FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2605014166> ?p ?o ?g. }

• W2605014166 abstract "The availability of tick-by-tick data has led researchers to a new era of modeling the transaction of financial trades. Up until the early 90s time series data were aggregated and modeling and estimation were performed upon these aggregated data sets. This would hide a lot of information concerning a particular trade. For example, variation in the price during the day of a particular trade may not be captured if the total volume per day is used for modeling. Also there may be some other exogenous variables affecting trading patterns and they also may not be captured. For example, if the day is the width of the interval concerned, most of the information during the day might be lost due to aggregation of the time series data. One of the main features of these high frequency data is that they are irregularly time-spaced. This feature has challenged researchers as the standard econometric techniques of modeling time series data, especially ones with fixed time intervals, cannot be applied directly. These high frequency data have opened a new path for researchers to model time series data by taking most of the intraday information into account. The auto regressive conditional duration (ACD) models provide a flexible way of modeling these irregularly time-spaced data. In the recent years, the ACD approach has become a very attractive way of modeling high frequency data. There are a number of ACD models available for univariate data, and only a very few for multivariate data in the literature. Further, a few ACD models have constraints imposed on the parameters of the model. There are a number of estimation methods proposed for estimating ACD models and they include : Maximum Likelihood (ML) and Quasi-Maximum Likelihood (QML), which are fully parametric, and semiparametric methods. For example, the fully parametric methods assume that the distributions are correctly specified, while the semiparametric method of Drost & Werker (2004) was proposed for estimating ACD models when the errors are either independent and identically distributed (iid) or non-iid. Given that the specification of the ACD model is correct, it is crucial to identify the correct distributional form for the innovations. As distributional assumptions are very important in the estimation of ACD models, any form of misspecification would lead to inconsistent estimators and hence inaccurate statistical inference and forecasts. Assessing the performances of density forecasts in the context of ACD models is largely unknown. Multivariate generalizations of ACD models are fundamental to our understanding of the relationships between financial market variables such as prices, quotes and volumes. However, it is very difficult to model more than one series of durations because they could have been recorded on very different time scales. The existing generalizations of ACD models to multivariate settings are not straightforward, because they often involve strong assumptions about exogeneity or are very complex models. The objective of this thesis is threefold. We first compare the performances of existing estimation methods for ACD models with iid and non-iid innovations in terms of mean square error (MSE), and show that the semiparametric method of Drost & Werker (2004) performs well compared to ML and QML methods, particularly when the innovations are non-iid. Then, we make some improvements on the existing semiparametric method, by accommodating the constraints imposed on the ACD models parameters. An extensive simulation study conducted in this thesis shows that our proposed estimator performs better overall than the semiparametric estimator of Drost & Werker (2004). Mathematical proofs of the results of new constrained semiparametric estimation method are provided. Secondly, we assess the performances of density forecasts in the standard ACD models. We conducted a comprehensive simulation study to compare parametric and non-parametric density forecasts. Results of the simulation study show that both the nonparametric gamma kernel of Chen (2000b) and the fully parametric gamma distributions perform well in all the cases we considered. Finally, we propose a new methodology for modelling multivariate duration data in a flexible way while imposing less assumptions on exogeneity. Using an empirical application we illustrate the usefulness of the new method for modeling multivariate duration data." @default.
• W2605014166 created "2017-04-14" @default.
• W2605014166 creator A5021549011 @default.
• W2605014166 creator A5091074809 @default.
• W2605014166 date "2017-01-13" @default.
• W2605014166 modified "2023-09-27" @default.
• W2605014166 title "New approaches for statistical inference in duration models" @default.
• W2605014166 doi "https://doi.org/10.4225/03/587848e0a5cbb" @default.
• W2605014166 hasPublicationYear "2017" @default.
• W2605014166 type Work @default.
• W2605014166 sameAs 2605014166 @default.
• W2605014166 citedByCount "0" @default.
