This short paper shows that a New Keynesian model with limited asset market participation can generate a high risk-premium on unlevered equity relative to short-term risk-free bonds and high variability of equity returns driven by monetary policy shocks with zero persistence.

We examine time-varying explanatory power of realized moments on subsequent bond futures excess returns using more than 12 years of high-frequency data from U.S. and German sovereign bond markets. We detect realized volatility and realized kurtosis to carry valuable information for next-day...

We develop a four-factor model intended to capture size, value, and credit rating transition patterns in excess returns for a panel of predominantly mid- and large-cap entities. Using credit transition matrices and rating histories from 48 US issuers, we provide evidence to support a...

We examine how extreme market risks are priced in the cross-section of asset returns at various horizons. Based on the frequency decomposition of covariance between indicator functions, we define the quantile cross-spectral beta of an asset capturing tail-specific as well as horizon-, or...

This paper examines both intertemporal and contemporaneous relationship between excess US Treasury futures returns and realized moments - realized volatility, realized skewness and realized kurtosis using high-frequency data. We find realized skewness to have significant negative effect on...

Measuring risk in the stock market context is one of the key challenges of modern finance. Despite of the substantial significance of the topic to investors and market regulators, there is a controversy over what risk factors should be used to price the assets or to determine the cost of...

Cost of equity is crucial information that enters business valuation. Yet, even after decades of academic research, consensus has not been reached regarding the appropriate cost of equity estimation. The aim of our paper is to investigate the cost of equity estimation in practice. In other...

This paper analyses the evolution of systematic risk of banking industries in eight advanced countries using weekly data from 1990 to 2012. The estimation of time-varying betas is done by means of a Bayesian state space model with stochastic volatility, whose results are contrasted with those of...

We examine the predictability of expected stock returns across horizons using machine learning. We use neural networks, and gradient boosted regression trees on the U.S. and international equity datasets. We find that predictability of returns using neural networks models decreases with longer...

A prominent factor used in most models predicting stock returns is firm size. Yet no consensus has emerged on the magnitude and stability of the size premium, with some researchers even questioning the usefulness of the factor. To take stock of the voluminous academic literature on the size...