1,721,144 research outputs found
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 smooth transition threshold autoregressive models for electricity prices
No full textIn this paper we suggest the use of robust STAR (Smooth Transition AutoRegressive) processes to model and forecast electricity prices observed on deregulated markets. The robustness of the model is achieved by extending to time series the M-type estimator based on the polynomial weighting function first introduced for independent multivariate data. The robust M-STAR estimator can be considered as a generalization of the robust SETAR estimator [1], because in STAR processes the change from one regime to another is ruled by a smooth function rather than by a fixed threshold. The main advantage of estimating robust STAR models is the possibility to capture two very well-known stylized facts of electricity prices: nonlinearity produced by changes of regimes and the presence of sudden spikes due to inelasticity of demand. The forecasting performance of the model is assessed through an application to the Italian electricity market (IPEX). By means of prediction performance indexes and tests, robust and non-robust STAR models for electricity prices are compared
Robust self exciting Threshold autoregressive models for electricity prices
No full textIn this paper we suggest the use of robust GM-SETAR (Self Exciting Threshold AutoRegressive) processes to model and forecast electricity prices observed on deregulated markets. The robustness of the model is achieved by extending to time series the generalized M-type (GM) estimator first introduced for independent multivariate data. As it has been shown in a very recent paper [1], the polynomial weighting function over-performs the classical ordinary least squares method when extreme observations are present. The main advantage of estimating robust SETAR models is the possibility to capture two very well-known stylized facts of electricity prices: nonlinearity produced by changes of regimes and the presence of sudden spikes due to inelasticity of demand. The forecasting performance of the model applied to the Italian electricity market (IPEX) is improved by the introduction of predicted demand as an exogenous regressor. The availability of this regressor is a particular feature of the Italian market. By means of prediction performance indexes and tests, it will be shown that this regressor plays a crucial role and that robust methods improve the overall forecasting performance of the model. © 2014 IEEE
Analyzing Financial Time Series through Robust Estimators
No full textIn this paper we suggest an extension of the forward search
methodology to GARCH models which are often used for forecasting stock market volatility. It is frequently found that estimated residuals from GARCH models have excess kurtosis, even when one allows for conditional t-distributed errors. Some papers have appeared on outlier detection in GARCH models
(Van Dijk et al., 1999; Franses and Ghijsels, 1999) but the proposed methods are iterative and may suffer from masking effects. The forward search is a method for determining the effect of outliers on fitted parameters and for detecting
also masked outliers. In the case of GARCH models outliers are strictly related to extreme observations which are responsible for the well-known volatility clustering of financial returns. It is possible, through the forward search, to visualize the effect on estimated parameters of patches of extremal observations
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
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
Robust estimation of regime switching models
No full textIt is well known that generalized-M (GM) estimators for linear models are consistent and lead to a small loss of efficiency with respect to least squares (LS) estimator. When they are extended to threshold models the consistency of GM estimators is guaranteed only under certain objective functions. In this paper we explore, in a simulation experiment, the loss of consistency of GM-SETAR estimator under different objective functions, time-series length, parameter combinations and type of contaminations. Finally the best robust estimator is applied to study the dynamic of electricity prices where regime switching and high spikes are widely observed features
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