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

Abstract

The modeling of volatility and correlation is important in order to calculate hedge ratios, value at risk estimates, CAPM betas, derivate pricing and risk management in general. Recent access to intra-daily high-frequency data for two of the most liquid contracts at the Nord Pool exchange has made it possible to apply new and promising methods for analyzing volatility and correlation. The concepts of realized volatility and realized correlation are applied, and this study statistically describes the distribution (both distributional properties and temporal dependencies) of electricity forward data from 2005 to 2009. The main findings show that the logarithmic realized volatility is approximately normally distributed, while realized correlation seems not to be. Further, realized volatility and realized correlation have a long-memory feature. There also seems to be a high correlation between realized correlation and volatilities and positive relations between trading volume and realized volatility and between trading volume and realized correlation. These results are to a large extent consistent with earlier studies of stylized facts of other financial and commodity markets.