DETECTING LEVEL SHIFTS IN THE PRESENCE OF CONDITIONAL HETEROSCEDASTICITY

The objective of this paper is to analyze the finite sample performance of two variants of the likelihood ratio test for detecting a level shift in uncorrelated conditionally heteroscedastic time series. We show that the behavior of the likelihood ratio test is not appropriate in this context whereas if the test statistic is appropriately standardized, it works better. We also compare two alternative procedures for testing for several level shifts. The results are illustrated by analyzing daily returns of exchange rates.EGARCH, GARCH, Likelihood Ratio, Stochastic Volatility.

SPURIOUS AND HIDDEN VOLATILITY

This paper analyzes the effects caused by outliers on the identification and estimation of GARCH models. We show that outliers can lead to detect spurious conditional heteroscedasticity and can also hide genuine ARCH effects. First, we derive the asymptotic biases caused by outliers on the sample autocorrelations of squared observations and their effects on some homoscedasticity tests. Then, we obtain the asymptotic biases of the OLS estimates of ARCH(p) models and analyze their finite sample behaviour by means of extensive Monte Carlo experiments. The finite sample results are extended to GLS and ML estimates ARCH(p) and GARCH(1,1) models.GARCH, Outliers, Heteroscedasticity

Estimating and Forecasting GARCH Volatility in the Presence of Outiers

The main goal when fitting GARCH models to conditionally heteroscedastic time series is to estimate the underlying volatilities. It is well known that outliers affect the estimation of the GARCH parameters. However, little is known about their effects when estimating volatilities. In this paper, we show that when estimating the volatility by using Maximum Likelihood estimates of the parameters, the biases incurred can be very large even if estimated parameters have small biases. Consequently, we propose to use robust procedures. In particular, a simple robust estimator of the parameters is proposed and shown that its properties are comparable with other more complicated ones available in the literature. The properties of the estimated and predicted volatilities obtained by using robust filters based on robust parameter estimates are analyzed. All the results are illustrated using daily S&P500 and IBEX35 returns.Heteroscedasticity, M-estimator, QML estimator, Robustness, Financial Markets

DETECTING LEVEL SHIFTS IN THE PRESENCE OF CONDITIONAL HETEROSCEDASTICITY.

The objective of this paper is to analyze the finite sample performance of two variants of the likelihood ratio test for detecting a level shift in uncorrelated conditionally heteroscedastic time series. We show that the behavior of the likelihood ratio test is not appropriate in this context whereas if the test statistic is appropriately standardized, it works better. We also compare two alternative procedures for testing for several level shifts. The results are illustrated by analyzing daily returns of exchange rates.

A logic approach for exceptions and anomalies in association rules

Association rules have been used for obtaining information hidden in a database. Recent researches have pointed out that simple associations are insu cient for representing the diverse kinds of knowledge collected in a database. The use of exceptions and anomalies deal with a di erent type of knowledge sometimes more useful than simple associations. Moreover ex- ceptions and anomalies provide a more comprehensive understanding of the information provided by a database. This work intends to go deeper in the logic model studied in [5]. In the model, association rules can be viewed as general relations between two or more attributes quanti ed by means of a convenient quanti er. Using this formulation we establish the true semantics of the distinct kinds of knowledge we can nd in the database hidden in the four folds of the contingency table. The model is also useful for providing some measures for assessing the validity of those kinds of rulesPeer Reviewe

Fairy circle landscapes under the sea

Short-scale interactions yield large-scale vegetation patterns that, in turn, shape ecosystem function across landscapes. Fairy circles, which are circular patches bare of vegetation within otherwise continuous landscapes, are characteristic features of semiarid grasslands. We report the occurrence of submarine fairy circle seascapes in seagrass meadows and propose a simple model that reproduces the diversity of seascapes observed in these ecosystems as emerging from plant interactions within the meadow. These seascapes include two extreme cases, a continuous meadow and a bare landscape, along with intermediate states that range from the occurrence of persistent but isolated fairy circles, or solitons, to seascapes with multiple fairy circles, banded vegetation, and "leopard skin" patterns consisting of bare seascapes patterns consisting of bare seascapes dotted with plant patches. The model predicts that these intermediate seascapes extending across kilometers emerge as a consequence of local demographic imbalances along with facilitative and competitive interactions among the plants with a characteristic spatial scale of 20 to 30 m, consistent with known drivers of seagrass performance. The model, which can be extended to clonal growth plants in other landscapes showing fairy rings, reveals that the different seascapes observed hold diagnostic power as to the proximity of seagrass meadows to extinction points that can be used to identify ecosystems at risks

