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...

The observation that price declines usually lead to volatility increases is known as the asymmetric volatility effect and has become a stylized fact about the financial markets. We study asymmetric volatility effect in 19 equity indices from North America, Latin America, Europe, Asia and...

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...

The dynamic conditional correlation (DCC) model by Engle (2002) is one of the most popular multivariate volatility models. This model is based solely on closing prices. It has been documented in the literature that the high and low prices of a given day can be used to obtain an efficient...

Using data from 214 hydro-power projects in Norway we study whether investors in renewable energy projects exert discretion about the timing of investment decisions. We know from interviews with these investors that they do not use the real options model; however, we would like to learn whether...