05442nam 2200661Ia 450 991077746250332120230120005402.01-282-28475-497866122847550-08-049904-X(CKB)1000000000439293(EBL)452805(OCoLC)213298473(SSID)ssj0000300325(PQKBManifestationID)11205939(PQKBTitleCode)TC0000300325(PQKBWorkID)10251913(PQKB)10766963(Au-PeEL)EBL452805(CaPaEBR)ebr10329603(CaONFJC)MIL228475(OCoLC)935268339(CaSebORM)9780122796715(MiAaPQ)EBC452805(EXLCZ)99100000000043929320010621d2001 uy 0engurunu|||||txtccrAn introduction to high-frequency finance[electronic resource] /Michel M. Dacorogna ... [et al.]1st editionSan Diego Academic Pressc20011 online resource (411 p.)Description based upon print version of record.0-12-279671-3 Includes bibliographical references (p. 356-375) and index.Front Cover; AN INTRODUCTION TO HIGH-FREQUENCY FINANCE; Copyright Page; CONTENTS; LIST OF FIGURES; LIST OF TABLES; PREFACE; ACKNOWLEDGMENTS; CHAPTER 1. INTRODUCTION; 1.1 Markets: The Source of High-Frequency Data; 1.2 Methodology of High-Frequency Research; 1.3 Data Frequency and Market Information; 1.4 New Levels of Significance; 1.5 Interrelating Different Time Scales; CHAPTER 2. MARKETS AND DATA; 2.1 General Remarks on Markets and Data Types; 2.2 Foreign Exchange Markets; 2.3 Over-The-Counter Interest Rate Markets; 2.4 Interest Rate Futures; 2.5 Bond Futures Markets; 2.6 Commodity Futures2.7 Equity Markets CHAPTER 3. TIME SERIES of INTEREST; 3.1 Time Series and Operators; 3.2 Variables in Homogeneous Time Series; 3.3 Convolution Operators; 3.4 Microscopic Operators; CHAPTER 4. ADAPTIVE DATA CLEANING; 4.1 Introduction: Using a Filter to Clean the Data; 4.2 Data and Data Errors; 4.3 General Overview of the Filter; 4.4 Basic Filtering Elements and Operations; 4.5 The Scalar Filtering Window; 4.6 The Full-Quote Filtering Window; 4.7 Univariate Filtering; 4.8 Special Filter Elements; 4.9 Behavior and Effects of the Data Filter; CHAPTER 5. BASIC STYLIZED FACTS; 5.1 Introduction5.2 Price Formation Process 5.3 Institutional Structure and Exogeneous Impacts; 5.4 Distributional Properties of Returns; 5.5 Scaling Laws; 5.6 Autocorrelation and Seasonality; CHAPTER 6. MODELING SEASONAL VOLATILITY; 6.1 Introduction; 6.2 A Model of Market Activity; 6.3 A New Business Time Scale (ò-Scale); 6.4 Filtering Intraday Seasonalities With Wavelets; CHAPTER 7. REALIZED VOLATILITY DYNAMICS; 7.1 Introduction; 7.2 The Bias of Realized Volatility and Its Correction; 7.3 Conditional Heteroskedasticity; 7.4 The Heterogeneous Market Hypothesis; CHAPTER 8. VOLATILITY PROCESSES8.1 Introduction 8.2 Intraday Volatility and GARCH Models; 8.3 Modeling Heterogeneous Volatilities; 8.4 Forecasting Short-Term Volatility; CHAPTER 9. FORECASTING RISK AND RETURN; 9.1 Introduction to Forecasting; 9.2 Forecasting Volatility for Value-at-Risk; 9.3 Forecasting Returns over Multiple Time Horizons; 9.4 Measuring Forecast Quality; CHAPTER 10. CORRELATION AND MULTIVARIATE RISK; 10.1 Introduction; 10.2 Estimating the Dependence of Financial Time Series; 10.3 Covolatility Weighting; 10.4 Stability of Return Correlations; 10.5 Correlation Behavior at High Data Frequencies10.6 Conclusions CHAPTER 11. TRADING MODELS; 11.1 Introduction; 11.2 Real-Time Trading Strategies; 11.3 Risk Sensitive Performance Measures; 11.4 Trading Model Algorithms; 11.5 Optimization and Testing Procedures; 11.6 Statistical Study of a Trading Model; 11.7 Trading Model Portfolios; 11.8 Currency Risk Hedging; CHAPTER 12. TOWARD A THEORY of HETEROGENEOUS MARKETS; 12.1 Definition of Efficient Markets; 12.2 Dynamic Markets and Relativistic Effects; 12.3 Impact of the New Technology; 12.4 Zero-Sum Game or Perpetuum Mobile?; 12.5 Discussion of the Conventional Definition12.6 An Improved Definition of ""Efficient Markets""Liquid markets generate hundreds or thousands of ticks (the minimum change in price a security can have, either up or down) every business day. Data vendors such as Reuters transmit more than 275,000 prices per day for foreign exchange spot rates alone. Thus, high-frequency data can be a fundamental object of study, as traders make decisions by observing high-frequency or tick-by-tick data. Yet most studies published in financial literature deal with low frequency, regularly spaced data. For a variety of reasons, high-frequency data are becoming a way for understanding market microstructure.FinanceEconometric modelsTime-series analysisFinanceEconometric models.Time-series analysis.332.0151955Dacorogna Michel