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A complete guide to the theory and practice of volatility modelsin financial engineering Volatility has become a hot topic in this era of instantcommunications, spawning a great deal of research in empiricalfinance and time series econometrics. Providing an overview of themost recent advances, Handbook of Volatility Models and TheirApplications explores key concepts and topics essential formodeling the volatility of financial time series, both univariateand multivariate, parametric and non-parametric, high-frequency andlow-frequency. Featuring contributions from international experts in the…mehr

Produktbeschreibung
A complete guide to the theory and practice of volatility modelsin financial engineering Volatility has become a hot topic in this era of instantcommunications, spawning a great deal of research in empiricalfinance and time series econometrics. Providing an overview of themost recent advances, Handbook of Volatility Models and TheirApplications explores key concepts and topics essential formodeling the volatility of financial time series, both univariateand multivariate, parametric and non-parametric, high-frequency andlow-frequency. Featuring contributions from international experts in the field,the book features numerous examples and applications fromreal-world projects and cutting-edge research, showing step by stephow to use various methods accurately and efficiently whenassessing volatility rates. Following a comprehensive introductionto the topic, readers are provided with three distinct sectionsthat unify the statistical and practical aspects of volatility: * Autoregressive Conditional Heteroskedasticity and StochasticVolatility presents ARCH and stochastic volatility models, with afocus on recent research topics including mean, volatility, andskewness spillovers in equity markets * Other Models and Methods presents alternative approaches, suchas multiplicative error models, nonparametric and semi-parametricmodels, and copula-based models of (co)volatilities * Realized Volatility explores issues of the measurement ofvolatility by realized variances and covariances, guiding readerson how to successfully model and forecast these measures Handbook of Volatility Models and Their Applications isan essential reference for academics and practitioners in finance,business, and econometrics who work with volatility models in theireveryday work. The book also serves as a supplement for courses onrisk management and volatility at the upper-undergraduate andgraduate levels.

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  • Produktdetails
  • Verlag: John Wiley & Sons
  • Seitenzahl: 568
  • Erscheinungstermin: 22.03.2012
  • Englisch
  • ISBN-13: 9781118271995
  • Artikelnr.: 38448223
Autorenporträt
Luc Bauwens, PhD, is Professor of Economics at theUniversité catholique de Louvain (Belgium), where he is alsoPresident of the Center for Operations Research and Econometrics(CORE). He has written more than 100 published papers on the topicsof econometrics, statistics, and microeconomics. Christian Hafner, PhD, is Professor and President of the LouvainSchool of Statistics, Biostatistics, and Actuarial Science (LSBA)at the Université catholique de Louvain (Belgium). He haspublished extensively in the areas of time series econometrics,applied nonparametric statistics, and empirical finance. Sebastien Laurent, PhD, is Associate Professor of Econometricsin the Department of Quantitative Economics at MaastrichtUniversity (The Netherlands). Dr. Laurent's current areas ofresearch interest include financial econometrics and computationaleconometrics.
