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on Econometric Time Series |
| By: | Christian T. Brownlees (Università degli Studi di Firenze, Dipartimento di Statistica); Giampiero Gallo (Università degli Studi di Firenze, Dipartimento di Statistica "G. Parenti") |
| Abstract: | This paper assesses the performance of volatility forecasting using focused selection and combination strategies to include relevant explanatory variables in the forecasting model. The focused selection/combination strategies consist of picking up the model that minimizes the estimated risk (e.g. MSE) of a given smooth function of the parameters of interest to the forecaster. The proposed focused methods are compared with other strategies, including the well established AIC and BIC. The methodology is applied to a daily recursive 1--step ahead value--at--risk (VaR) forecasting exercise of 4 widely traded New York Stock Exchange stocks. Results show that VaR forecasts can significantly be improved upon using focused forecast strategies for the selection of relevant exogenous information. The set of explanatory variables that helps improving prediction is stock dependent. Traditional information criteria do not appear to be helpful in suggesting the inclusion of explanatory variables that actually improve prediction significantly. In line with recent theoretical findings, the predictive performance of the BIC appears to be modest. |
| Keywords: | Forecasting, Shrinkage Estimation, FIC, MEM, GARCH, ACD |
| JEL: | C22 C51 C53 |
| Date: | 2007–05 |
| URL: | http://d.repec.org/n?u=RePEc:fir:econom:wp2007_04&r=ets |
| By: | Giampiero Gallo (Università degli Studi di Firenze, Dipartimento di Statistica "G. Parenti"); Edoardo Otranto (Università di Sassari, Dipartimento di Economia, Impresa e Regolamentazione) |
| Abstract: | The transmission mechanisms of volatility between markets can be characterized within a new Markov Switching bivariate model where the state of one variable feeds into the transition probability of the state of the other. A number of model restrictions and hypotheses can be tested to stress the role of one market relative to another (spillover, interdependence, comovement, independence, Granger non causality). The model is estimated on the weekly high--low range of five Asian markets, assuming a central (but not necessarily dominant) role for Hong Kong. The results show plausible market characterizations over the long run with a spillover from Hong Kong to Korea and Thailand, interdependence with Malaysia and comovement with Singapore. |
| Keywords: | Markov Switching, multiple chains, volatility, spillover effect, comovements. |
| JEL: | C32 C52 C53 |
| Date: | 2007–10 |
| URL: | http://d.repec.org/n?u=RePEc:fir:econom:wp2007_11&r=ets |
| By: | Kruse, Robinson |
| Abstract: | This paper proposes a new unit root test against a non-linear exponential smooth transition autoregressive (ESTAR) model. The new test is build upon the non-standard testing approach of Abadir and Distaso (2007) who introduce a class of modified statistics for testing joint hypotheses when one of the alternatives is one-sided. In a Monte Carlo study the popular Dickey-Fuller type test proposed by Kapetanios et al. (2003) is compared with the new test. The results suggest that the new test is generally superior in terms of power. An application to a real effective exchange rate underlines its usefulness. |
| Keywords: | Unit root test, Nonlinearities, Smooth transition |
| JEL: | C12 C22 F31 |
| Date: | 2008–04 |
| URL: | http://d.repec.org/n?u=RePEc:han:dpaper:dp-398&r=ets |
| By: | Dominique Guegan (Centre d'Economie de la Sorbonne et Paris School of Economics) |
| Abstract: | In this paper we deal with the problem of non-stationarity encountered in a lot of data sets, mainly in financial and economics domains, coming from the presence of multiple seasonnalities, jumps, volatility, distorsion, aggregation, etc. Existence of non-stationarity involves spurious behaviors in estimated statistics as soon as we work with finite samples. We illustrate this fact using Markov switching processes, Stopbreak models and SETAR processes. Thus, working with a theoretical framework based on the existence of an invariant measure for a whole sample is not satisfactory. Empirically alternative strategies have been developed introducing dynamics inside modelling mainly through the parameter with the use of rolling windows. A specific framework has not yet been proposed to study such non-invariant data sets. The question is difficult. Here, we address a discussion on this topic proposing the concept of meta-distribution which can be used to improve risk management strategies or forecasts. |
