility, skewness and kurtosis for future stock returns. Adding to the work of Maio (2016), who finds cross-sectional volatility to forecast a decline in the equity premium with high predictive power in-sample as well as...

In this paper we estimate the term structure of daily UK interest rates using more flexible continuous time models. The multivariate CKLS framework is employed for dynamic estimation and forecasting of four classical models over the eventful period of 2000-2013. The extensions are applied in two...

Superior modeling of the yield curve is useful for asset pricing, financial planning, and risk management. In this article, we estimate five affine term structure models using daily Colombian data. We find that a three-factor model outperforms the other models in one and five days ahead...

This paper presents a new procedure for forecasting recessions utilizing short-term (slope) dynamics present in the yield curve. Building on a large body of literature chronicling the relationship between the shape of the yield curve and the business cycle, this paper employs Dynamic...

This paper demonstrates that the forecasted CAPM beta of momentum portfolios explains a large portion of the return, ranging from 40% to 60% for stock level momentum, and 30% to 50% for industry level momentum. Beta forecasts are from a realized beta estimator using daily returns over the prior...

We identify all return leader-follower pairs among individual stocks using Granger causality regressions. Thus-identified leaders can reliably predict their followers' returns out of sample, and the return predictability works at the level of individual stocks rather than industries. Our results...

This paper examines the ability of different GARCH models to forecast stock return volatility under a range of forecast metrics, including both statistical and economic evaluation. In particular, we are interested in whether wavelet de-noising of the data prior to estimation affects the ability...

This article comprehensively reviews the predictability of six equity factors. These factors are the market excess return, size, value, momentum, low beta and quality. I find predictability for the low beta factor and moderate predictability for the size factor. The results for other factors are...