Covariance Matrix Estimation using an Errors-in-Variables Factor Model with Applications to Portfolio Selection and a Deregulated Electricity Market

We propose an errors-in-variables factor model which extends the classical capital asset pricing model (CAPM) to the case where the market returns contain additive noise. Using the model, we propose a method for choosing portfolios of assets, such as U.S. stocks in the S&P 100 and virtual electricity contracts in a regional transmission organization. Virtual electricity contracts relate real-time and day-ahead electircity prices and can be used for hedging or speculation. Using the errors-in-variables factor model, we first estimate the covariance matrix of electricity prices at different locations and then construct efficient portfolios which balance risk and return. We present applications using US equities and virtual electricity contracts to show the new covariance matrix estimation technique can effectively manage risk.