LEADER 06603nam 22008295 450 001 996465567503316 005 20200702235541.0 010 $a3-642-03730-5 024 7 $a10.1007/978-3-642-03730-6 035 $a(CKB)1000000000772861 035 $a(SSID)ssj0000317114 035 $a(PQKBManifestationID)11923518 035 $a(PQKBTitleCode)TC0000317114 035 $a(PQKBWorkID)10286447 035 $a(PQKB)10801827 035 $a(DE-He213)978-3-642-03730-6 035 $a(MiAaPQ)EBC3064470 035 $a(PPN)139955062 035 $a(EXLCZ)991000000000772861 100 $a20100301d2009 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aData Warehousing and Knowledge Discovery$b[electronic resource] $e11th International Conference, DaWaK 2009 Linz, Austria, August 31-September 2, 2009 Proceedings /$fedited by Mukesh K. Mohania, A Min Tjoa 205 $a1st ed. 2009. 210 1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2009. 215 $a1 online resource (XIV, 480 p.) 225 1 $aInformation Systems and Applications, incl. Internet/Web, and HCI ;$v5691 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-642-03729-1 320 $aIncludes bibliographical references and index. 327 $aInvited Talk -- New Challenges in Information Integration -- Data Warehouse Modeling -- What Is Spatio-Temporal Data Warehousing? -- Towards a Modernization Process for Secure Data Warehouses -- Visual Modelling of Data Warehousing Flows with UML Profiles -- Data Streams -- CAMS: OLAPing Multidimensional Data Streams Efficiently -- Data Stream Prediction Using Incremental Hidden Markov Models -- History Guided Low-Cost Change Detection in Streams -- Physical Design -- HOBI: Hierarchically Organized Bitmap Index for Indexing Dimensional Data -- A Joint Design Approach of Partitioning and Allocation in Parallel Data Warehouses -- Fast Loads and Fast Queries -- Pattern Mining -- TidFP: Mining Frequent Patterns in Different Databases with Transaction ID -- Non-Derivable Item Set and Non-Derivable Literal Set Representations of Patterns Admitting Negation -- Which Is Better for Frequent Pattern Mining: Approximate Counting or Sampling? -- A Fast Feature-Based Method to Detect Unusual Patterns in Multidimensional Datasets -- Data Cubes -- Efficient Online Aggregates in Dense-Region-Based Data Cube Representations -- BitCube: A Bottom-Up Cubing Engineering -- Exact and Approximate Sizes of Convex Datacubes -- Data Mining Applications -- Finding Clothing That Fit through Cluster Analysis and Objective Interestingness Measures -- Customer Churn Prediction for Broadband Internet Services -- Mining High-Correlation Association Rules for Inferring Gene Regulation Networks -- Analytics -- Extend UDF Technology for Integrated Analytics -- High Performance Analytics with the R3-Cache -- Open Source BI Platforms: A Functional and Architectural Comparison -- Ontology-Based Exchange and Immediate Application of Business Calculation Definitions for Online Analytical Processing -- Data Mining -- Skyline View: Efficient Distributed Subspace Skyline Computation -- HDB-Subdue: A Scalable Approach to Graph Mining -- Mining Violations to Relax Relational Database Constraints -- Arguing from Experience to Classifying Noisy Data -- Clustering -- Dynamic Clustering-Based Estimation of Missing Values in Mixed Type Data -- The PDG-Mixture Model for Clustering -- Clustering for Video Retrieval -- Spatio-Temporal Mining -- Trends Analysis of Topics Based on Temporal Segmentation -- Finding N-Most Prevalent Colocated Event Sets -- Rule Mining -- Rule Learning with Probabilistic Smoothing -- Missing Values: Proposition of a Typology and Characterization with an Association Rule-Based Model -- Olap Recommendation -- Recommending Multidimensional Queries -- Preference-Based Recommendations for OLAP Analysis. 330 $aThis book constitutes the refereed proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery, DaWak 2009 held in Linz, Austria in August/September 2009. The 36 revised full papers presented were carefully reviewed and selected from 124 submissions. The papers are organized in topical sections on data warehouse modeling, data streams, physical design, pattern mining, data cubes, data mining applications, analytics, data mining, clustering, spatio-temporal mining, rule mining, and OLAP recommendation. 410 0$aInformation Systems and Applications, incl. Internet/Web, and HCI ;$v5691 606 $aDatabase management 606 $aData mining 606 $aInformation storage and retrieval 606 $aApplication software 606 $aComputers 606 $aPattern recognition 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 606 $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032 606 $aInformation Systems Applications (incl. Internet)$3https://scigraph.springernature.com/ontologies/product-market-codes/I18040 606 $aInformation Systems and Communication Service$3https://scigraph.springernature.com/ontologies/product-market-codes/I18008 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 608 $aKongress.$2swd 608 $aLinz (2009)$2swd 615 0$aDatabase management. 615 0$aData mining. 615 0$aInformation storage and retrieval. 615 0$aApplication software. 615 0$aComputers. 615 0$aPattern recognition. 615 14$aDatabase Management. 615 24$aData Mining and Knowledge Discovery. 615 24$aInformation Storage and Retrieval. 