This paper investigates Seasoned Equity Offering (SEO) cost differences between Europe and the US in the period 1990 to 2011. We find that the direct costs for the US companies are around 40% higher than those for the European companies. Results of this paper are consistent with strategic...

Bitcoin is an open source peer-to-peer electronic money and payment system. It is traded at several exchanges and high-frequency trade data are publicly available. We study the contributions of Bitcoin exchanges to price discovery. Our results show that Mt.Gox and BTC-e are the market leaders...

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...

We suggest a simple and general way to improve the GARCH volatility models using the intraday range between the highest and the lowest price to proxy volatility. We illustrate the method by modifying a GARCH(1,1) model to a Range-GARCH(1,1) model. Our empirical analysis conducted on stocks,...

This paper investigates the most traded VIX exchange traded products (ETPs) with focus on their performance, price discovery, hedging ability and trading strategy. The VIX ETPs track their benchmark indices well. They are therefore exposed to the same time-decay (high negative expected returns)...

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 is the first comprehensive study on the forecasting of the realized volatility of non-ferrous metal futures. Based on 8.5 years of intraday data on copper, zinc, nickel, lead and aluminum, we explore a variety of extensions of the univariate heterogeneous autoregressive (HAR) model and seek...

We study which variables can explain and predict the return, volatility and trading volume of Bitcoin. The considered variables are return, volatility, trading volume, transaction volume, change in the number of unique Bitcoin addresses, the VIX index and Google searches for “Bitcoin”. We...