This study investigates whether weekly futures prices, covering the time period 1996-2013, are unbiased predictors of future spot price in the Nordic power market. The results give no clear evidence of bias in the futures prices, except for during the winter period from 2003-2009. In this period...

We examine the information content of the CBOE Crude Oil Volatility Index (OVX) when forecasting realized volatility in the WTI futures market. Additionally, we study whether other market variables, such as volume, open interest, daily returns, bid-ask spread and the slope of the futures curve,...

Cooper and Nyborg (2008) derive a tax-adjusted discount rate formula under a constant proportion leverage policy, investor taxes and risky debt. However, their analysis assumes zero recovery in default. We extend their framework to allow for positive recovery rates. We also allow for differences...

We study the impact of fuel prices, emission allowances, demand, past prices, wind and solar production on hourly day-ahead electricity prices in Germany over the period from January 2015 until June 2018. Working within a linear regression, ARX-EGARCH and quantile regression framework we compare...

Recent research has found that electricity consumption is a very useful variable in economics. In many applications it might be desirable to decompose electricity consumption into unpredictable and deterministic (or highly predictable) component. We want to find out whether forecasting works...

This paper uses a dynamic conditional correlation model to examine whether Bitcoin can act as a hedge and safe haven for major world stock indices, bonds, oil, gold, the general commodity index and the US dollar index. Daily and weekly data span from July 2011 to December 2015. Overall, the...

We study the relationship between Bitcoin and commodities by assessing the ability of Bitcoin to act as a diversifier, hedge, or safe haven against daily movements in commodities in general, and energy commodities in particular. We focus on energy commodities because energy, in the form of...

We investigate whether data from Google Trends can be used to forecast stock returns. Previous studies have found that high Google search volumes predict high returns for the first one to two weeks, with subsequent price reversal. By using a more recent dataset that covers the period from 2008...