615 24$aInformation Systems Applications (incl. Internet). 615 24$aInformation Systems and Communication Service. 615 24$aPattern Recognition. 676 $a006.31222gerDNB 686 $aDAT 620f$2stub 686 $aSS 4800$2rvk 702 $aMohania$b Mukesh K$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aTjoa$b A Min$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aDaWaK 2009 906 $aBOOK 912 $a996465567503316 996 $aData Warehousing and Knowledge Discovery$9772048 997 $aUNISA LEADER 05212nam 2200661Ia 450 001 9910140611403321 005 20230725023225.0 010 $a1-282-54774-7 010 $a9786612547744 010 $a0-470-68801-7 010 $a0-470-68802-5 035 $a(CKB)2670000000014746 035 $a(EBL)514415 035 $a(OCoLC)609862847 035 $a(SSID)ssj0000356704 035 $a(PQKBManifestationID)11275000 035 $a(PQKBTitleCode)TC0000356704 035 $a(PQKBWorkID)10350294 035 $a(PQKB)11490533 035 $a(MiAaPQ)EBC514415 035 $a(Au-PeEL)EBL514415 035 $a(CaPaEBR)ebr10377794 035 $a(CaONFJC)MIL254774 035 $a(EXLCZ)992670000000014746 100 $a20091217d2010 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aARCH models for financial applications$b[electronic resource] /$fEvdokia Xekalaki, Stavros Degiannakis 210 $aChichester ;$aHoboken $cJohn Wiley & Sons$d2010 215 $a1 online resource (560 p.) 300 $aDescription based upon print version of record. 311 $a0-470-06630-X 320 $aIncludes bibliographical references and index. 327 $aARCH Models for Financial Applications; Contents; Preface; Notation; 1 What is an ARCH process?; 1.1 Introduction; 1.2 The autoregressive conditionally heteroscedastic process; 1.3 The leverage effect; 1.4 The non-trading period effect; 1.5 The non-synchronous trading effect; 1.6 The relationship between conditional variance and conditional mean; 1.6.1 The ARCH in mean model; 1.6.2 Volatility and serial correlation; 2 ARCH volatility specifications; 2.1 Model specifications; 2.2 Methods of estimation; 2.2.1 Maximum likelihood estimation; 2.2.2 Numerical estimation algorithms 327 $a2.2.3 Quasi-maximum likelihood estimation2.2.4 Other estimation methods; 2.3 Estimating the GARCH model with EViews 6: an empirical example; 2.4 Asymmetric conditional volatility specifications; 2.5 Simulating ARCH models using EViews; 2.6 Estimating asymmetric ARCH models with G@RCH 4.2 OxMetrics: an empirical example; 2.7 Misspecification tests; 2.7.1 The Box-Pierce and Ljung-Box Q statistics; 2.7.2 Tse's residual based diagnostic test for conditional heteroscedasticity; 2.7.3 Engle's Lagrange multiplier test; 2.7.4 Engle and Ng's sign bias tests 327 $a2.7.5 The Breusch-Pagan, Godfrey, Glejser, Harvey and White tests2.7.6 The Wald, likelihood ratio and Lagrange multiplier tests; 2.8 Other ARCH volatility specifications; 2.8.1 Regime-switching ARCH models; 2.8.2 Extended ARCH models; 2.9 Other methods of volatility modelling; 2.10 Interpretation of the ARCH process; Appendix; 3 Fractionally integrated ARCH models; 3.1 Fractionally integrated ARCH model specifications; 3.2 Estimating fractionally integrated ARCH models using G@RCH 4.2 OxMetrics: an empirical example 327 $a3.3 A more detailed investigation of the normality of the standardized residuals: goodness-of-fit tests3.3.1 EDF tests; 3.3.2 Chi-square tests; 3.3.3 QQ plots; 3.3.4 Goodness-of-fit tests using EViews and G@RCH; Appendix; 4 Volatility forecasting: an empirical example using EViews 6; 4.1 One-step-ahead volatility forecasting; 4.2 Ten-step-ahead volatility forecasting; Appendix; 5 Other distributional assumptions; 5.1 Non-normally distributed standardized innovations 327 $a5.2 Estimating ARCH models with non-normally distributed standardized innovations using G@RCH 4.2 OxMetrics: an empirical example5.3 Estimating ARCH models with non-normally distributed standardized innovations using EViews 6: an empirical example; 5.4 Estimating ARCH models with non-normally distributed standardized innovations using EViews 6: the logl object; Appendix; 6 Volatility forecasting: an empirical example using G@RCH Ox; Appendix; 7 Intraday realized volatility models; 7.1 Realized volatility; 7.2 Intraday volatility models 327 $a7.3 Intraday realized volatility andARFIMAXmodels in G@RCH 4.2 OxMetrics: an empirical example 330 $aAutoregressive Conditional Heteroskedastic (ARCH) processes are used in finance to model asset price volatility over time. This book introduces both the theory and applications of ARCH models and provides the basic theoretical and empirical background, before proceeding to more advanced issues and applications. The Authors provide coverage of the recent developments in ARCH modelling which can be implemented using econometric software, model construction, fitting and forecasting and model evaluation and selection. Key Features:Presents a comprehensive overview of both t 606 $aFinance$xMathematical models 606 $aAutoregression (Statistics) 615 0$aFinance$xMathematical models. 615 0$aAutoregression (Statistics) 676 $a332.015195 676 $a332.01519536 700 $aXekalaki$b Evdokia$0614604 701 $aDegiannakis$b Stavros$0614605 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910140611403321 996 $aARCH models for financial applications$91131618 997 $aUNINA