M464715MiAaPQMiAaPQMiAaPQBOOK9910777462503321Introduction to high-frequency finance208611UNINA05440nam 22007452 450 991082247360332120180709160709.01-78330-024-8(CKB)2550000001279484(EBL)1680069(SSID)ssj0001215336(PQKBManifestationID)11682442(PQKBTitleCode)TC0001215336(PQKBWorkID)11176276(PQKB)10381181(MiAaPQ)EBC1680069(UkCbUP)CR9781783300242(EXLCZ)99255000000127948420180524d2014|||| uy| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierDelivering research data management services fundamentals of good practice /edited by Graham Pryor, Sarah Jones and Angus Whyte[electronic resource]London :Facet,2014.1 online resource (xiv, 242 pages) digital, PDF file(s)Title from publisher's bibliographic system (viewed on 05 Jul 2018).1-85604-933-7 1-306-69439-6 Includes bibliographical references at the end of each chapters and index.A patchwork of change -- Options and approaches to RDM service provision -- Who's doing data? A spectrum of roles, responsibilities and competences -- A pathway to sustainable research data services : from scoping to sustainability -- The range and components of RDM infrastructure and services -- Case study 1 : Johns Hopkins University Data Management Services -- Case study 2 : University of Southhampton -- a partnership approach to research data management -- Case study 3 : Monash University, a strategic approach -- Case study 4 : a national solution -- the UK Data Service -- Case study 5 : development of institutional RDM services by projects in the Jisc Managing Research Data programmes.The research landscape is changing, with key global research funders now requiring institutions to demonstrate how they will preserve and share research data. However, the practice of structured research data management is very new, and the construction of services remains experimental and in need of models and standards of approach. This groundbreaking guide will lead researchers, institutions and policy makers through the processes needed to set up and run effective institutional research data management services. This 'how to' guide provides a step-by-step explanation of the components for an institutional service. Case studies from the newly emerging service infrastructures in the UK, USA and Australia draw out the lessons learnt. Different approaches are highlighted and compared; for example, a researcher-focused strategy from Australia is contrasted with a national, top-down approach, and a national research data management service is discussed as an alternative to institutional services. The key topics covered are: research data provision; options and approaches to research data management (RDM) service provision; a spectrum of roles, responsibilities and competences; a pathway to sustainable research data services; the range and components of RDM infrastructure and services; case studies of Johns Hopkins University, University of Southampton, Monash University, the UK Data Service and Jisc Managing Research Data programmes. This book will be an invaluable guide to those entering a new and untried enterprise. It will be particularly relevant to heads of libraries, information technology managers, research support office staff and research directors planning for these types of services. It will also be of interest to researchers, funders and policy makers as a reference tool for understanding how shifts in policy will have a range of ramifications within institutions. Library and information science students will find it an informative window on an emerging area of practice.Information storage and retrieval systemsResearchInformation servicesData librariesElectronic information resourcesManagementCase studiesDigital librariesManagementCase studiesDatabase managementCase studiesLibraries and scholarsCommunication in learning and scholarshipTechnological innovationsInstitutional repositoriesDigital preservationManagementCase studiesInformation servicesManagementCase studiesInformation storage and retrieval systems.ResearchInformation services.Data libraries.Electronic information resourcesManagementDigital librariesManagementDatabase managementLibraries and scholars.Communication in learning and scholarshipTechnological innovations.Institutional repositories.Digital preservationManagementInformation servicesManagement025.04Pryor GrahamJones Sarah1981-Whyte Angus1959-UkCbUPUkCbUPBOOK9910822473603321Delivering research data management services4038465UNINA