Impairment losses: causes and impacts

Purpose - To analyze recognition of impairment losses in tangible and intangible assets, and their relevance to investors in companies listed in the Lisbon and Madrid Stock Exchange (2007-2011).Methodology - Quantitative analysis of a panel data sample of 80 companies listed in the Lisbon and Madrid Stock Exchange (2007-2011) was carried out. Panel data linear and non-linear regression models were estimated.Findings - We found that the amount of impairment losses showed an upward trend, and that these losses are most significant among intangibles, especially goodwill (GW). We also found that the probability of recognition of impairment losses is positively influenced by the dimension of entities and negatively by market value (p < 0.10). Portuguese export-oriented companies have a higher probability of not recognizing impairments. However, Portuguese companies with higher market values have greater probability of recognizing impairment losses, contrary to the sample as a whole, in which the relationship is negative (p < 0.10). The results also suggest that there is a smoothing effect on results because of impairments, especially in IBEX35 companies. As to the relevance of impairment losses to market value, we confirm a significant negative relationship, in line with conclusions from previous studies.Originality/value - This study contributes to the introduction of the cultural factor in this analysis, highlighting the differentiated behaviors between Portuguese and Spanish companies

Finite sample properties of a QML estimator of Stochastic Volatility models with long memory

We analyse the finite sample properties of a QML estimator of LMSV models. We show up its poor performance for realistic parameter values. We discuss an identification problem when the volatility has a unit root. An empirical analysis illustrates our findings.Publicad

Modelling intra-daily volatility by functional data analysis: an empirical application to the spanish stock market

We propose recent functional data analysis techniques to study the intra-daily volatility. In particular, the volatility extraction is based on functional principal components and the volatility prediction on functional AR(1) models. The estimation of the corresponding parameters is carried out using the functional equivalent to OLS. We apply these ideas to the empirical analysis of the IBEX35 returns observed each _ve minutes. We also analyze the performance of the proposed functional AR(1) model to predict the volatility along a given day given the information in previous days for the intra-daily volatility for the firms in the IBEX35 Madrid stocks inde

Forecasting international stock market correlations: does anything beat a CCC?

It is well known that the correlation between financial series varies over time. Here, the forecasting performance of different time-varying correlation models is compared for cross-country correlations of weekly G5 and daily European stock market indices. In contrast to previous studies only the correlation and not the entire covariance matrix is forecasted and multi-step forecasts are considered. The forecast comparison is done by considering statistical and economic criteria. The results suggest that under a statistical criterion time-varying correlation models perform quite well for weekly data, but cannot outperform the constant correlation model for daily data. Considering economic criteria it is hard to beat a constant correlation model. --dynamic conditional correlation,regime switching,stochastic correlation,smooth correlations,indirect model comparison,portfolio construction

Autorregresive conditional volatility, skewness and kurtosis

This paper proposes a GARCH-type model allowing for time-varying volatility, skewness and kurtosis. The model is estimated assuming a Gram-Charlier series expansion of the normal density function for the error term, which is easier to estimate than the non-central t distribution proposed by Harvey and Siddique (1999). Moreover, this approach accounts for time-varying skewness and kurtosis while the approach by Harvey and Siddique (1999) only accounts for nonnormal skewness. We apply this method to daily returns of a variety of stock indices and exchange rates. Our results indicate a significant presence of conditional skewness and kurtosis. It is also found that specifications allowing for time-varying skewness and kurtosis outperform specifications with constant third and fourth moments.skewness and kurtosis, conditional volatility, Gram-Charlier series expansion, stock indices

Modelling intra-daily volatility by functional data analysis: an empirical application to the spanish stock market

We propose recent functional data analysis techniques to study the intra-daily volatility. In particular, the volatility extraction is based on functional principal components and the volatility prediction on functional AR(1) models. The estimation of the corresponding parameters is carried out using the functional equivalent to OLS. We apply these ideas to the empirical analysis of the IBEX35 returns observed each _ve minutes. We also analyze the performance of the proposed functional AR(1) model to predict the volatility along a given day given the information in previous days for the intra-daily volatility for the firms in the IBEX35 Madrid stocks indexMarket microstructure, Ultra-high frequency data, Functional data analysis,Functional AR(1) model

Bootstrap prediction intervals for VaR and ES in the context of GARCH models

In this paper, we propose a new bootstrap procedure to obtain prediction intervals of future Value at Risk (VaR) and Expected Shortfall (ES) in the context of univariate GARCH models. These intervals incorporate the parameter uncertainty associated with the estimation of the conditional variance of returns. Furthermore, they do not depend on any particular assumption on the error distribution. Alternative bootstrap intervals previously proposed in the literature incorporate the first but not the second source of uncertainty when computing the VaR and ES. We also consider an iterated smoothed bootstrap with better properties than traditional ones when computing prediction intervals for quantiles. However, this latter procedure depends on parameters that have to be arbitrarily chosen and is very complicated computationally. We analyze the finite sample performance of the proposed procedure and show that the coverage of our proposed procedure is closer to the nominal than that of the alternatives. All the results are illustrated by obtaining one-step-ahead prediction intervals of the VaR and ES of several real time series of financial returns.Expected Shortfall, Feasible Historical Simulation, Hill estimator, Parameter uncertainty, Quantile intervals, Value at Risk

Multifractality and long memory of a financial index

In this paper we will try to assess the multifractality displayed by the high-frequency returns of Madrid's Stock Exchange IBEX35 index. A Multifractal Detrended Fluctuation Analysis shows that this index has a wide singularity spectrum which is most likely caused by its long memory. Our findings also show that this long-memory can be considered as the superposition of a high-frequency component (related to the daily cycles of arrival of information to the market), over a slowly-varying component that reverberates for long periods of time and which shows no apparent relation with human economic cycles. This later component is therefore postulated to be endogenous to market's dynamics and to be also the most probable source of some of the stylized facts commonly associated with financial time series.Comment: 12 pages, 7 figures, typed in AMS-LaTe

GARCH CLASS MODELS PERFORMANCE IN CONTEXT OF HIGH MARKET VOLATILITY

In the presented paper GARCH class models were considered for describing and forecasting market volatility in context of the economic crisis. The sample composition was designed to emphasize models performance in two groups of markets: well-developed and transition. As a preview to our results, we presented the procedure of model selection form the GARCH family. We distinguished three subperiods in the time series in a way that the dependencies between forecast outcomes and a scale of market volatility were emphasized. The comparison of the forecast errors revealed a serious problem of volatility prediction in times of high market instability. The crisis impact was particularly apparent in transition markets. Our findings showed that GARCH models allowed risk control, with risk understood as a relation of forecast error to the level of predicted volatility