Erik Haugom, Rina Ray
Journal of Commodity Markets,Vol. 5, Pages: 36-49.
We are the first to analyze the relation between liquidity, volatility, and return distributions for the crude oil futures market. We do this by using a quantile regression method while most of the research in the field of liquidity and volatility has employed conventional OLS regression. While the latter approach can be useful in many applications, it fails to provide any insight about the effects in the rest of the distributions-outside the mean-of interest. Our results show that a distinct volatility “smile” is formed when trading activity, …
Erik Haugom, Rina Ray, Carl J Ullrich, Steinar Veka, Sjur Westgaard
Finance Research Letters,Vol. 16, Pages: 196-207.
This paper proposes a parsimonious quantile regression model for forecasting Value-at-Risk. The model uses only observable measures of daily, weekly, and monthly volatility as input and thus simplifies optimization substantially compared with other methods proposed in the literature. The framework also provides a new way of illustrating the volatility effects of a heterogeneous market. When subjected to formal coverage tests for out-of-sample VaR predictions, model performance is similar to more complicated models.
Jing Chen, Lorn Chollete, Rina Ray
Journal of Financial Markets,Vol. 13, Issue 2, Pages: 249-267.
We investigate the link between distress and idiosyncratic volatility. Specifically, we examine the twin puzzles of anomalously low returns for high idiosyncratic volatility stocks and high distress risk stocks, documented by Ang et al.(2006) and Campbell et al.(2008), respectively. We document that these puzzles are empirically connected, and can be explained by a simple, theoretical, single-beta CAPM model.