In this paper we propose a jump-diffusion type model which recovers the main characteristics of electricity spot price dynamics in the Nordic market, including seasonality, mean-reversion and spiky behavior. We show how the calibration of the market price of risk to actively traded futures contracts allows for efficient valuation of Nord Pool's Asian-style… (More)
Forecasting wholesale electricity prices: A review of time series models, in "Financial Markets: Principles of Modelling, Forecasting and Decision-Making", eds. Abstract. In this paper we assess the short-term forecasting power of different time series models in the electricity spot market. We calibrate autoregression (AR) models, including specifications… (More)
In this paper we address the issue of modeling electricity loads. After analyzing properties of the deseasonalized loads from the California power market we fit an ARMA(1,6) model to the data. The obtained residuals seem to be independent but with tails heavier than Gaussian. It turns out that the hyperbolic distribution provides an excellent fit.
In this paper we study simple time series models and assess their forecasting performance. In particular we calibrate ARMA and AR-MAX (where the exogenous variable is the system load) processes. Models are tested on a time series of California power market system prices and loads from the period proceeding and including the market crash.
In this paper we study two statistical approaches to load forecasting. Both of them model electricity load as a sum of two components – a deterministic (representing seasonalities) and a stochastic (representing noise). They differ in the choice of the seasonality reduction method. Model A utilizes differencing, while Model B uses a recently developed… (More)
In this paper, we present a procedure for consistent estimation of the severity and frequency distributions based on incomplete insurance data and demonstrate that ignoring the thresholds leads to a serious underestimation of the ruin probabilities. The event frequency is modelled with a non-homogeneous Poisson process with a sinusoidal intensity rate… (More)
In the first years after the emergence of deregulated power markets it became apparent that for the valuation of electricity derivatives we cannot simply rely on models developed for financial or other commodity markets. However, before adequate models can be put forward the unique characteristics of electricity (spot) prices have to be thoroughly analyzed.… (More)
Using an agent-based modeling approach we show how personal attributes, like conformity or indifference, impact the opinions of individual electricity consumers regarding switching to innovative dynamic tariff programs. We also examine the influence of advertising, discomfort of usage and the expectations of financial savings on opinion dynamics. Our main… (More)
In this paper we assess the short-term forecasting power of different time series models in the electricity spot market. However, instead of evaluating point predictions we concentrate on interval forecasts. The latter are specifically important for risk management purposes where one is more interested in predicting intervals for future price movements than… (More)