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

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 temperature indices with timevarying long-memory models

The hedging of weather risks has become extremely relevant in recent years, promoting the diffusion of weather derivative contracts. The pricing of such contracts require the development of appropriate models for the prediction of the underlying weather variables. Within this framework, we present a modification of the double long memory ARFIMA-FIGARCH model introducing time-varying memory coefficients for both mean and variance. The model satisfies the empirical evidence of changing memory observed in average temperature series and provide useful improvements in the forecasting, simulation and pricing issues related to weather derivatives. We present an application related to the forecast and simulation of temperature indices used for pricing of weather options.weather forecasting, weather derivatives, long memory time series, time-varying long memory, derivative pricing

Modelling and forecasting wind speed intensity for weather risk management

The modelling of wind speed is a traditional topic in meteorological research, where the main interest is on the short-term forecast of wind speed intensity and direction. More recently, this theme has received some interest in the quantitative finance literature for its relationship with electricity production by wind farms. In fact, electricity producers are interested in long-range forecasts and simulation of wind speed for two main reasons: to evaluate the profitability of building a wind farm in a given location and to offset the risks associated with the variability of wind speed for an already operating wind farm. In this paper, we contribute to the increasing literature regarding environmental finance by comparing three approaches that are capable of forecasting and simulating the long run evolution of wind speed intensity (direction is not a concern, given that the recent turbines can rotate to follow wind direction): the Auto Regressive Gamma process, the Gamma Auto Regressive process, and the ARFIMA-FIGARCH model. We provide both in-sample and out-of-sample comparisons of the models, as well as some examples for the pricing of wind speed derivatives using a model-based Monte Carlo simulation approach.Gamma Auto Regressive, Auto Regressive Gamma, ARFIMA-FIGARCH, wind speed modelling, wind speed simulation

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.

Measuring Non-Catastrophic Weather Risks for Businesses

While many published articles touch on the problem of using weather derivatives as tools for non-catastrophic weather-risk management, few studies have looked at the problem of appropriate risk measurement. This paper aims to present and evaluate all available methods used to identify and estimate the impact of non-catastrophic weather upon commercial enterprises. Correctly defining these parameters fundamentally affects building weather cover. Analysis of already existing methods of weather-risk measurement for businesses, as presented in the literature, has shown a few disadvantages. This paper proposes an improved approach to weather risk measurement – one based on an extended econometric model. We have empirically tested all the methods proposed herein and present our conclusions. The Geneva Papers (2009) 34, 425–439. doi:10.1057/gpp.2009.16