| Keywords: | Non-stationarity, switching processes, SETAR processes, jumps, forecast, risk management, copula, probability distribution function. |
| JEL: | C32 C51 G12 |
| Date: | 2008–03 |
| URL: | http://d.repec.org/n?u=RePEc:mse:cesdoc:b08026&r=ets |
| By: | Abdou Kâ Diongue (Université Gaston Berger - Sénégal); Dominique Guegan (Centre d'Economie de la Sorbonne et Paris School of Economics); Rodney C. Wolff (School of Mathematical Sciences, QUT - Brisbane) |
| Abstract: | In this paper, we discuss the class of Bilinear GATRCH (BL-GARCH) models which are capable of capturing simultaneously two key properties of non-linear time series : volatility clustering and leverage effects. It has been observed often that the marginal distributions of such time series have heavy tails ; thus we examine the BL-GARCH model in a general setting under some non-Normal distributions. We investigate some probabilistic properties of this model and we propose and implement a maximum likelihood estimation (MLE) methodology. To evaluate the small-sample performance of this method for the various models, a Monte Carlo study is conducted. Finally, within-sample estimation properties are studied using S&P 500 daily returns, when the features of interest manifest as volatility clustering and leverage effects. |
| Keywords: | BL-GARCH process, elliptical distribution, leverage effects, maximum likelihood, Monte Carlo method, volatility clustering. |
| Date: | 2008–04 |
| URL: | http://d.repec.org/n?u=RePEc:mse:cesdoc:b08027&r=ets |
| By: | Laurent Ferrara (Banque de France et Centre d'Economie de la Sorbonne); Dominique Guegan (Centre d'Economie de la Sorbonne et Paris School of Economics) |
| Abstract: | Business surveys are an important element in the analysis of the short-term economic situation because of the timeliness and nature of the information they convey. Especially, surveys are often involved in econometric models in order to provide an early assessment of the current state of the economy, which is of great interest for policy-makers. In this paper, we focus on non-seasonally adjusted business surveys released by the European Commission. We introduce an innovative way for modelling those series taking the persistence of the seasonal roots into account through seasonal-cyclical long memory models. We empirically prove that such models produce more accurate forecasts than classical seasonal linear models. |
| Keywords: | Euro area, nowcasting, business surveys, seasonal, long memory. |
| JEL: | C22 C53 E32 |
| Date: | 2008–05 |
| URL: | http://d.repec.org/n?u=RePEc:mse:cesdoc:b08035&r=ets |
| By: | Ibrahim Ahamada (Centre d'Economie de la Sorbonne et Paris School of Economics); Philippe Jolivaldt (Centre d'Economie de la Sorbonne et Paris School of Economics) |
| Abstract: | Test for unit root based in wavelets theory is recently defined (Gençay and Fan, 2007). While the new test is supposed to be robust to the initial value, we bring out by contrast the significant effects of the initial value in the size and the power. We found also that both the wavelets unit root test and ADF test give the same efficiency if the data are corrected of the initial value. Our approach is based in monte carlo experiment. |
| Keywords: | Unit root tests, wavelets, monte carlo experiments, size-power curve. |
| JEL: | C12 C15 C16 C22 |
| Date: | 2008–03 |
| URL: | http://d.repec.org/n?u=RePEc:mse:cesdoc:v08032&r=ets |
| By: | Stephan Popp |
| Abstract: | The Perron test is the most commonly applied procedure to test for a unit root in the presence of a structural break of unknown timing in the trend function. Deriving the Perron-type test regression from an unobserved component model, it is shown that the test regression in fact is nonlinear in coefficient. Taking account of the nonlinearity leads to a test with properties that are exclusively assigned to Schmidt-Phillips LM-type unit root tests. |
| Keywords: | Unit root tests, nonlinear regression, structural breaks, innovational outliers |
| JEL: | C12 C22 |
| Date: | 2008–05 |
| URL: | http://d.repec.org/n?u=RePEc:rwi:repape:0045&r=ets |