This paper proposes an Exponential HEAVY (EHEAVY) model. The model specifies the dynamics of returns and realized measures of volatility in an exponential form, which guarantees the positivity of volatility without restrictions on parameters and naturally allows the asymmetric effects. It...

We construct a momentum factor that identifies cross-sectional winners and losers based on a weighting scheme that incorporates all the price data, over the entire lookback period, as opposed to only the first and last price points of the window. The weighting scheme is derived from the...

We establish innovative measures of liquidity premium Beta on both asset and portfolio levels, and corresponding liquidity-adjusted return and volatility, for selected crypto assets. We develop a liquidity-adjusted ARMA-GARCH/EGARCH representation to model the liquidity-adjusted return for...

The empirical literature of stock market predictability mainly suffers from model uncertainty and parameter instability. To meet this challenge, we propose a novel approach that combines the documented merits of diffusion indices, regime-switching models, and forecast combination to predict the...

The use of mixture distributions for modeling asset returns has a long history in finance. New methods of demonstrating support for the presence of mixtures in the multivariate case are provided. The use of a two-component multivariate normal mixture distribution, coupled with shrinkage via a...

This paper examines the effectiveness of using futures contracts as hedging instruments of: (1) alternative models of volatility for estimating conditional variances and covariances; (2) alternative currencies; and (3) alternative maturities of futures contracts. For this purpose, daily data of...

While numerous studies have analyzed the asset allocation issue of US stock market from various angles, much less attention has been paid to the asset allocation issue of Chinese stock market. This article investigates the asset allocation in Chinese stock market from a perspective of...

We postulate that utilizing return prediction models with fundamental, macroeconomic, and technical indicators instead of using historical averages should result in superior asset allocation decisions. We investigate the predictive power of individual variables for forecasting industry returns...

We study dynamic portfolio choice of a long-horizon investor who uses deep learning methods to predict equity returns when forming optimal portfolios. Our results show statistically and economically significant benefits from using deep learning to form optimal portfolios through certainty...

We introduce a flexible utility-based empirical approach to directly determine asset allocation decisions between risky and risk-free assets. This is in contrast to the commonly used two-step approach where least squares optimal statistical equity premium predictions are first constructed to...