M. Angeles Carnero

Periodic Seasonal Reg-ARFIMA-GARCH Models for Daily Electricity Spot Prices

Novel periodic extensions of dynamic long-memory regression models with autoregressive conditional heteroscedastic errors are considered for the analysis of daily electricity spot prices. The parameters of the model with mean and variance specifications are estimated simultaneously by the method of approximate maximum likelihood. The methods are implemented for time series of 1,200–4,400 daily price observations in four European power markets. Apart from persistence, heteroscedasticity, and extreme observations in prices, a novel empirical finding is the importance of day-of-the-week periodicity in the autocovariance function of electricity spot prices. In particular, the very persistent daily log prices from the Nord Pool power exchange of Norway are effectively modeled by our framework, which is also extended with explanatory variables to capture supply-and-demand effects. The daily log prices of the other three electricity markets—EEX in Germany, Powernext in France, and APX in The Netherlands—are less persistent, but periodicity is also highly significant. The dynamic behavior differs from market to market and depends primarily on the method of power generation: hydro power, power generated from fossil fuels, or nuclear power. The article improves on existing models in capturing the memory characteristics, which are important in derivative pricing and real option analysis.

Effects of Outliers on the Identification and Estimation of GARCH Models

This paper analyses how outliers affect the identification of conditional heteroscedasticity and the estimation of generalized autoregressive conditionally heteroscedastic (GARCH) models. First, we derive the asymptotic biases of the sample autocorrelations of squared observations generated by stationary processes and show that the properties of some conditional homoscedasticity tests can be distorted. Second, we obtain the asymptotic and finite sample biases of the ordinary least squares (OLS) estimator of ARCH(p) models. The finite sample results are extended to generalized least squares (GLS), maximum likelihood (ML) and quasi-maximum likelihood (QML) estimators of ARCH(p) and GARCH(1,1) models. Finally, we show that the estimated asymptotic standard deviations are biased estimates of the sample standard deviations.

Periodic Heteroskedastic RegARFIMA Models for Daily Electricity Spot Prices

In this paper we consider different periodic extensions of regression models with autoregressive fractionally integrated moving average disturbances for the analysis of daily spot prices of electricity. We show that day-of-the-week periodicity and long memory are important determinants for the dynamic modelling of the conditional mean of electricity spot prices. Once an effective description of the conditional mean of spot prices is empirically identified, focus can be directed towards volatility features of the time series. For the older electricity market of Nord Pool in Norway, it is found that a long memory model with periodic coefficients is required to model daily spot prices effectively. Further, strong evidence of conditional heteroskedasticity is found in the mean corrected Nord Pool series. For daily prices at three emerging electricity markets that we consider (APX in The Netherlands, EEX in Germany and Powernext in France) periodicity in the autoregressive coefficients is also stablished, but evidence of long memory is not found and existence of dynamic behaviour in the variance of the spot prices is less pronounced. The novel findings in this paper can have important consequences for the modelling and forecasting of mean and variance functions of spot prices for electricity and associated contingent assets

Mobbing and its determinants: the case of Spain

The objective of this article is to analyse empirically the problem of mobbing in Spain. Based on the fifth Spanish survey on working conditions, we find that during 2003, around 5% of workers declared being mobbed at their workplace. Some personal, job characteristics and working conditions are found to be significant at explaining the probability of being a mobbing victim. Finally, we find differences in the variables affecting such probability depending on the victims gender.

Mobbing and workers' health: an empirical analysis for Spain

his paper analyzes empirically the impact of mobbing on the health of workers in Spain. Based on the Sixth Spanish Survey on Working Conditions, we first describe the differences in health among mobbed and not mobbed workers, sing two different indicators: the workers self-perception that work affects health and the presence of bad health symptoms. The descriptive evidence shows that mobbing victims perform worse on such health indicators. We estimate the effect of being mobbed on the probability of suffering from health problems, taking into account the potential endogeneity of mobbing. Our estimates show that being a mobbing victim increases significantly the probability of having bad health, independently on the indicator used. Moreover, when bad health is measured by the perception indicator, we find that the effect of mobbing is underestimated if endogeneity is not accounted for.

Estimating VAR-MGARCH models in multiple steps

Abstract This paper analyzes the performance of multiple steps estimators of vector autoregressive multivariate conditional correlation GARCH models by means of Monte Carlo experiments. We show that if innovations are Gaussian, estimating the parameters in multiple steps is a reasonable alternative to the maximization of the full likelihood function. Our results also suggest that for the sample sizes usually encountered in financial econometrics, the differences between the volatility and correlation estimates obtained with the more efficient estimator and the multiple steps estimators are negligible. However, when innovations are distributed as a Student-t, using multiple steps estimators might not be a good idea.

Explaining transactions in time banks in economic crisis

This article studies the services exchanged in a particular Spanish time bank. Using data from users and transactions, we analyse the users’ profile as well as the determinants of providing and receiving different services. Our results show that the representative user is a Spanish female, not married, middle aged, highly educated and unemployed. We also find differences in the personal characteristics driving the supply and demand of services.