Working Papers del Dipartimento di Statistica precedente al DiSIA: abstract 2001
Copycats and Common Swings: the Impact of the Use of Forecasts in Information Sets
Giampiero M. Gallo, Clive W.J. Granger, Yongil Jeon
This paper presents evidence, using data from Consensus Forecasts, that there is an 'attraction' to conform to the mean forecasts; in other words, views expressed by other forecasters in the previous period influence individuals' current forecast. The paper then discusses--and provides further evidence on--two important implications of this finding. The first is that the forecasting performance of these groups may be severely affected by the detected imitation behavior and lead to convergence to a value which is not the 'right' target. Second, since the forecasts are not independent, the common practice of using the standard deviation from the forecasts' distribution as if they were standard errors of the estimated mean is not warranted.
Published as IMF Staff Papers, Volume 11, pp. 4-21, 2002, published.
Working Papers del Dipartimento di Statistica precedente al DiSIA
Modelling the Impact of Overnight Surprises on Intra-daily Volatility
Giampiero M. Gallo
In this paper we evaluate the impact that stock returns recorded between market closing and opening the next business day have on intra-daily volatility. A simple test shows that the estimated volatility clustering of the intra-daily returns may be affected by a market opening surprise bias. An extension of the standard GARCH model is suggested here to include the effect of this surprise and is applied on a sample of largely traded US stocks. The performance of two specifications in which this effect is included is evaluated in an out-of-sample forecasting exercise relative to their standard counterparts.
Published as Australian Economic Papers, Volume 40, pp. 567-580, 2001, published.
Working Papers del Dipartimento di Statistica precedente al DiSIA
Modelling the Impact of Overnight Surprises on Intra-daily Stock Returns
Giampiero M. Gallo, Yongmiao Hong, Tae-Hwy Lee
In this paper we examine under what circumstances the information accumulated during market closing time and conveyed to the price formation at market opening may be exploited to predict where the stock price will be at the end of the trading day. In our sample of three financial time series, we find that, in spite of linear uncorrelatedness, there exists a strong nonlinear dependence structure in the conditional mean of the intra-daily returns. To model this structure we use the functional-coefficient (FC) model of Cai, Fan, and Yao (2000) where the coefficients are time-varying and dependent on the state of stock return volatility. Out-of-sample forecast performances of the FC models and linear models where the coefficients are constant are also compared using the criteria of mean square forecast errors, trading returns, and directional forecasts.
Working Papers del Dipartimento di Statistica precedente al DiSIA
A Nonparametric Bayesian Approach to Detect the Number of Regimes in Markov Switching Models
Edoardo Otranto, Giampiero M. Gallo
The literature on Markov switching models is increasing and producing interesting results both at theoretical and applied levels. Most often the number of regimes, i.e., of data generating processes, is considered known; this strong hypothesis is adopted to somewhat bypass the nuisance parameter problem which affects hypothesis testing for the number of regimes. In this paper we take the view that some results derived from a nonparametric Bayesian approach provide a convenient way to deal with the issue of detecting the number of components in the mixture density, based on the assumption that the parameter distributions are generated by a Dirichlet process. The advantage is that we need no testing (in a classical sense) for the number of regimes, and the approach is not affected by a change point at the beginning or at the end of the sample. A Monte Carlo experiment provides some insights into the performance of the procedure. The potentiality of the approach is illustrated in reference with some well known results on exchange rate modelling.
Published as Econometric Reviews, Volume 21, Issue 4, pp. 477-496, 2002; link, published.
Working Papers del Dipartimento di Statistica precedente al DiSIA
Specification issues in stratified variance component ordinal response models
Leonardo Grilli, Carla Rampichini
The paper presents some criteria for the specification of ordinal variance component models when the second level units are grouped in few strata. The base model is specified using a latent variable approach, allowing the first level variance, the second level variance and the thresholds to vary according to the strata. However this model is not identifiable. The paper discusses some alternative assumptions that overcome the identification problem and illustrates a possible general strategy for the model selection. The proposed methodology is applied to the analysis of course program evaluations based on student ratings, referring to three different schools of the University of Florence. The adopted model takes into account both the ordinal scale of the ratings and the hierarchical nature of the phenomenon. In this framework, the identification of the latent variable distributions is crucial, since a different first level variance among the schools would change substantially the interpretation of model parameters. This is not the case in our application. Results show that both the latent average evaluation of the courses and the measurement scale vary with the school, suggesting to be careful in the interpretation of raw ratings based on an ordinal scale.
Published as Statistical Modelling, Volume 2, Issue 3, pp. 251-264, 2002; link, published.
Working Papers del Dipartimento di Statistica precedente al DiSIA
The evaluation of DNA evidence in pedigrees requiring population inference
Fabio Corradi, Giampietro Lago, Federico M. Stefanini
The evaluation of nuclear DNA evidences for identification purposes is here performed taking account of the uncertainty about population parameters. Graphical models are used to detail out the hypotheses under forensic debate, those that determine the pedigree structure. Graphs clarify the set of evidences that contribute to population inferences and they also describe the conditional independence structure of DNA evidences. Numerical illustrations are provided by reexamining three case studies taken from the literature. Our calculations of the weight of evidence differ from those given by the authors of case studies in the direction of more conservative values.
Working Papers del Dipartimento di Statistica precedente al DiSIA