Forecasting electricity spot market prices with a k-factor GIGARCH process

In this article, we investigate conditional mean and variance forecasts using a dynamic model following a k-factor GIGARCH process. We are particularly interested in calculating the conditional variance of the prediction error. We apply this method to electricity prices and test spot prices forecasts until one month ahead forecast. We conclude that the k-factor GIGARCH process is a suitable tool to forecast spot prices, using the classical RMSE criteria.Conditional mean ; conditional variance ; forecast ; electricity prices ; GIGARCH process

Forecasting electricity spot market prices with a k-factor GIGARCH process

In this article, we investigate conditional mean and variance forecasts using a dynamic model following a k-factor GIGARCH process. We are particularly interested in calculating the conditional variance of the prediction error. We apply this method to electricity prices and test spot prices forecasts until one month ahead forecast. We conclude that the k-factor GIGARCH process is a suitable tool to forecast spot prices, using the classical RMSE criteria.Conditional mean - conditional variance - forecast - electricity prices - GIGARCH process

Fine mapping of causal HLA variants using penalised regression

The identification of risk loci in the Human Leukocyte Antigen (HLA) region using single-SNP association tests has been hampered by the extent of linkage disequilibrium (LD). Penalised regression via Least Absolute Shrinkage and Selection Operator (LASSO) can be used as a method for selection of variables in multi-SNP analysis, and to deal with the problem of multi-collinearity among predictors. This method applies a penalty that shrinks the estimates of the regression coefficients towards zero. This is equivalent to applying a double exponential (DE) prior distribution to the coefficients with a mode at zero, corresponding to the prior belief that most of the effects are negligible in a Bayesian approach. Parameter inference is based on the posterior mode, with non-zero values indicating marker-disease associations. Single-SNP, stepwise regression and the LASSO approach were applied to case-control studies of rheumatoid arthritis, a disease which has been associated with markers from the HLA region. A generalisation of the LASSO called the HyperLasso (HLASSO), which uses the normal-exponential-gamma prior in place of the DE, was also investigated. These approaches were applied to data from the Genetics of Rheumatoid Arthritis (GoRA) study. Genotype imputation was used as a means to jointly analyse the GoRA and the Wellcome Trust Case Control Consortium (WTCCC) HLA SNPs. The North American Rheumatoid Arthritis Consortium (NARAC) study was used to validate the findings. After controlling for type-I error, the penalised approaches greatly reduced the number of positive signals compared to single-SNP analysis, suggesting that correlation among SNP loci was better handled. The HLASSO results were sparser but similar to the LASSO results. One SNP in HLA-DPB1 was replicated in the NARAC study. In both models, the robustness of the retained variables was verified by bootstrapping. The results suggest that SNP-selection using LASSO or HLASSO shows a substantial benefit in identifying risk loci in regions of high LD

A k- factor GIGARCH process : estimation and application to electricity market spot prices,

Some crucial time series of market data, such as electricity spot prices, exhibit long memory, in the sense of slowly-decaying correlations combined with heteroscedasticity. To e able to model such a behaviour, we consider the k-factor GIGARCH process and we propose two methods to address the related parameter estimation problem. For each method, we develop the asymptotic theory for this estimation.GIGARCH process – estimation theory – Electricity spot prices.

Comparison of local electrochemical impedance measurements derived frombi-electrode and microcapillary techniques

In the present paper, local electrochemical impedance spectrawere obtained on a 316L stainless steel from two configurations: a dual microelectrode (bi-electrode) and microcapillaries. With the bi-electrode, the local impedance measurements were made from the ratio of the applied voltage to the local current density calculated from the application of the ohm’s law. With the use of microelectrochemical cells, the specimen surface area in contact with the electrolyte is limited by the use of glass microcapillaries and the local impedance was defined fromthe ratio of the local potential to the local current restricted to the analysed surface area. Differences and similarities observed in local impedance spectra obtained with the two configurations were describe

On Stochastic Error and Computational Efficiency of the Markov Chain Monte Carlo Method

In Markov Chain Monte Carlo (MCMC) simulations, the thermal equilibria quantities are estimated by ensemble average over a sample set containing a large number of correlated samples. These samples are selected in accordance with the probability distribution function, known from the partition function of equilibrium state. As the stochastic error of the simulation results is significant, it is desirable to understand the variance of the estimation by ensemble average, which depends on the sample size (i.e., the total number of samples in the set) and the sampling interval (i.e., cycle number between two consecutive samples). Although large sample sizes reduce the variance, they increase the computational cost of the simulation. For a given CPU time, the sample size can be reduced greatly by increasing the sampling interval, while having the corresponding increase in variance be negligible if the original sampling interval is very small. In this work, we report a few general rules that relate the variance with the sample size and the sampling interval. These results are observed and confirmed numerically. These variance rules are derived for the MCMC method but are also valid for the correlated samples obtained using other Monte Carlo methods. The main contribution of this work includes the theoretical proof of these numerical observations and the set of assumptions that lead to them

A k- factor GIGARCH process : estimation and application to electricity market spot prices,

International audienceSome crucial time series of market data, such as electricity spot prices, exhibit long memory, in the sense of slowly-decaying correlations combined with heteroscedasticity. To e able to model such a behaviour, we consider the k-factor GIGARCH process and we propose two methods to address the related parameter estimation problem. For each method, we develop the asymptotic theory for this estimation

Forecasting electricity spot market prices with a k-factor GIGARCH process

URL des Documents de travail :http://ces.univ-paris1.fr/cesdp/CESFramDP2007.htmParu dans Applied Energy, 86, 4 (2009) 505-510.Documents de travail du Centre d'Economie de la Sorbonne 2007.58 - ISSN : 1955-611XIn this article, we investigate conditional mean and variance forecasts using a dynamic model following a k-factor GIGARCH process. We are particularly interested in calculating the conditional variance of the prediction error. We apply this method to electricity prices and test spot prices forecasts until one month ahead forecast. We conclude that the k-factor GIGARCH process is a suitable tool to forecast spot prices, using the classical RMSE criteria.On donne l'expression analytique de la prévision en moyenne et en variance issue d'un processus GIGARCH à k-facteur. Les propriétés probabilistes sont données. Une application aux prix spot d'électricité sur le marché allemand est fourni