We study fluctuations in stock prices using a framework derived from the present value model augmented with a macroeconomic factor. The fundamental value is derived as the expected present discounted value of broad dividends that include, in addition to traditional cash dividends, other payouts...

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better...

This study explores the dependency structure of S&P 500 survivor stocks. Using a hand-collected sample of stocks that survived in the S&P 500 since March 1957, we employ rescaled/range analysis to investigate survivors. First, we find nonlinearities in the return processes of survivor stocks due...

In this paper, we analysed the heavy-tailed behaviour in the dynamics of housing-price returns in the United States. We investigated the sources of heavy tails by estimating autoregressive models in which innovations can be subject to GARCH effects and/or non-Gaussianity. Using monthly data from...

We examine the impact of oil price and oil price volatility on US illiquidity premiums (return on illiquid-minus-liquid stocks), using the US Oil Fund options implied volatility OVX index. We use daily data from 2007 to 2018, taking into account the structural break in June 2009 and controlling...