Διδακτορικές διατριβές
Μόνιμο URI για αυτήν τη συλλογήhttps://pyxida.aueb.gr/handle/123456789/14
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Πλοήγηση Διδακτορικές διατριβές ανά Συγγραφέα "Lamprinakou, Fiori"
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Τεκμήριο An econometric analysis of high-frequency financial data(12/09/2021) Lamprinakou, Fiori; Λαμπρινάκου, Φιόρη; Athens University of Economics and Business, Department of Statistics; Papaspiliopoulos, Omiros; Demiris, Nikolaos; Pedeli, Xanthi; Papastamoulis, Panagiotis; Tsionas, Mike; Damien, Paul; Dellaportas, PetrosWe present and compare observation driven and parameter driven models for predictinginteger price changes of high-frequency financial data. We explore Bayesian inferencevia Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) for the observationdriven model activity-direction-size (ADS), introduced by Rydberg and Shephard [1998a,2003]. We extend the ADS model by proposing a parameter driven model and use a Bernoulligeneralized linear model (GLM) with a latent process in the mean. We propose a new decompositionmodel that uses trade intervals and is applied on data that allow three possible tickmovements: one tick up price change, one tick down price change, or no price change. Wemodel each component sequentially using a Binomial generalized linear autoregressive movingaverage (GLARMA) model, as well as a GLM with a latent process in the mean. We perform asimulation study to investigate the effectiveness of the proposed parameter driven models usingdifferent algorithms within a Bayesian framework. We illustrate the analysis by modelling thetransaction-by-transaction data of of E-mini Standard and Poor’s (S&P) 500 index futures contracttraded on the Chicago Mercantile Exchange’s Globex platformbetween May 16th 2011 andMay 24th 2011. In order to assess the predictive performance, we compare the mean square error(MSE) and mean absolute error (MAE) criterion, as well as four scalar performance measures,namely, accuracy, sensitivity, precision and specificity derived from the confusion matrix.