Javier Perote

An investigation of insider trading profits in the Spanish stock market

Abstract This paper investigates the profitability and information content of insider trading in the Spanish stock market. Our results show that insiders earn excess profits when investing on corporate nonpublic information, while outsiders mimicking them fail to obtain those excess returns. The paper also investigates the relevance of a third party investing on the insider’s behalf. The study further focuses on some methodological aspects, such as the need to take estimation periods that are not affected by other events or by other prediction periods, and the need to allow volatility during insider trading events to have interday memory.

Measuring the Impact of Corporate Investment Announcements on Share Prices: The Spanish Experience

We bring together three disparate strands of literature to develop a comprehensive empirical framework to examine the efficiency of security analysts earnings forecasts in Singapore. We focus specifically on how the increased uncertainty and the negative market sentiment during the period of the Asian crisis affected the quality of earnings forecasts. While we find no evidence of inefficiencies in the pre-crisis period, our results suggest that after the onset of the crisis, analysts (1) issued forecasts that were systematically upward biased; (2) did not fully incorporate the (negative) earnings-related news; and (3) predicted earnings changes which proved too extreme.

Testing densities with financial data: an empirical comparison of the Edgeworth-Sargan density to the Student's t

The Edgeworth—Sargan density has been shown capable of capturing salient empirical regularities of financial data in some studies. The main purpose of the reported study is to compare its performance with other densities, most notably to the Student t. Both densities can account for thick tails, and asymmetry One important by product of the comparison is to test the existence of moments. The comparison of densities is carried out with daily financial observations, spanning 25 years of data from two major world stock markets. Attention is paid to the fitting of other empirical regularities, and especially to the peak, frequently found at the middle of the densities.

Strategy-proof estimators for simple regression

Abstract This paper introduces a whole class of estimators (clockwise repeated median estimators or CRM) for the simple regression model that are immune to strategic manipulation by the agents generating the data. We find that some well-known robust estimators proposed in the literature like the resistant line method are included in our family. Finally, we also undertake a Monte Carlo study to compare the distribution of some estimators that are robust to data manipulation with the OLS estimators under different scenarios.

Gram–Charlier densities: a multivariate approach

This paper introduces a new family of multivariate distributions based on Gram–Charlier and Edgeworth expansions. This family encompasses many of the univariate semi-non-parametric densities proposed in financial econometrics as marginal of its different formulations. Within this family, we focus on the analysis of the specifications that guarantee positivity to obtain well-defined multivariate semi-non-parametric densities. We compare two different multivariate distributions of the family with the multivariate Edgeworth–Sargan, Normal, Students t and skewed Students t in an in- and out-of-sample framework for financial returns data. Our results show that the proposed specifications provide a reasonably good performance, and would therefore be of interest for applications involving the modelling and forecasting of heavy-tailed distributions.

Multivariate Moments Expansion Density: Application of the Dynamic Equicorrelation Model

In this study, we propose a new semi-nonparametric (SNP) density model for describing the density of portfolio returns. This distribution, which we refer to as the multivariate moments expansion (MME), admits any non-Gaussian (multivariate) distribution as its basis because it is specified directly in terms of the basis density s moments. To obtain the expansion of the Gaussian density, the MME is a reformulation of the multivariate Gram-Charlier (MGC), but the MME is much simpler and tractable than the MGC when positive transformations are used to produce well-defined densities. As an empirical application, we extend the dynamic conditional equicorrelation (DECO) model to an SNP framework using the MME. The resulting model is parameterized in a feasible manner to admit two-stage consistent estimation, and it represents the DECO as well as the salient non-Gaussian features of portfolio return distributions. The in- and out-of-sample performance of a MME-DECO model of a portfolio of 10 assets demonstrates that it can be a useful tool for risk management purposes.

Forecasting Heavy-Tailed Densities with Positive Edgeworth and Gram-Charlier Expansions*

This article presents a new semi-nonparametric (SNP) density function, named Positive Edgeworth-Sargan (PES). We show that this distribution belongs to the family of (positive) Gram-Charlier (GC) densities and thus it preserves all the good properties of this type of SNP distributions but with a much simpler structure. The in- and out-of-sample performance of the PES is compared with symmetric and skewed GC distributions and other widely used densities in economics and finance. The results confirm the PES as a good alternative to approximate financial returns distribution, specially when skewness is not severe.

Higher-Order Risk Preferences, Constant Relative Risk Aversion and the Optimal Portfolio Allocation

We derive the conditions for the optimal portfolio choice within a constant relative risk aversion type of utility function considering alternative probability distributions that are able to capture the asymmetric and leptokurtic features of asset returns. We illustrate the role —beyond risk aversion— played by higher-order moments in the optimal decision to form a portfolio of risky assets. In particular, we show that higher-order risk attitudes such as prudence and temperance associated with the third and fourth moments of the distribution define different optimal portfolios than those constrained under risk aversion.

The productivity of top researchers: a semi-nonparametric approach

Research productivity distributions exhibit heavy tails because it is common for a few researchers to accumulate the majority of the top publications and their corresponding citations. Measurements of this productivity are very sensitive to the field being analyzed and the distribution used. In particular, distributions such as the lognormal distribution seem to systematically underestimate the productivity of the top researchers. In this article, we propose the use of a (log)semi-nonparametric distribution (log-SNP) that nests the lognormal and captures the heavy tail of the productivity distribution through the introduction of new parameters linked to high-order moments. The application uses scientific production data on 140,971 researchers who have produced 253,634 publications in 18 fields of knowledge (O’Boyle and Aguinis in Pers Psychol 65(1):79–119, 2012) and publications in the field of finance of 330 academic institutions (Borokhovich et al. in J Finance 50(5):1691–1717, 1995), and shows that the log-SNP distribution outperforms the lognormal and provides more accurate measures for the high quantiles of the productivity distribution.

Value at Risk of Non-Normal Portfolios

ABSTRACT This paper sheds light on the evaluation of portfolio risk when portfolio variables are not normal, as is usually the case with financial variables. The methodology proposed in these cases is based on the assumption of a more general distribution capable of incorporating the behaviour of such variables, especially at the tails: the so called Edgeworth-Sargan distribution. This density is preferable over other distributions, such as the Students t, when fitting high frequency financial variables, because of its flexibility for improving data fits by adding more parameters in a natural way. Furthermore, this distribution is easy to generalise to a multivariate context and, therefore, correlation coefficients among variables can be estimated efficiently. This article, therefore, provides new insights into VaR methodology by estimating the joint density of portfolio variables, and simultaneously calculating the right critical values of the underlying portfolio density. The empirical examples include the estimation and evaluation of different portfolios composed of stock indices for major financial markets.