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Does the macroeconomy predict UK asset returns in an nonlinear fashion? : comprehensive out-of-sample evidence

Guidolin, Massimo; Hyde, Stuart; McMillan, David G.; … - 2010

We perform a comprehensive examination of the recursive, comparative predictive performance of a number of linear and non-linear models for UK stock and bond returns. We estimate Markov switching, threshold autoregressive (TAR), and smooth transition autoregressive (STR) regime switching models,...

Persistent link: https://www.econbiz.de/10008990694

Predictable Risks and Predictive Regression in Present-Value Models

Piatti, Ilaria - 2017

Using a latent variables approach, we estimate the dynamics of dividends and returns in a tractable present-value model with time-varying risks. Expected returns imply a similar return predictability as under homoskedasticity, while expected dividend growth is more persistent and explains a...

Persistent link: https://www.econbiz.de/10012976115

Forecasting the realized variance in the presence of intraday periodicity

Dumitru, Ana-Maria H.; Hizmeri, Rodrigo; Izzeldin, Marwan - 2019

This paper examines the impact of intraday periodicity on forecasting realized volatility using a heterogeneous autoregressive model (HAR) framework. We show that periodicity inflates the variance of the realized volatility and biases jump estimators. This combined effect adversely affects...

Persistent link: https://www.econbiz.de/10012063222

Can Deep Neural Networks Outperform Fama-MacBeth Regression and Other Supervised Learning Approaches in Stock Returns Prediction With Asset-Pricing Factors?

Teng, Huei-Wen; Li, Yu-Hsien - 2022

In asset pricing, most studies focus on finding new factors such as macroeconomic factors or firm characteristics to explain risk premium. Investigating whether these factors are useful in forecasting stock returns remains active research in the field of finance and computer science. This paper...

Persistent link: https://www.econbiz.de/10014235825

A Frequency-Domain Alternative to Long-Horizon Regressions with Application to Return Predictability

Sizova, Natalia - 2013

This paper aims at improved accuracy in testing for long-run predictability in noisy series, such as stock market returns. Long-horizon regressions have previously been the dominant approach in this area. We suggest an alternative method that yields more accurate results. We find evidence of...

Persistent link: https://www.econbiz.de/10013078663

The One-Trading-Day-Ahead Forecast Errors of Intra-Day Realized Volatility

Degiannakis, Stavros Antonios - 2018

Two volatility forecasting evaluation measures are considered; the squared one-day ahead forecast error and its … standardized version. The mean squared forecast error is the widely accepted evaluation function for the realized volatility … forecasting accuracy. Additionally, we explore the forecasting accuracy based on the squared distance of the forecast error …

Persistent link: https://www.econbiz.de/10012910114

Pockets of Predictability

Farmer, Leland - 2019

For many benchmark predictor variables, short-horizon return predictability in the U.S. stock market is local in time as short periods with significant predictability (‘pockets') are interspersed with long periods with little or no evidence of return predictability. We document this result...

Persistent link: https://www.econbiz.de/10012899675

Robust block bootstrap panel predictability tests

Westerlund, Joakim; Smeekes, Stephan - 2013

Persistent link: https://www.econbiz.de/10010199463

Regularizing Bayesian Predictive Regressions

Feng, Guanhao - 2017

Regularizing Bayesian predictive regressions provides a framework for prior sensitivity analysis via the regularization path. We jointly regularize both expectations and variance-covariance matrices using a pair of shrinkage priors. Our methodology applies directly to vector autoregressions...

Persistent link: https://www.econbiz.de/10012968480

Can deep neural networks outperform Fama-MacBeth regression and other supervised learning approaches in stock returns prediction with asset-pricing factors?

Teng, Huei-Wen; Li, Yu-Hsien - In: Digital finance : smart data analytics, investment … 5 (2023) 1, pp. 149-182

Persistent link: https://www.econbiz.de/10014251571