{"product_id":"arch-models-for-financial-applications-isbn-9780470066300","title":"ARCH Models for Financial Applications","description":"Autoregressive 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.  \u003cp\u003eKey Features:\u003c\/p\u003e \u003cul type=\"disc\"\u003e \u003cli\u003ePresents a comprehensive overview of both the theory and the practical applications of ARCH, an increasingly popular financial modelling technique.\u003c\/li\u003e \u003cli\u003eAssumes no prior knowledge of ARCH models; the basics such as model construction are introduced, before proceeding to more complex applications such as value-at-risk, option pricing and model evaluation.\u003c\/li\u003e \u003cli\u003eUses empirical examples to demonstrate how the recent developments in ARCH can be implemented.\u003c\/li\u003e \u003cli\u003eProvides step-by-step instructive examples, using econometric software, such as Econometric Views and the G@RCH module for the Ox software package, used in Estimating and Forecasting ARCH Models.\u003c\/li\u003e \u003cli\u003eAccompanied by a CD-ROM containing links to the software as well as the datasets used in the examples.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eAimed at readers wishing to gain an aptitude in the applications of financial econometric modelling with a focus on practical implementation, via applications to real data and via examples worked with econometrics packages.\u003c\/p\u003e  Prologue.  \u003cp\u003eNotation.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 What is an ARCH process?\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction.\u003c\/p\u003e \u003cp\u003e1.2 The Autoregressive Conditionally Heteroskedastic Process.\u003c\/p\u003e \u003cp\u003e1.3 The Leverage Effect.\u003c\/p\u003e \u003cp\u003e1.4 The Non-trading Period Effect.\u003c\/p\u003e \u003cp\u003e1.5 Non-synchronous Trading Effect.\u003c\/p\u003e \u003cp\u003e1.6 The Relationship between Conditional Variance and Conditional Mean.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 ARCH Volatility Specifications.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 Model Specifications.\u003c\/p\u003e \u003cp\u003e2.2 Methods of Estimation.\u003c\/p\u003e \u003cp\u003e2.3. Estimating the GARCH Model with EViews 6: An Empirical Example..\u003c\/p\u003e \u003cp\u003e2.4. Asymmetric Conditional Volatility Specifications.\u003c\/p\u003e \u003cp\u003e2.5. Simulating ARCH Models Using EViews.\u003c\/p\u003e \u003cp\u003e2.6. Estimating Asymmetric ARCH Models with G@RCH 4.2 OxMetrics – An Empirical Example..\u003c\/p\u003e \u003cp\u003e2.7. Misspecification Tests.\u003c\/p\u003e \u003cp\u003e2.8 Other ARCH Volatility Specifications.\u003c\/p\u003e \u003cp\u003e2.9 Other Methods of Volatility Modeling.\u003c\/p\u003e \u003cp\u003e2.10 Interpretation of the ARCH Process.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Fractionally Integrated ARCH Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Fractionally Integrated ARCH Model Specifications.\u003c\/p\u003e \u003cp\u003e3.2 Estimating Fractionally Integrated ARCH Models Using G@RCH 4.2 OxMetrics – An Empirical Example.\u003c\/p\u003e \u003cp\u003e3.3 A More Detailed Investigation of the Normality of the Standardized Residuals – Goodness-of-fit Tests.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Volatility Forecasting: An Empirical Example Using EViews 6.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 One-step-ahead Volatility Forecasting.\u003c\/p\u003e \u003cp\u003e4.2 Ten-step-ahead Volatility Forecasting.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Other Distributional Assumptions.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Non-Normally Distributed Standardized Innovations.\u003c\/p\u003e \u003cp\u003e5.2 Estimating ARCH Models with Non-Normally Distributed Standardized Innovations Using G@RCH 4.2 OxMetrics – An Empirical Example.\u003c\/p\u003e \u003cp\u003e5.3 Estimating ARCH Models with Non-Normally Distributed Standardized Innovations Using EViews 6 – An Empirical Example.\u003c\/p\u003e \u003cp\u003e5.4 Estimating ARCH Models with Non-Normally Distributed Standardized Innovations Using EViews 6 – The LogL Object.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Volatility Forecasting: An Empirical Example Using G@RCH Ox.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Intra-Day Realized Volatility Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Realized Volatility.\u003c\/p\u003e \u003cp\u003e7.2 Intra-Day Volatility Models.\u003c\/p\u003e \u003cp\u003e7.3 Intra-Day Realized Volatility \u0026amp; ARFIMAX Models in G@RCH 4.2 OxMetrics – An Empirical example.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Applications in Value-at-Risk, Expected Shortfalls, Options Pricing.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e8.1 One-day-ahead Value-at-Risk Forecasting.\u003c\/p\u003e \u003cp\u003e8.2 One-day-ahead Expected Shortfalls Forecasting.\u003c\/p\u003e \u003cp\u003e8.3 FTSE100 Index: One-step-ahead Value-at-Risk and Expected Shortfall Forecasting.