1,720,979 research outputs found
A robust forward weighted Lagrange multiplier test for conditional heteroscedasticity
No full textStatistical tests routinely adopted for detecting nonlinear components in time series rely on the auxiliary regression of ARMA lagged residuals, and the Lagrange multiplier test to detect ARCH components is an example. The size distortion of such test suggests adopting a weighted test, where the weights are computed through a forward search algorithm. Simulations show that the forward weighted robust test is preferable to the classical Lagrange test and to existing robust tests, which are based on backward weighted regression or on estimated autocorrelation function. The forward weighted robust test is applied to daily financial and quarterly macroeconomic time series, showing its usefulness in detecting ARCH effects, even when outliers are present. © 2008 Elsevier B.V. All rights reserved
Smoothing sample extremes: the mixed model approach
No full textNonparametric regression for sample extremes can be performed using a variety of techniques. The penalized spline approach for the Poisson point process model is considered. The generalized linear mixed model representation for the spline model, with its Bayesian approach to inference, turns out to be a very flexible framework. Monte Carlo Markov chain algorithms are employed for exploration of the posterior distribution. The overall performance of the method is tested on simulated data. Two real data applications are also discussed for modeling trend of intensity of earthquakes in Italy and for assessing seasonality and short term trend of summer extreme temperatures in Milan, Italy
Robust estimation of efficient mean-variance frontiers
No full textStandard methods for optimal allocation of shares in a financial portfolio are determined by second-order conditions which are very sensitive to outliers. The well-known Markowitz approach, which is based on the input of a mean vector and a covariance matrix, seems to provide questionable results in financial management, since small changes of inputs might lead to irrelevant portfolio allocations. However, existing robust estimators often suffer from masking of multiple influential observations, so we propose a new robust estimator which suitably weights data using a forward search approach. A Monte Carlo simulation study and an application to real data show some advantages of the proposed approach. © 2011 Springer-Verlag
Analysis of economic time series: Effects of extremal observations on testing heteroscedastic components
No full textMacroeconomic and financial time series are often tested for the presence of non-linearity effects. Sometimes, small patches of extremal observations may wrongly influence non-linearity tests. In this paper, a robust analysis of the Lagrange multiplier (LM) test for GARCH components is suggested. With Monte-Carlo simulation we show that extreme observations might cause over-estimation of the number of GARCH components, with the main contribution consisting by introducing the forward search method into the GARCH model family. Using robust estimators of regression coefficients and graphical displays of results, the effect of influential observations on estimates can be efficiently monitored. Analysing macroeconomic and financial time series we show that identifying the order of a GARCH model can be unduly influenced by a few isolated large values, and extremal observations affect p-values and t-statistics in an unexpected manner. Copyright © 2004 John Wiley & Sons, Ltd
Robust portfolio asset allocation
No full textSelection of stocks in a portfolio of shares represents a very interesting problem of “optimal classification”. Often such optimal allocation is determined by second-order conditions which are very sensitive to outliers. Existing robust estimator of the covariance matrix seems to provide poor results in financial management, so we propose an alternative way of weighting observations by using a forward search approach. An application to real data, which shows the advantages of the proposed approach is reported at the end of this work
Robust portfolio asset allocation
No full textSelection of stocks in a portfolio of shares represents a very interesting problem of 'optimal classification'. Often such optimal allocation is determined by second-order conditions which are very sensitive to outliers. Classical Markowitz estimators of the covariance matrix seem to provide poor results in financial management, so we propose an alternative way of weighting observations by using a forward search approach. An application to real data, which shows the advantages of the proposed approach is given at the end of this work. © Springer-Verlag Berlin Heidelberg 2011
Analysis of economic time series: effects of extremal observations on testing heteroscedastic components
No full textMacroeconomic and financial time series are often tested for the presence of non-linearity effects. Sometimes, small patches of extremal observations may wrongly influence non-linearity tests. In this paper, a robust analysis of the Lagrange multiplier (LM) test for GARCH components is suggested. Using Monte-Carlo simulations we show that extreme observations might cause over-estimation of the number of GARCH components, with the main contribution consisting by introducing the forward search method into the GARCH model family. Using robust estimators of regression coefficients and graphical displays of results, the effect of influential observations on estimates can be efficiently monitored. Analysing macroeconomic and financial time series we show that identifying the order of a GARCH model can be unduly influenced by a few isolated large values, and extremal observations affect p-values and t-statistics in an unexpected manner
Store flyers: managing spatial distribution under budget constraints
Full text linkPurpose: The study develops a decision support system for the spatial distribution of store flyers, identifying a number of factors related to the demand and the competition influencing the complexities of their allocation to the target population. Design/methodology/approach: The model was developed incorporating the insights found in existing marketing literature and bypassing the limitations of the managerial practices. To this end, an in-depth discussion with a panel of retailers was held. The model was tested in collaboration with a retail chain. Findings: The proposed system is flexible and provides an almost endless array of solutions in accordance with the retailer's strategic approach to the market. It captures the key trade-offs that need to be made during the decision-making process of a retailer with limited marketing resources. Practical implications: The traditional managerial approach, based on a set of operational steps, is overtaken by a model that systematically considers the interrelationships between the decision-making factors involved. Originality/value: This is the first attempt to analyse spatial distribution of store flyers, a topic that has yet to be explored in retail marketing research. The paper conceptualises the key variables which affect the optimisation problem and reviews the different streams of extant research to obtain the appropriate insights
Robust detection of nonlinearity in financial time series
No full textWe review the Lagrange Multiplier (LM) test for detection of non-linear features through a robust analysis with forward search tool of Atkinson and Riani (2000).
We focus on test for detection of ARCH components. Robust estimators of regression coefficients and graphical tools give insights into the structure and the effect of influential
observations. We show how difficult can be to identify the order of the underlying ARCH model. Influential observations are monitored through t−statistics of the LM test, yielding to difficult identification of ARCH order
OPTIMAL PORTFOLIO ALLOCATION WITH CVAR: A ROBUSTAPPROACH
Full text linkThe paper discuss the sensitivity to the presence of outliers of the portfolio optimization procedure based on the expected shortfall as a measure of risk. A robust approach based on the forward search is then suggested which seems to give quite good results
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