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.

Rental Housing Discrimination and the Persistence of Ethnic Enclaves

We conduct a field experiment to show that discrimination in the rental market represents a significant obstacle for the geographical assimilation process by immigrants. We employ the Internet platform to identify vacant rental apartments in different areas of the two largest Spanish cities, Madrid and Barcelona. We send emails showing interest in the apartments and signal the applicants' ethnicity by using native and foreign-sounding names. We find that, in line with previous studies, immigrants face a differential treatment when trying to rent an apartment. Our results also indicate that this negative treatment varies considerably with the concentration of immigrants in the area. In neighborhoods with a low presence of immigrants the response rate is 30 percentage points lower for immigrants than for natives, while this differential disappears when the immigration share reaches 50%. We conclude that discriminatory practices in the rental housing market contribute to perpetuate the ethnic spatial segregation observed in large cities.immigration, discrimination, spatial segregation

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

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

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.

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 assetsGARCH, Long Memory

Outliers and misleading leverage effect in asymmetric GARCH-type models

This paper illustrates how outliers can affect both the estimation and testing of leverage effect by focusing on the TGARCH model. Three estimation methods are compared through Monte Carlo experiments: Gaussian Quasi-Maximum Likelihood, Quasi-Maximum Likelihood based on the Student-t likelihood and Least Absolute Deviation method. The empirical behavior of the t-ratio and the Likelihood Ratio tests for the significance of the leverage parameter is also analyzed. Our results put forward the unreliability of Gaussian Quasi-Maximum Likelihood methods in the presence of outliers. In particular, we show that one isolated outlier could hide true leverage effect whereas two consecutive outliers bias the estimated leverage coefficient in a direction that crucially depends on the sign of the first outlier and could lead to wrongly reject the null of no leverage effect or to estimate asymmetries of the wrong sign. By contrast, we highlight the good performance of the robust estimators in the presence of one isolated outlier. However, when there are patches of outliers, our findings suggest that the sizes and powers of the tests as well as the estimated parameters based on robust methods may still be distorted in some cases. We illustrate these results with two series of daily returns.Generalitat Valenciana, Funder Id: http://dx.doi.org/10.13039/501100003359, Grant Number: AICO/2019/295. Consejería de Educación, Junta de Castilla y León, Funder Id: http://dx.doi.org/10.13039/501100008431, Grant Number: VA148G18. Spanish Government, Grant Number: ECO2017-87069-P and ECO2016-77900-P

Estimating VAR-MGARCH models in multiple steps

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.Financial support from IVIE (Instituto Valenciano de Investigaciones Económicas) to the project “Estimating Multivariate GARCH Models in Multiple Steps with an Application to Stock Markets” is gratefully acknowledged. We also acknowledge the Spanish Government for grant ECO2011-29751

Information and discrimination in the rental housing market: evidence from a field experiment

This paper investigates the effect of disclosing information on the discriminatory behaviour against immigrants in the Spanish rental market. We conduct a field experiment where emails are sent showing interest on vacant rental apartments. Fictitious applicants whose names represent different ethnic groups send emails with different amount of information about their ability to pay the rent. Our results show that applicants with a Moroccan sounding name are 15 percentage points less likely to be contacted by the property owner than those with a Spanish name. We also find that revealing positive information about the socioeconomic status of the Moroccan candidate increases the probability of being contacted by 8 percentage points. However, the information revealed does not completely eliminate discriminatory behavior, suggesting the presence of negative attitudes towards immigrants.Discrimination, migration, rental market, field experiment

Rental housing discrimination and the persistence of ethnic enclaves

We conduct a field experiment to show that discrimination in the rental market represents a significant obstacle for the geographical assimilation process by immigrants. We employ the Internet platform to identify vacant rental apartments in different areas of the two largest Spanish cities, Madrid and Barcelona. We send emails showing interest in the apartments and signal the applicants’ ethnicity by using native and foreign-sounding names. We find that, in line with previous studies, immigrants face a differential treatment when trying to rent an apartment. Our results also indicate that this negative treatment varies considerably with the concentration of immigrants in the area. In neighbourhoods with a low presence of immigrants the response rate is 30 percentage points lower for immigrants than for natives, while this differential disappears when the immigration share reaches 50%. We conclude that discriminatory practices in the rental housing market contribute to perpetuate the ethnic spatial segregation observed in large cities.immigration, discrimination, spatial segregation.