Peter Molnár

Bitcoin for Energy Commodities Before and After the December 2013 Crash: Diversifier, Hedge or Safe Haven?

ABSTRACT 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 electricity, is an essential input in the Bitcoin production. For the entire period, results show that Bitcoin is a strong hedge and a safe-haven against movements in both commodity indices. We further examine whether that ability is also present for non-energy commodities and our analysis show insignificant results when energy commodities are excluded from the general commodity index. We also account for the December 2013 Bitcoin price crash and our results reveal that Bitcoin hedge and safe-haven properties against commodities and energy commodities are only present in the pre-crash period, whereas in the post-crash period Bitcoin is no more than a diversifier. In addition to uncovering the time-varying role of Bitcoin, we highlight the dissimilarity in the dynamic correlations between the extreme downward and extreme upward movements.

Volatility forecasting of strategically linked commodity ETFs: gold-silver

We apply heterogeneous autoregressive (HAR) models—including nine univariate, two multivariate and three combination models—to high-frequency data to predict the one-day forward volatilities of two strategically linked commodities, gold and silver. We provide evidence that it is difficult to beat the benchmark HAR model using univariate models and that, a much better strategy is to average the forecasts from many models. In addition, the forecasts are not improved by using volatilities from strategically linked commodities; thus, no volatility spillovers are detected. Interestingly, when the two strategically linked commodities are modelled together using the generalized HAR model, the forecasts are comparable to those of combination forecast models.

The Forecasting Power of Medium-Term Futures Contracts

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 the futures prices overshoot the spot price, resulting in a positive risk premium. We find a significant premium during winter and fall when analyzing the whole sample. There is no evidence of a premium during summer. Dividing the sample into two sub periods, 1996-2005 and 2006-2013, we find the highest and most significant risk premium during winter in the first sub period. In the latter period, there is less evidence of a significant risk premium.

Electricity Consumption Modelling: A Case of Germany

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 better if we model electricity load independently for each hour or if we model in the first step the average daily consumption and in a second step we model for each hour deviation from this average. We therefore compare two simple, yet flexible models for hourly electricity consumption in Germany. Both models use temperature, industrial production, hours of daylight and dummies for days of the week and month of the year as explanatory variables. We find that the first model, despite being simpler, forecasts hourly electricity demand more precisely. This indicates that hourly electricity consumption represents various goods, and should be modelled separately for each hour.

High-low range in GARCH models of stock return volatility

ABSTRACT 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, stock indices and simulated data shows that the range-GARCH(1,1) model performs significantly better than the standard GARCH(1,1) model both in terms of in-sample fit and out-of-sample forecasting ability.

Google searches and Gasoline prices

We study the relation between gasoline prices and Google searches for the term “gasoline prices”. Utilizing the framework of a vector autoregressive model and Granger causality, we find a two-way relationship between gasoline prices and Google searches. In both directions, the relation is negative, i.e. an increase in one of the variables leads to a decrease in the other variable. Moreover, we find the strongest evidence for causality from Google searches to gasoline prices.

Green certificates and investments in small hydro power plants

Investment in renewable energy production in Norway is since the 1st of January 2012 subsidized through a market for green certificates common with Sweden. We study how the prospects of a future green certificates scheme affected the expectations of investors investing in small hydropower plants in Norway. Data from 214 licenses granted from 2001 including 2008 are used to replicate the investors decision problem. We derive the implied expected subsidies at the time the investment decisions were made assuming investors acted in accordance with a traditional net present value rule or in accordance with a real options rule. In the latter, investors will only invest if the net present value is at least as high as the value of postponing the investment decision. We find empirical support for investors acting in accordance with real options theory indicating that they value the opportunity to optimally time their investment. Moreover, professional investors value the opportunity to wait higher than do non-professional investors; i.e. the implied subsidies when investing are higher for these investors.

Structural breaks in emission allowance prices

We study structural breaks in the emission allowance price process of the European Union Trading System during Phase Π and Phase III, covering years from 2008 to 2016. There is indeed a structural break between the Phase II and Phase III. However, there are several regimes within each of these phases. We find that the high-volatility regimes are usually the regimes with negative average returns, whereas low-volatility regimes usually exhibit zero or positive average returns.

Applying copulas functions for wind and hydro complementarity evaluation: A Brazilian case

Wind energy has been expanding in many countries typically hydro electricity producers in the last decades and some investments take advantage from the complementarity among hydro and wind productions as a strategy to increase profits and minimize risks. In this paper, our focus is applying copulas functions to evaluate a complementarity between wind and hydro production in different regions in Brazil. The goal is evaluate which regions are most suitable for development of wind energy considering the existing hydro electricity production. Copulas functions can capture the dependence structure among random variables offering a great flexibility in building multivariate stochastic models while statistic correlation does not capture nonlinear effects sufficiently. For this reason, we apply nine different copulas functions in our study finding those that best capture the complementarity.