Handbook of Volatility Models and Their Applications

by Bauwens, Luc; Hafner, Christian M.; Laurent, Sebastien

• ISBN13: 9780470872512
• ISBN10: 0470872519
• Edition: 1st
• Format: Hardcover
• Copyright: 2012-04-17
• Publisher: Wiley

Summary

The main purpose of this handbook is to illustrate the mathematically fundamental implementation of various volatility models in the banking and financial industries, both at home and abroad, through use of real-world, time-sensitive applications. Conceived and written by over two-dozen experts in the field, the focus is to cohesively demonstrate how "volatile" certain statistical decision-making techniques can be when solving a range of financial problems. By using examples derived from consulting projects, current research and course instruction, each chapter in the book offers a systematic understanding of the recent advances in volatility modeling related to real-world situations. Every effort is made to present a balanced treatment between theory and practice, as well as to showcase how accuracy and efficiency in implementing various methods can be used as indispensable tools in assessing volatility rates. Unique to the book is in-depth coverage of GARCH-family models, contagion, and model comparisons between different volatility models. To by-pass tedious computation, software illustrations are presented in an assortment of packages, ranging from R, C++, EXCEL-VBA, Minitab, to JMP/SAS.

Author Biography

Luc Bauwens, PhD, is Professor of Economics at the Université catholique de Louvain (Belgium), where he is also President of the Center for Operations Research and Econometrics (CORE). He has written more than 100 published papers on the topics of econometrics, statistics, and microeconomics.

Christian Hafner, PhD, is Professor and President of the Louvain School of Statistics, Biostatistics, and Actuarial Science (LSBA) at the Université catholique de Louvain (Belgium). He has published extensively in the areas of time series econometrics, applied nonparametric statistics, and empirical finance.

Sebastien Laurent, PhD, is Associate Professor of Econometrics in the Department of Quantitative Economics at Maastricht University (The Netherlands). Dr. Laurent's current areas of research interest include financial econometrics and computational econometrics.

Table of Contents

Volatility Models	p. 1
Introduction	p. 1
GARCH	p. 1
Stochastic Volatility	p. 31
Realized Volatility	p. 42
ARCH and SV
Nonlinear ARCH Models	p. 63
Introduction	p. 63
Standard GARCH model	p. 64
Predecessors to Nonlinear GARCH	p. 65
Nonlinear ARCH and GARCH	p. 67
Testing	p. 76
Estimation	p. 81
Forecasting	p. 83
Multiplicative Decomposition	p. 86
Conclusion	p. 88
Mixture and Regime-switching GARCH Models	p. 89
Introduction	p. 89
Regime-switching GARCH models	p. 92
Stationarity and Moment Structure	p. 102
Regime Inference, Likelihood Functions, and Volatility Forecasting	p. 111
Application of Mixture GARCH Models	p. 119
Conclusion	p. 124
Forecasting High Dimensional Covariance Matrices	p. 129
Introduction	p. 129
Notation	p. 130
Rolling-Window Forecasts	p. 131
Dynamic Models	p. 136
High-Frequency Based Forecasts	p. 147
Forecast Evaluation	p. 154
Conclusion	p. 157
Mean, Volatility and Skewness Spillovers in Equity Markets	p. 159
Introduction	p. 159
Data and Summary Statistics	p. 162
Empirical Results	p. 171
Conclusion	p. 177
Relating Stochastic Volatility Estimation Methods	p. 185
Introduction	p. 185
Theory and Methodology	p. 188
Comparison of Methods	p. 201
Estimating Volatility Models in Practice	p. 209
Conclusion	p. 217
Multivariate Stochastic Volatility Models	p. 221
Introduction	p. 221
MSV model	p. 223
Factor MSV model	p. 231
Applications to Stock Indices Returns	p. 237
Conclusion	p. 244
Model Selection and Testing of Volatility Models	p. 249
Introduction	p. 249
Model Selection and Testing	p. 252
Empirical Example	p. 265
Conclusion	p. 277
Other models and methods
Multiplicative Error Models	p. 281
Introduction	p. 281
Theory and Methodology	p. 283
MEM Application	p. 293
MEM Extensions	p. 302
Conclusion	p. 308
Locally Stationary Volatility Modeling	p. 311
Introduction	p. 311
Empirical evidences	p. 314
Locally Stationary Processes	p. 319
Locally Stationary Volatility Models	p. 323
Multivariate Models for Locally Stationary Volatility	p. 331
Conclusion	p. 333
Nonparametric and Semiparametric Volatility Models	p. 335
Introduction	p. 335
Nonparametric and Semiparametric Univariate Models	p. 338
Nonparametric and Semiparametric Multivariate Volatility Models	p. 354
Empirical Analysis	p. 360
Conclusion	p. 363
Copula-based Volatility Models	p. 367
Introduction	p. 367
Definition and Properties of Copulas	p. 369
Estimation	p. 375
Dynamic Copulas	p. 381
Value-at-Risk	p. 387
Multivariate Static copulas	p. 389
Conclusion	p. 395
Realized Volatility
Realized Volatility: Theory and Applications	p. 399
Introduction	p. 399
Modelling Framework	p. 400
Issues in Handling Intra-day Transaction Databases	p. 404
Realized Variance and Covariance	p. 411
Modelling and Forecasting	p. 422
Asset Pricing	p. 426
Estimating Continuous Time Models	p. 431
Likelihood-Based Volatility Estimators	p. 435
Introduction	p. 435
Volatility Estimation	p. 438
Covariance Estimation	p. 447
Empirical Application	p. 450
Conclusion	p. 452
HAR Modeling for Realized Volatility Forecasting	p. 453
Introduction	p. 453
Stylized Facts	p. 455
Heterogeneity and Volatility Persistence	p. 457
HAR Extensions	p. 463
Multivariate Models	p. 469
Applications	p. 473
Conclusion	p. 478
Forecasting volatility with MIDAS	p. 481
Introduction	p. 481
MIDAS Regression Models and Volatility Forecasting	p. 482
Likelihood-based Methods	p. 492
Multivariate Models	p. 505
Conclusion	p. 507
Jumps	p. 509
Introduction	p. 509
Estimators of Integrated Variance and Integrated Covariance	p. 519
Testing for the Presence of Jumps	p. 548
Conclusion	p. 563
Jumps, Periodicity and Microstructure Noise	p. 565
Introduction	p. 565
Model	p. 568
Price Jump Detection Method	p. 570
Simulation Study	p. 576
Comparison on NYSE-Stock Prices	p. 581
Conclusion	p. 583
Volatility Forecasts Evaluation and Comparison	p. 585
Introduction	p. 585
Notation	p. 588
Single Forecast Evaluation	p. 590
Loss Functions and the Latent Variable Problem	p. 593
Pairwise Comparison	p. 597
Multiple Comparison	p. 601
Consistency of the Ordering and Inference on Forecast Performances	p. 607
Conclusion	p. 613
Index	p. 615
Bibliography	p. 629
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