A robust forward weighted Lagrange multiplier test for conditional heteroscedasticity

Grossi, Luigi; Laurini, Fabrizio

doi:10.1016/j.csda.2008.09.018

Cited by 15 publications

(10 citation statements)

References 25 publications

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“…In order to solve the above-mentioned optimization problem, construct Lagrange function as follows [33]:…”

Section: Methods Combining Subjective and Objective Assigning-weight Bmentioning

confidence: 99%

An Evaluating Method with Combined Assigning-Weight Based on Maximizing Variance

Liu

2015

Scientific Programming

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This paper proposes a combined assigning-weight approach to determine attribute weights in the multiattribute decision problems. The approach combines subjective weights and objective weights of attributes based on maximizing variance. Objective weights are determined by rough set method and subjective weights by Analytic Hierarchy Process. This new combination method may integrate the merits of both subjective and objective weighting methods. Empirical study shows that the new method can lead to more reasonable weighting results and decision.

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“…In order to solve the above-mentioned optimization problem, construct Lagrange function as follows [33]:…”

Section: Methods Combining Subjective and Objective Assigning-weight Bmentioning

confidence: 99%

An Evaluating Method with Combined Assigning-Weight Based on Maximizing Variance

Liu

2015

Scientific Programming

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“…Score tests using the added variables of de Jong and Penzer (1998) can be used to check for shocks, such as outliers, level shifts and switches. Grossi and Laurini (2009) extended the procedure to the financial time series which are likely to contain ARCH effects. In addition, the score test for transformations mentioned in Section 3.4 can be applied to time series, adding an extra recursion in the transition equation of the state-space formulation of the Kalman filter (Riani, 2009).…”

Section: The Analysis Of Time Seriesmentioning

confidence: 99%

The forward search: Theory and data analysis

Atkinson

Riani

Cerioli

2010

Journal of the Korean Statistical Society

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a b s t r a c tThe Forward Search is a powerful general method, incorporating flexible data-driven trimming, for the detection of outliers and unsuspected structure in data and so for building robust models. Starting from small subsets of data, observations that are close to the fitted model are added to the observations used in parameter estimation. As this subset grows we monitor parameter estimates, test statistics and measures of fit such as residuals. The paper surveys theoretical development in work on the Forward Search over the last decade. The main illustration is a regression example with 330 observations and 9 potential explanatory variables. Mention is also made of procedures for multivariate data, including clustering, time series analysis and fraud detection.

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“…The procedure is intended to provide a nominal size of 1%, meaning that the method should identify, on average, a signal once in every 100 outlier-free samples. Grossi and Laurini (2009) develop a soft weighting robust estimator based on the FS described by Atkinson and Riani (2000). Their method is based on simulation envelopes, where the studentized residuals obtained during each stage of the search are compared with simulated envelope bounds.…”

Section: The Fsmentioning

confidence: 99%

Identifying outliers in asset pricing data with a new weighted forward search estimator

Aronne

Grossi

Bressan

2020

Rev. contab. finanç.

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The purpose of this work is to present the Weighted Forward Search (FSW) method for the detection of outliers in asset pricing data. This new estimator, which is based on an algorithm that downweights the most anomalous observations of the dataset, is tested using both simulated and empirical asset pricing data. The impact of outliers on the estimation of asset pricing models is assessed under different scenarios, and the results are evaluated with associated statistical tests based on this new approach. Our proposal generates an alternative procedure for robust estimation of portfolio betas, allowing for the comparison between concurrent asset pricing models. The algorithm, which is both efficient and robust to outliers, is used to provide robust estimates of the models’ parameters in a comparison with traditional econometric estimation methods usually used in the literature. In particular, the precision of the alphas is highly increased when the Forward Search (FS) method is used. We use Monte Carlo simulations, and also the well-known dataset of equity factor returns provided by Prof. Kenneth French, consisting of the 25 Fama-French portfolios on the United States of America equity market using single and three-factor models, on monthly and annual basis. Our results indicate that the marginal rejection of the Fama-French three-factor model is influenced by the presence of outliers in the portfolios, when using monthly returns. In annual data, the use of robust methods increases the rejection level of null alphas in the Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model, with more efficient estimates in the absence of outliers and consistent alphas when outliers are present.

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A robust forward weighted Lagrange multiplier test for conditional heteroscedasticity

Cited by 15 publications

References 25 publications

An Evaluating Method with Combined Assigning-Weight Based on Maximizing Variance

An Evaluating Method with Combined Assigning-Weight Based on Maximizing Variance

The forward search: Theory and data analysis

Identifying outliers in asset pricing data with a new weighted forward search estimator

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