Bitcoin is an exciting new financial product that may be useful for inclusion in investment portfolios. This paper investigates the implications of replacing gold in an investment portfolio with bitcoin ('digital gold'). Our approach is to use several different multivariate GARCH models (dynamic...

This paper aims to develop some static and conditional (dynamic) models to predict portfolio returns in the Borsa Istanbul (BIST) that are calibrated to combine the capital asset-pricing model (CAPM) and corporate governance quality. In our conditional model proposals, both the traditional CAPM...

The equity premium follows a pronounced v-shape pattern around the beginning of recessions. It sharply drops into negative territory just before business cycle peaks and then strongly recovers as the recession unfolds. Recessions are preceded by an inverted yield curve. Thus probit models using...

We assess the benefits of using frequency-domain information for active portfolio management. To do so, we forecast the bond risk premium and equity risk premium using a methodology that isolates frequencies (of the predictors) with the highest predictive power. The resulting forecasts are more...

Any time series can be decomposed into cyclical components fluctuating at different frequencies. Accordingly, in this paper we propose a method to forecast the stock market's equity premium which exploits the frequency relationship between the equity premium and several predictor variables. We...

Purpose: The aim of our paper is twofold. First, we examine the predictive ability of log bookmarket, dividend-price, earnings-price and dividend-earnings ratios on the most recent data set of the strongest securities in the UK economy; unlike the majority of the studies in this data set, our...

The study tests whether realised moments of stock returns (mean, variance, skewness and kurtosis) computed from daily returns over the last month, quarter and year can predict the 1-month cross-sectional stock returns of 40 US-traded liquid stocks in the period 1986-2019. The performed...

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...

We employ a battery of model evaluation tests for a broad-set of GARCH-MIDAS models and account for data snooping bias. We document that inferences based on standard tests for GM variance components can be misleading. Our data mining free results show that the gains of macro-variables in...

Using the S&P GSCI and its five component sub-indices, we show that considering each commodity separately yields nontrivial hedging gains in and out of sample. During 1999-2019, the maximum Sharpe ratio portfolio assigns positive weights to the GSCI Energy, Industrial and Precious Metals,...