Forecasting volatility in the financial markets / / edited by John Knight, Stephen Satchell

• Record Nr.: UNINA9910824456303321
• Pubbl/distr/stampa: Amsterdam ; ; Boston, : Butterworth-Heinemann, 2007
• ISBN: 1-280-96289-5; 9786610962891; 0-08-047142-0
• Edizione: [3rd ed.]
• Descrizione fisica: 1 online resource (428 p.)
• Collana: Quantitative finance series
• Altri autori (Persone): KnightJohn L; SatchellS (Stephen)
• Disciplina: 332.66/2042
• Soggetti: Options (Finance) - Mathematical models; Securities - Prices - Mathematical models; Stock price forecasting - Mathematical models
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Front Cover; Forecasting Volatility in the Financial Markets; Copyright Page; Table of Contents; List of contributors; Preface to Third Edition; Introduction; Chapter 1 Volatility modelling and forecasting in finance; 1.1 Introduction; 1.2 Autoregressive moving average models; 1.3 Changes in volatility; 1.3.1 Volatility in financial time series: stylized facts; 1.3.2 The basic set-up; 1.4 ARCH models; 1.4.1 Generalized ARCH; 1.4.2 Integrated ARCH; 1.4.3 Exponential ARCH; 1.4.4 ARCH-M model; 1.4.5 Fractionally integrated ARCH; 1.4.6 Other univariate ARCH formulations; 1.4.7 Multivariate ARCH models1.5 Stochastic variance models; 1.5.1 From continuous time financial models to discrete time SV models; 1.5.2 Persistence and the SV model; 1.5.3 Long memory SV models; 1.5.4 Risk-return trade-off in SV models; 1.5.5 Multivariate SV models; 1.6 Structural changes in the underlying process; 1.6.1 Regime switching models; 1.6.2 Extensions of the regime switching models; 1.7 Threshold models; 1.7.1 Self-exciting threshold models; 1.7.2 Open loop threshold models; 1.7.3 Closed loop threshold models; 1.7.4 Smooth threshold autoregressive models; 1.7.5 Identification in SETAR models1.7.6 A threshold AR(1) model; 1.7.7 A threshold MA model; 1.7.8 Threshold models and asymmetries in volatility; 1.7.9 Testing for non-linearity; 1.7.10 Threshold estimation and prediction of TAR models; 1.8 Volatility forecasting; 1.8.1 Volatility forecasting based on time-series models; 1.8.2 Volatility forecasting based on option ISD (Implied Standard Deviation); 1.9 Conclusion; References and further reading; Notes; Chapter 2 What good is a volatility model?; Abstract; 2.1 Introduction; 2.1.1 Notation; 2.1.2 Types of volatility models; 2.2 Stylized facts about asset price volatility2.2.1 Volatility exhibits persistence; 2.2.2 Volatility is mean reverting; 2.2.3 Innovations may have an asymmetric impact on volatility; 2.2.4 Exogenous variables may influence volatility; 2.2.5 Tail probabilities; 2.2.6 Forecast evaluation; 2.3 An empirical example; 2.3.1 Summary of the data; 2.3.2 A volatility model; 2.3.3 Mean reversion and persistence in volatility; 2.3.4 An asymmetric volatility model; 2.3.5 A model with exogenous volatility regressors; 2.3.6 Aggregation of volatility models; 2.4 Conclusions and challenges for future researchReferences; Notes; Chapter 3 Applications of portfolio variety; Abstract; 3.1 Introduction; 3.2 Some applications of variety; 3.3 Empirical research on variety; 3.4 Variety and risk estimation; 3.5 Variety as an explanation of active management styles; 3.6 Summary; References; Chapter 4 A comparison of the properties of realized variance for the FTSE 100 and FTSE 250 equity indices; 4.1 Introduction; 4.2 Data; 4.3 Theory and empirical methodology; 4.3.1 Realized variance; 4.3.2 Optimal sampling frequency; 4.3.3 Estimation; 4.3.4 Forecasting; 4.4 Initial data analysis
• Sommario/riassunto: This new edition of Forecasting Volatility in the Financial Markets assumes that the reader has a firm grounding in the key principles and methods of understanding volatility measurement and builds on that knowledge to detail cutting-edge modelling and forecasting techniques. It provides a survey of ways to measure risk and define the different models of volatility and return. Editors John Knight and Stephen Satchell have brought together an impressive array of contributors who present research from their area of specialization related to volatility forecasting. Readers with an understanding o