Erik Haugom

Forecasting Spot Price Volatility Using the Short-Term Forward Curve

We use high frequency real time spot prices and day-ahead forward prices from the Pennsylvania–New Jersey–Maryland wholesale electricity market to calculate, describe, and forecast spot price volatility. We introduce the concept of forward realized volatility calculated from day-ahead forward prices. Forward realized volatility improves forecasts of spot price volatility – in the sense of higher R2s and significantly lower forecast errors – when compared with forecasts based solely upon historical volatility. The largest forecast improvements obtained when the change in forward realized volatility is large in magnitude. Splitting total volatility into its continuous and jump components is crucial for forecasting volatility at weekly and monthly horizons.

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.

Modelling day ahead Nord Pool forward price volatility: Realized volatility versus GARCH models

Traditionally, and still within electricity futures/forward markets, daily data has been utilized as the unit of analyses when modelling and making predictions of volatility. However, over the recent past it is argued that better volatility estimates can be obtained by using standard time series techniques on non-parametric volatility measures constructed from high-frequency intradaily returns. Liquidity in financial electricity markets has increased rapidly over the recent years, which make it possible to apply these relatively new methods for measuring market volatility. In this paper high-frequency data and the concept of realized volatility is utilized to make day ahead predictions of Nord Pool forward price volatility. Such short term volatility predictions are especially important for operators and other participants in the electricity sector. We compare the results obtained from standard time-series techniques with the more traditional GARCH-framework which utilizes daily returns. Additionally, we examine whether different approaches of decomposing the total variation into a continuous — and jump measure improves the model fit or not. The paper provides new insights to how the financial electricity market at Nord Pool works, and how we efficiently can model and make predictions of the price movements in this market.

Modelling and forecasting alpine skier visits

This study is the first to examine formally what drives variations in skier attendance at Norwegian ski resorts. The authors use a unique data set of the number of daily visitors at a specific ski resort from 2007/2008 to 2013/2014. The main findings suggest that weather conditions, day of the week and holidays significantly affect the number of daily visitors. The time series regression analysis highlights the demand pattern and specific non-linear relationships between visitors and wind chill temperature. The study finds that, if the wind chill temperature is below −9.5°C, a temperature increase has a positive effect on the number of daily visitors. Similarly, if the wind chill temperature is above −9.5°C, a higher temperature leads to a lower number of skier visits, on average. Tourism providers can use these results for decision-making, planning and managing ski resort operations. In addition, the findings could serve as an incentive to implement innovative pricing tactics.

Some stylized facts about high-frequency Nord Pool forward electricity prices

This paper uses high-frequency data and the concept of realized volatility to analyze the stylized properties of two of the most liquid contracts at the central data source at Nord Pool. The paper examines a number of well-known features of traditional financial assets, their returns and volatilities, such as distribution properties, serial correlation, volatility clustering, the influence of extreme events and seasonality in the various measures. The main findings suggest that financial electricity prices exhibit many of the stylized properties found in traditional financial markets, such as high excess kurtosis for short sampling intervals, strong seasonality and long memory in the serial correlation. The results also provide new insights into how realized volatility and its different components behave with respect to financial electricity prices traded at Nord Pool.

Estimating and Evaluating Value‐at‐Risk Forecasts Based on Realized Variance: Empirical Evidence from ICE Brent Crude Oil Futures

This paper is the first to use the concept of realized volatility to forecast Value-at-Risk (VaR) for ICE Brent Crude oil futures. We examine sensitivities in the VaR forecasts across intra-daily sampling frequency used to calculate realized volatility. We evaluate the VaR forecasts using Christoffersens test for conditional coverage on quantiles of particular interest. Additionally, we examine a percentile–percentile plot of the VaR forecasts for all percentiles. The main empirical results show that very good VaR forecasts can be obtained using Gaussian critical values in combination with volatility forecasts based on realized volatility. An examination of the sampling frequency suggests that the most accurate VaR forecasts are obtained with a sampling frequency of between 1 and 10 min. This has important implications for practitioners operating in the financial oil sector.

Variable pricing and change in alpine skiing attendance

In this article, we examine two aspects of the relation between variable intra-week pricing and alpine skiing attendance at three ski resorts in Norway. First, we study what affects the probability of increased skiing frequency during the midweek if the price is reduced in this period compared to the regular (weekend) price. Second, we examine the cannibalization effect from a lower midweek price on the weekend skiing activity. Our results show that the probability of increased midweek skiing at a lower price is significantly influenced by age and income. The probability that cannibalization occurs is significantly influenced by skiing interest and family status.

Economies of scale in Norwegian electricity distribution: a quantile regression approach

ABSTRACT In this article, we investigate scale economies in Norwegian electricity distribution companies using a quantile regression approach. To the best of our knowledge, this is the first attempt to apply this estimation technique when analysing scale economies. We estimate the cost elasticities of the two output components: network length and number of customers, to calculate returns to scale. Our results show large potential of scale economies, particularly for the smallest companies. We also find that returns to scale is increasing over time. These findings have important implications for policymakers when they are deciding the structure of the industry in the future.

Covariance estimation using high-frequency data: Analysis of Nord Pool electricity forward data

Volatility and correlation modelling is important in order to calculate hedge ratios, value at risk estimates, CAPM betas, derivate pricing and for risk management in general. Historically, these measures have usually been obtained by analyzing daily data. Recently access to intra-daily high-frequency data for two of the most liquid contracts at the Nord Pool exchange (quarterly and yearly forward contracts), makes it possible to apply new and promising methods for analyzing volatility and correlation. We apply the concept of realized volatility and realized correlation, and as the first study statistically describe the distribution (both distributional properties and temporal dependencies) of electricity forward data from 2005 to 2009. The overall main findings show that the logarithmic realized volatility are approximately normal distributed, while realized correlation seems not. Further, realized volatility and realized correlation has a long memory feature, and there seem to be a high correlation between realized correlation and volatilities. These results are to a large extent consistent with earlier stylized facts studies of other financial and commodity markets.