OUTLIERS AND CONDITIONAL AUTOREGRESSIVE HETEROSCEDASTICITY IN TIME SERIES

This paper reviews the literature on GARCH-type models proposed to represent the dynamic evolution of conditional variances. Effects of level outliers on the diagnostic and estimation of GARCH models are also studied. Both outliers and conditional heteroscedasticity can generate time series with excess kurtosis and autocorrelated squared observations. Consequently, both phenomena can be confused. However, since outliers are generated by unexpected events and the conditional variances are predictable, it is important to identify which one is producing the observed features in the data. We compare two alternative procedures for dealing with the simultaneous presence of outliers and conditional heteroscedasticity in time series. The first one is to clean the series of outliers before fitting a GARCH model. The second is to estimate first the GARCH model and then to clean of outliers by using the residuals adjusted by its conditional variance. It is shown that both approaches may result in different estimated conditional variances.

Un procedimiento simple para evaluar el comportamiento de aceites y grasas a temperaturas de fritura

5 pages, 5 tables.[EN] A standard procedure is proposed to evaluate the performance of fats and oils at frying temperatures, taking the advantages provided by the Rancimat apparatus, i. e., standard vessels, temperature correction and temperature homogenity in all vessels resulting from the particular characteristics of the heating block. The results obtained in oil samples of 8 g heated at 180° C for 10 h in triplicate gave coefficients of variation lower than 6% for total polar compounds and polymers. In case of limited amount of oil, it is additionally proposed to use only 2 g of sample provided that a similar surface-to-oil volume ratio is maintained, and coefficients of variation of the same order than those for 8 g samples were thus obtained. Advantages of the procedure as well as potential applications for evaluation of frying fats and oils are Included. As an example, the effect of a-tocopherol on performance of sunflower oils was analyzed.[ES] Se propone un procedimiento estándar para evaluar el comportamiento de aceites y grasas a temperaturas de fritura. En este procedimiento se utilizan las ventajas del aparato Rancimat, que permite el uso de tubos estándar, la corrección de la temperatura, en su caso, y la igualdad de temperatura en todos los tubos dadas las características del bloque de calentamiento. De los resultados obtenidos en muestras de 8 g de aceite calentadas a 180° C durante 10 h, analizadas por triplicado, se obtuvieron coeficientes de variación inferiores al 6% para la determinación de compuestos polares y polímeros. En caso de limitación en la cantidad de aceite, se propone utilizar 2 g de muestra, manteniendo similares valores para la relación superficie a volumen de aceite, lo que permite obtener valores de alteración y coeficientes de variación del mismo orden. Se analizan finalmente las ventajas globales del procedimiento y sus distintas posibilidades en la evaluación de grasas de fritura. Como ejemplo, se aplica el procedimiento a la evaluación del efecto de los antioxidantes naturales de los aceites de girasol.This study was funded by CICYT (Project ALI-95- 0736). Daniel Barrera Arellano was supported by a postdoctoral fellowship (95/9304-2) from Fundaçao de Apoío à Pesquisa do Estado de Sao Paulo (FAPESP).Peer reviewe

IS STOCHASTIC VOLATILITY MORE FLEXIBLE THAN GARCH?

This paper compares the ability of GARCH and ARSV models to represent adequately the main empirical properties usually observed in high frequency financial time series: high kurtosis, small first order autocorrelation of squared observations and slow decay towards zero of the autocorrelation coefficients of squared observations. We show that the ARSV(1) model is more flexible than the GARCH(1,1) model in the sense that it is able to generate series with higher kurtosis and smaller first order autocorrelation of squares for a wider variety of parameter specifications. Our results may help to clarify some puzzles raised in the empirical analysis of real financial time series.