Workshop #3. – Statistical Inference in Energy Markets

Institut Henri Poincaré, Paris - May 25 2018

Confirmed speakers: Mark Podolskij (Aarhus University), Paulina Rowinska (Imperial College, London), Alexandre Brouste (Le Mans Université), Markus Bibinger (Philipps Universität, Marburg), John Moriarty (Queen Mary University of London), Rafał Weron (Politechnika Wrocławska)

 

Program

9:00 - 9:30: Welcoming of the participants

09:30 – 10:15 Alexandre Brouste (Le Mans Université), Parametric estimation at high-frequency

Abstract

Asymptotic efficiency of the sequence of maximum likelihood estimators is considered in statistical experiments implying the fractional Gaussian noise or symmetric stable random variables observed at high-frequency. Likelihood ratio hypothesis tests are also studied with an application to oil price modeling.

10:15 – 11:00 Mark Podolskij (Aarhus University), Statistical inference for fractional models

Abstract

In recent literature fractional and moving average type models have gained popularity in economics and finance. Examples include fractional Brownian/stable motion, rough volatility models and Hawkes processes. In this talk we will review some existing estimation methods and present new results. We will make the link to potential application in modelling energy markets

 

11:00 - 11:20 Coffee break

11:20 - 12: 05 Paulina Rowinska (Imperial College), Blowing in the Wind

Abstract

We introduce a three-factor model of electricity spot prices, consisting of a deterministic seasonality and trend function as well as short- and long-term stochastic components, and derive a formula for futures prices. The long-term component is modelled as a Lévy process with increments belonging to the class of generalised hyperbolic distributions. We describe the short-term factor by Lévy semistationary processes: we start from a CARMA(2,1), i.e. a continous-time ARMA model, and generalise it by adding a short-memory stochastic volatility. We further modify the model by including the information about the wind energy production as an exogenous variable. We fit our models to German and Austrian data including spot and futures prices as well as the wind energy production and total load data. Empirical studies reveal that taking into account the impact of the wind energy generation on the prices improves the goodness of fit.

12:05 - 12: 50 Pierre Gruet (EDF R&D), tba

12:50 – 14:15 Lunch

14:15 – 15:00 Markus Bibinger (Philipps-Universität Marburg), tba

15:00 - 15:45 John Moriarty (Queen Mary University of London), Rare events in energy networks and markets: an MCMC approach

Abstract

Energy networks and markets experience various types of disturbance. For example, the increasing penetration of renewable energy sources is increasing the variability of power generation, with both physical and financial consequences. Further, unusually large power disturbances propagate in a complex manner due to network effects. We present a novel extension, named ghost sampling, of the Metropolis-Hastings Markov Chain Monte Carlo method that is tailored to efficiently sample rare power disturbances, conditional on some unusual physical or financial outcome. Generating a representative random sample provides insight into the effect of stochastic generation on, among others, locational marginal prices and system security, and we present examples from small simulated networks. Our method can perform conditional sampling from any joint distribution of power disturbances and thus capture important statistical features of renewable generation including, for instance, correlated and non-Gaussian disturbances.

15:45 – 16:00 Coffee Break

16:00 – 16:45 Rafal Weron (Wrocław University of Technology), Recent advances in electricity price forecasting: A 2018 perspective

Abstract

A variety of methods and ideas have been tried for electricity price forecasting over the last two decades, with varying degrees of success. In this talk I will provide a short overview of the recent advances.

16:45  End of the workshop

 

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