Assessing the influence of spot price predictability on electricity futures hedging

A common feature of energy prices is that spot price changes are partially predictable due to weather and demand seasonalities. This paper follows the Ederington and Salas (2008) framework and considers the expected change in spot prices when minimum variance hedge ratios are computed. The poor effectiveness of hedging strategies obtained in previous studies on electricity was because the standard hedging approach underestimates the effectiveness of hedging. In the empirical study made in this paper, weekly spot price risk is hedged with weekly futures in the Nord Pool electricity market. In this case, the optimal selection of the futures contract may produce risk reductions whose values vary between 60% and 80% – depending on the hedging duration (one to three weeks) and the analysed sub-period (in-sample and out-of-sample sub-periods).electricity markets; futures; hedging ratio;electricity price risk

Model based Monte Carlo pricing of energy and temperature quanto options

Weather derivatives have become very popular tools in weather risk management in recent years. One of the elements supporting their diffusion has been the increase in volatility observed on many energy markets. Among the several available contracts, Quanto options are now becoming very popular for a simple reason: they take into account the strong correlation between energy consumption and certain weather conditions, so enabling price and weather risk to be controlled at the same time. These products are more efficient and, in many cases, significantly cheaper than simpler plain vanilla options. Unfortunately, the specific features of energy and weather time series do not enable the use of analytical formulae based on the Black-Scholes pricing approach, nor other more advanced continuous time methods that extend the Black-Scholes approach, unless under strong and unrealistic assumptions. In this study, we propose a Monte Carlo pricing framework based on a bivariate time series model. Our approach takes into account the average and variance interdependence between temperature and energy price series. Furthermore, our approach includes other relevant empirical features, such as periodic patterns in average, variance, and correlations. The model structure enables a more appropriate pricing of Quanto options compared to traditional methods.weather derivatives; Quanto options pricing; derivative pricing; model simulation; forecast

Assessing the influence of spot price predictability on electricity futures hedging

A common feature of energy prices is that spot price changes are partially predictable due to weather and demand seasonalities. This paper follows the Ederington and Salas (2008) framework and considers the expected change in spot prices when minimum variance hedge ratios are computed. The poor effectiveness of hedging strategies obtained in previous studies on electricity was because the standard hedging approach underestimates the effectiveness of hedging. In the empirical study made in this paper, weekly spot price risk is hedged with weekly futures in the Nord Pool electricity market. In this case, the optimal selection of the futures contract may produce risk reductions whose values vary between 60% and 80% – depending on the hedging duration (one to three weeks) and the analysed sub-period (in-sample and out-of-sample sub-periods)

Assessing the influence of spot price predictability on electricity futures hedging

A common feature of energy prices is that spot price changes are partially predictable due to weather and demand seasonalities. This paper follows the Ederington and Salas (2008) framework and considers the expected change in spot prices when minimum variance hedge ratios are computed. The poor effectiveness of hedging strategies obtained in previous studies on electricity was because the standard hedging approach underestimates the effectiveness of hedging. In the empirical study made in this paper, weekly spot price risk is hedged with weekly futures in the Nord Pool electricity market. In this case, the optimal selection of the futures contract may produce risk reductions whose values vary between 60% and 80% – depending on the hedging duration (one to three weeks) and the analysed sub-period (in-sample and out-of-sample sub-periods)

Model based Monte Carlo pricing of energy and temperature quanto options

Weather derivatives have become very popular tools in weather risk management in recent years. One of the elements supporting their diffusion has been the increase in volatility observed on many energy markets. Among the several available contracts, Quanto options are now becoming very popular for a simple reason: they take into account the strong correlation between energy consumption and certain weather conditions, so enabling price and weather risk to be controlled at the same time. These products are more efficient and, in many cases, significantly cheaper than simpler plain vanilla options. Unfortunately, the specific features of energy and weather time series do not enable the use of analytical formulae based on the Black-Scholes pricing approach, nor other more advanced continuous time methods that extend the Black-Scholes approach, unless under strong and unrealistic assumptions. In this study, we propose a Monte Carlo pricing framework based on a bivariate time series model. Our approach takes into account the average and variance interdependence between temperature and energy price series. Furthermore, our approach includes other relevant empirical features, such as periodic patterns in average, variance, and correlations. The model structure enables a more appropriate pricing of Quanto options compared to traditional methods

Forecasting Weekly Electricity Prices at Nord Pool

This paper analyses the forecasting power of weekly futures prices at Nord Pool. The forecasting power of futures prices is compared to an ARIMAX model of the spot price. The time series model contains lagged external variables such as: temperature, precipitation, reservoir levels and the basis (futures price less the spot price); and generally reflects the typical seasonal patterns in weekly spot prices. Results show that the time series model forecasts significantly beat futures prices when using the Diebold and Mariano (1995) test. Furthermore, the average forecasting error of futures prices reveals that they are significantly above the settlement spot price at the 'delivery week' and their size increases as the time to maturity increases. Those agents taking positions in weekly futures contracts at Nord Pool might find the estimated ARIMAX model useful for improving their expectation formation process for the underlying spot price

Model based Monte Carlo pricing of energy and temperature Quanto options

Weather derivatives have become very popular tools in weather risk management in recent years. One of the elements supporting their diffusion has been the increase in volatility observed on many energy markets. Among the several available contracts, Quanto options are now becoming very popular for a simple reason: they take into account the strong correlation between energy consumption and certain weather conditions, so enabling price and weather risk to be controlled at the same time. These products are more efficient and, in many cases, significantly cheaper than simpler plain vanilla options. Unfortunately, the specific features of energy and weather time series do not enable the use of analytical formulae based on the Black- Scholes pricing approach, nor other more advanced continuous time methods that extend the Black- Scholes approach, unless under strong and unrealistic assumptions. In this study, we propose a Monte Carlo pricing framework based on a bivariate time series model. Our approach takes into account the average and variance interdependence between temperature and energy price series. Furthermore, our approach includes other relevant empirical features, such as periodic patterns in average, variance, and correlations. The model structure enables a more appropriate pricing of Quanto options compared to traditional methods

Model Based Monte Carlo Pricing of Energy and Temperature Quanto Options

Weather derivatives have become very popular tools in weather risk management in recent years. One of the elements supporting their diffusion has been the increase in volatility observed on many energy markets. Among the several available contracts, Quanto options are now becoming very popular for a simple reason: they take into account the strong correlation between energy consumption and certain weather conditions, so enabling price and weather risk to be controlled at the same time. These products are more efficient and, in many cases, significantly cheaper than simpler plain vanilla options. Unfortunately, the specific features of energy and weather time series do not enable the use of analytical formulae based on the Black-Scholes pricing approach, nor other more advanced continuous time methods that extend the Black-Scholes approach, unless under strong and unrealistic assumptions. In this study, we propose a Monte Carlo pricing framework based on a bivariate time series model. Our approach takes into account the average and variance interdependence between temperature and energy price series. Furthermore, our approach includes other relevant empirical features, such as periodic patterns in average, variance, and correlations. The model structure enables a more appropriate pricing of Quanto options compared to traditional methods.weather derivatives, Quanto options pricing, derivative pricing, model simulation and forecast.