• W2605014166 crossrefType "dissertation" @default.
• W2605014166 hasAuthorship W2605014166A5021549011 @default.
• W2605014166 hasAuthorship W2605014166A5091074809 @default.
• W2605014166 hasConcept C112758219 @default.
• W2605014166 hasConcept C119857082 @default.
• W2605014166 hasConcept C124101348 @default.
• W2605014166 hasConcept C124952713 @default.
• W2605014166 hasConcept C142362112 @default.
• W2605014166 hasConcept C143724316 @default.
• W2605014166 hasConcept C149782125 @default.
• W2605014166 hasConcept C151406439 @default.
• W2605014166 hasConcept C151730666 @default.
• W2605014166 hasConcept C154945302 @default.
• W2605014166 hasConcept C161584116 @default.
• W2605014166 hasConcept C180075932 @default.
• W2605014166 hasConcept C199163554 @default.
• W2605014166 hasConcept C2776214188 @default.
• W2605014166 hasConcept C33923547 @default.
• W2605014166 hasConcept C41008148 @default.
• W2605014166 hasConcept C86803240 @default.
• W2605014166 hasConceptScore W2605014166C112758219 @default.
• W2605014166 hasConceptScore W2605014166C119857082 @default.
• W2605014166 hasConceptScore W2605014166C124101348 @default.
• W2605014166 hasConceptScore W2605014166C124952713 @default.
• W2605014166 hasConceptScore W2605014166C142362112 @default.
• W2605014166 hasConceptScore W2605014166C143724316 @default.
• W2605014166 hasConceptScore W2605014166C149782125 @default.
• W2605014166 hasConceptScore W2605014166C151406439 @default.
• W2605014166 hasConceptScore W2605014166C151730666 @default.
• W2605014166 hasConceptScore W2605014166C154945302 @default.
• W2605014166 hasConceptScore W2605014166C161584116 @default.
• W2605014166 hasConceptScore W2605014166C180075932 @default.
• W2605014166 hasConceptScore W2605014166C199163554 @default.
• W2605014166 hasConceptScore W2605014166C2776214188 @default.
• W2605014166 hasConceptScore W2605014166C33923547 @default.
• W2605014166 hasConceptScore W2605014166C41008148 @default.
• W2605014166 hasConceptScore W2605014166C86803240 @default.
• W2605014166 hasLocation W26050141661 @default.
• W2605014166 hasOpenAccess W2605014166 @default.
• W2605014166 hasPrimaryLocation W26050141661 @default.
• W2605014166 hasRelatedWork W1718252282 @default.
• W2605014166 hasRelatedWork W1777109131 @default.
• W2605014166 hasRelatedWork W2033654980 @default.
• W2605014166 hasRelatedWork W2066169718 @default.
• W2605014166 hasRelatedWork W2109648321 @default.
• W2605014166 hasRelatedWork W2187066464 @default.
• W2605014166 hasRelatedWork W247518702 @default.
• W2605014166 hasRelatedWork W2556032405 @default.
• W2605014166 hasRelatedWork W2588198190 @default.
• W2605014166 hasRelatedWork W2592150606 @default.
• W2605014166 hasRelatedWork W2883426557 @default.
• W2605014166 hasRelatedWork W2943335884 @default.
• W2605014166 hasRelatedWork W3118535337 @default.
• W2605014166 hasRelatedWork W3121539074 @default.
• W2605014166 hasRelatedWork W3149772074 @default.
• W2605014166 hasRelatedWork W3187860368 @default.
• W2605014166 hasRelatedWork W3201499180 @default.
• W2605014166 hasRelatedWork W344627468 @default.
• W2605014166 hasRelatedWork W2741286879 @default.
• W2605014166 hasRelatedWork W3126945311 @default.
• W2605014166 isParatext "false" @default.
• W2605014166 isRetracted "false" @default.
• W2605014166 magId "2605014166" @default.
• W2605014166 workType "dissertation" @default.