\u003c\/p\u003e \u003cp\u003e8.4 Multi-period Value-at-Risk and Expected Shortfalls Forecasting.\u003c\/p\u003e \u003cp\u003e8.5 ARCH Volatility Forecasts in Black and Scholes Option Pricing.\u003c\/p\u003e \u003cp\u003e8.6 ARCH Option Pricing Formulas.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Implied Volatility Indices and ARCH Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e9.1 Implied Volatility.\u003c\/p\u003e \u003cp\u003e9.2 The VIX Index.\u003c\/p\u003e \u003cp\u003e9.3 The Implied Volatility Index as an Explanatory Variable.\u003c\/p\u003e \u003cp\u003e9.4 ARFIMAX Modeling for Implied Volatility Index.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 ARCH Model Evaluation and Selection.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e10.1 Evaluation of ARCH Models.\u003c\/p\u003e \u003cp\u003e10.2 Selection of ARCH Models.\u003c\/p\u003e \u003cp\u003e10.3 Application of Loss Functions as Methods of Model Selection..\u003c\/p\u003e \u003cp\u003e10.4 The SPA Test for VaR and Expected Shortfalls.\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Multivariate ARCH Models.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e11.1 Model Specifications.\u003c\/p\u003e \u003cp\u003e11.2 Maximum Likelihood Estimation.\u003c\/p\u003e \u003cp\u003e11.3 Estimating Multivariate ARCH Models Using EViews 6.\u003c\/p\u003e \u003cp\u003e11.4 Estimating Multivariate ARCH Models Using G@RCH 5.0.\u003c\/p\u003e \u003cp\u003e11.5 Evaluation of Multivariate ARCH Models.\u003c\/p\u003e \u003cp\u003eReferences.\u003c\/p\u003e \u003cp\u003eAuthor Index.\u003c\/p\u003e \u003cp\u003eSubject Index.\u003c\/p\u003e  \"Numerous articles on the Autoregressive Conditional Heteroskedastic (ARCH) process, an increasingly popular financial modeling technique, exist in various international journals. Now Xekalaki and Degiannakis (both statistics, Athens U. of Economics and Business, Greece) provide a thorough treatment of the ARCH theory and its practical applications, in a textbook for postgraduate and final-year undergraduate students which could serve as reference work for academics and financial market professionals.\" (\u003ci\u003eBook News Inc,\u003c\/i\u003e November 2010)\u003cbr\u003e \u003cbr\u003e  \u003cp\u003e\u003cstrong\u003eEvdokia Xekalaki, Department of Statistics, Athens University of Economics and Business\u003c\/strong\u003e\u003cbr\u003eProfessor Xekalaki has been teaching for nearly 30 years, and in that time has held such positions as Director of the graduate program, consultant to EUROSTAT and twice Chair of the Dept of Statistics at AUEB. She has published more than 50 papers in numerous international journals and has presented papers at many international conferences. She is also the Chief Editor of the journal \u003cem\u003eQuality Technology and Quantitative Management\u003c\/em\u003e and on the Editorial Board for the \u003cem\u003eJournal of Applied Stochastic Models in Business and Industry\u003c\/em\u003e. \u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eStavros Degiannakis, Department of Statistics, Athens University of Economics and Business\u003c\/strong\u003e\u003cbr\u003eAdjunct lecturer in applied econometrics, Dr Degiannakis acquired his PhD last year, and has already had eight articles published in seven journals, and eight other papers presented at a variety of international conferences.   Autoregressive 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.  \u003c\/p\u003e\u003cp\u003eKey Features:\u003c\/p\u003e \u003cul type=\"disc\"\u003e \u003cli\u003ePresents a comprehensive overview of both the theory and the practical applications of ARCH, an increasingly popular financial modelling technique.\u003c\/li\u003e \u003cli\u003eAssumes no prior knowledge of ARCH models; the basics such as model construction are introduced, before proceeding to more complex applications such as value-at-risk, option pricing and model evaluation.\u003c\/li\u003e \u003cli\u003eUses empirical examples to demonstrate how the recent developments in ARCH can be implemented.\u003c\/li\u003e \u003cli\u003eProvides step-by-step instructive examples, using econometric software, such as Econometric Views and the G@RCH module for the Ox software package, used in Estimating and Forecasting ARCH Models.\u003c\/li\u003e \u003cli\u003eAccompanied by a CD-ROM containing links to the software as well as the datasets used in the examples.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eAimed at readers wishing to gain an aptitude in the applications of financial econometric modelling with a focus on practical implementation, via applications to real data and via examples worked with econometrics packages.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988757725413,"sku":"NP9780470066300","price":118.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470066300.jpg?v=1761781473","url":"https:\/\/k12savings.com\/products\/arch-models-for-financial-applications-isbn-9780470066300","provider":"K12savings","version":"1.0","type":"link"}