Inhaltsangabe
1. Volatility Models 1 1.1 Introduction 1 1.2 GARCH 1 1.3 Stochastic Volatility 31 1.4 Realized Volatility 42 Part I. ARCH and SV 2. Nonlinear ARCH Models 63 2.1 Introduction 63 2.2 Standard GARCH model 64 2.3 Predecessors to Nonlinear GARCH 65 2.4 Nonlinear ARCH and GARCH 67 2.5 Testing 76 2.6 Estimation 81 2.7 Forecasting 83 2.8 Multiplicative Decomposition 86 2.9 Conclusion 88 3. Mixture and Regime
switching GARCH Models 89 3.1 Introduction 89 3.2 Regime
switching GARCH models 92 3.3 Stationarity and Moment Structure 102 3.4 Regime Inference, Likelihood Functions, and Volatility Forecasting 111 3.5 Application of Mixture GARCH Models 119 3.6 Conclusion 124 4. Forecasting High Dimensional Covariance Matrices 129 4.1 Introduction 129 4.2 Notation 130 4.3 Rolling
Window Forecasts 131 4.4 Dynamic Models 136 4.5 High
Frequency Based Forecasts 147 4.6 Forecast Evaluation 154 4.7 Conclusion 157 5. Mean, Volatility and Skewness Spillovers in Equity Markets 159 5.1 Introduction 159 5.2 Data and Summary Statistics 162 5.3 Empirical Results 171 5.4 Conclusion 177 6. Relating Stochastic Volatility Estimation Methods 185 6.1 Introduction 185 6.2 Theory and Methodology 188 6.3 Comparison of Methods 201 6.4 Estimating Volatility Models in Practice 209 6.5 Conclusion 217 7. Multivariate Stochastic Volatility Models 221 7.1 Introduction 221 7.2 MSV model 223 7.3 Factor MSV model 231 7.4 Applications to Stock Indices Returns 237 7.5 Conclusion 244 8. Model Selection and Testing of Volatility Models 249 8.1 Introduction 249 8.2 Model Selection and Testing 252 8.3 Empirical Example 265 8.4 Conclusion 277 Part II. Other models and methods 9. Multiplicative Error Models 281 9.1 Introduction 281 9.2 Theory and Methodology 283 9.3 MEM Application 293 9.4 MEM Extensions 302 9.5 Conclusion 308 10. Locally Stationary Volatility Modeling 311 10.1 Introduction 311 10.2 Empirical evidences 314 10.3 Locally Stationary Processes 319 10.4 Locally Stationary Volatility Models 323 10.5 Multivariate Models for Locally Stationary Volatility 331 10.6 Conclusion 333 11. Nonparametric and Semiparametric Volatility Models 335 11.1 Introduction 335 11.2 Nonparametric and Semiparametric Univariate Models 338 11.3 Nonparametric and Semiparametric Multivariate Volatility Models 354 11.4 Empirical Analysis 360 11.5 Conclusion 363 12. Copula
based Volatility Models 367 12.1 Introduction 367 12.2 Definition and Properties of Copulas 369 12.3 Estimation 375 12.4 Dynamic Copulas 381 12.5 Value
at
Risk 387 12.6 Multivariate Static copulas 389 12.7 Conclusion 395 Part III. Realized Volatility 13. Realized Volatility: Theory and Applications 399 13.1 Introduction 399 13.2 Modelling Framework 400 13.3 Issues in Handling Intra
day Transaction Databases 404 13.4 Realized Variance and Covariance 411 14.5 Modelling and Forecasting 422 13.6 Asset Pricing 426 13.7 Estimating Continuous Time Models 431 14. Likelihood
Based Volatility Estimators 435 14.1 Introduction 435 14.2 Volatility Estimation 438 14.3 Covariance Estimation 447 14.4 Empirical Application 450 14.5 Conclusion 452 15. HAR Modeling for Realized Volatility Forecasting 453 15.1 Introduction 453 15.2 Stylized Facts 455 15.3 Heterogeneity and Volatility Persistence 457 15.4 HAR Extensions 463 15.5 Multivariate Models 469 15.6 Applications 473 15.7 Conclusion 478 16. Forecasting volatility with MIDAS 481 16.1 Introduction 481 16.2 MIDAS Regression Models and Volatility Forecasting 482 16.3 Likelihood
based Methods 492 16.4 Multivariate Models 505 16.5 Conclusion 507 17. Jumps 509 17.1 Introduction 509 17.2 Estimators of Integrated Variance and Integrated Covariance 519 17.3 Testing for the Presence of Jumps 548 17.4 Conclusion 563 18. Jumps, Periodicity and Microstructure Noise 565 18.1 Introduction 565 18.2 Model 568 18.3 Price Jump Detection Method 570 18.4 Simulation Study 576 18.5 Comparison on NYSE
Stock Prices 581 18.6 Conclusion 583 19. Volatility Forecasts Evaluation and Comparison 585 19.1 Introduction 585 19.2 Notation 588 19.3 Single Forecast Evaluation 590 19.4 Loss Functions and the Latent Variable Problem 593 19.5 Pairwise Comparison 597 19.6 Multiple Comparison 601 19.7 Consistency of the Ordering and Inference on Forecast Performances 607 19.8 Conclusion 613 Index 615 Bibliography 629