Details

Handbook of Volatility Models and Their Applications


Handbook of Volatility Models and Their Applications


Wiley Handbooks in Financial Engineering and Econometrics, Band 3 1. Aufl.

von: Luc Bauwens, Christian M. Hafner, Sebastien Laurent

150,99 €

Verlag: Wiley
Format: PDF
Veröffentl.: 22.03.2012
ISBN/EAN: 9781118271995
Sprache: englisch
Anzahl Seiten: 576

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Beschreibungen

<b>A complete guide to the theory and practice of volatility models in financial engineering</b> <br /> <br /> <p>Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, <i>Handbook of Volatility Models and Their Applications</i> explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency.</p> <p>Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility:</p> <ul> <li> <p>Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets</p> </li> <li> <p>Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities</p> </li> <li> <p>Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures</p> </li> </ul> <p><i>Handbook of Volatility Models and Their Applications</i> is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.</p>
<b>1. Volatility Models 1</b> <p>1.1 Introduction 1</p> <p>1.2 GARCH 1</p> <p>1.3 Stochastic Volatility 31</p> <p>1.4 Realized Volatility 42</p> <p><b>Part I. ARCH and SV</b></p> <p><b>2. Nonlinear ARCH Models 63</b></p> <p>2.1 Introduction 63</p> <p>2.2 Standard GARCH model 64</p> <p>2.3 Predecessors to Nonlinear GARCH 65</p> <p>2.4 Nonlinear ARCH and GARCH 67</p> <p>2.5 Testing 76</p> <p>2.6 Estimation 81</p> <p>2.7 Forecasting 83</p> <p>2.8 Multiplicative Decomposition 86</p> <p>2.9 Conclusion 88</p> <p><b>3. Mixture and Regime-switching GARCH Models 89</b></p> <p>3.1 Introduction 89</p> <p>3.2 Regime-switching GARCH models 92</p> <p>3.3 Stationarity and Moment Structure 102</p> <p>3.4 Regime Inference, Likelihood Functions, and Volatility Forecasting 111</p> <p>3.5 Application of Mixture GARCH Models 119</p> <p>3.6 Conclusion 124</p> <p><b>4. Forecasting High Dimensional Covariance Matrices 129</b></p> <p>4.1 Introduction 129</p> <p>4.2 Notation 130</p> <p>4.3 Rolling-Window Forecasts 131</p> <p>4.4 Dynamic Models 136</p> <p>4.5 High-Frequency Based Forecasts 147</p> <p>4.6 Forecast Evaluation 154</p> <p>4.7 Conclusion 157</p> <p><b>5. Mean, Volatility and Skewness Spillovers in Equity Markets 159</b></p> <p>5.1 Introduction 159</p> <p>5.2 Data and Summary Statistics 162</p> <p>5.3 Empirical Results 171</p> <p>5.4 Conclusion 177</p> <p><b>6. Relating Stochastic Volatility Estimation Methods 185</b></p> <p>6.1 Introduction 185</p> <p>6.2 Theory and Methodology 188</p> <p>6.3 Comparison of Methods 201</p> <p>6.4 Estimating Volatility Models in Practice 209</p> <p>6.5 Conclusion 217</p> <p><b>7. Multivariate Stochastic Volatility Models 221</b></p> <p>7.1 Introduction 221</p> <p>7.2 MSV model 223</p> <p>7.3 Factor MSV model 231</p> <p>7.4 Applications to Stock Indices Returns 237</p> <p>7.5 Conclusion 244</p> <p><b>8. Model Selection and Testing of Volatility Models 249</b></p> <p>8.1 Introduction 249</p> <p>8.2 Model Selection and Testing 252</p> <p>8.3 Empirical Example 265</p> <p>8.4 Conclusion 277</p> <p><b>Part II. Other models and methods</b></p> <p><b>9. Multiplicative Error Models 281</b></p> <p>9.1 Introduction 281</p> <p>9.2 Theory and Methodology 283</p> <p>9.3 MEM Application 293</p> <p>9.4 MEM Extensions 302</p> <p>9.5 Conclusion 308</p> <p><b>10. Locally Stationary Volatility Modeling 311</b></p> <p>10.1 Introduction 311</p> <p>10.2 Empirical evidences 314</p> <p>10.3 Locally Stationary Processes 319</p> <p>10.4 Locally Stationary Volatility Models 323</p> <p>10.5 Multivariate Models for Locally Stationary Volatility 331</p> <p>10.6 Conclusion 333</p> <p><b>11. Nonparametric and Semiparametric Volatility Models 335</b></p> <p>11.1 Introduction 335</p> <p>11.2 Nonparametric and Semiparametric Univariate Models 338</p> <p>11.3 Nonparametric and Semiparametric Multivariate Volatility Models 354</p> <p>11.4 Empirical Analysis 360</p> <p>11.5 Conclusion 363</p> <p><b>12. Copula-based Volatility Models 367</b></p> <p>12.1 Introduction 367</p> <p>12.2 Definition and Properties of Copulas 369</p> <p>12.3 Estimation 375</p> <p>12.4 Dynamic Copulas 381</p> <p>12.5 Value-at-Risk 387</p> <p>12.6 Multivariate Static copulas 389</p> <p>12.7 Conclusion 395</p> <p><b>Part III. Realized Volatility</b></p> <p><b>13. Realized Volatility: Theory and Applications 399</b></p> <p>13.1 Introduction 399</p> <p>13.2 Modelling Framework 400</p> <p>13.3 Issues in Handling Intra-day Transaction Databases 404</p> <p>13.4 Realized Variance and Covariance 411</p> <p>14.5 Modelling and Forecasting 422</p> <p>13.6 Asset Pricing 426</p> <p>13.7 Estimating Continuous Time Models 431</p> <p><b>14. Likelihood-Based Volatility Estimators 435</b></p> <p>14.1 Introduction 435</p> <p>14.2 Volatility Estimation 438</p> <p>14.3 Covariance Estimation 447</p> <p>14.4 Empirical Application 450</p> <p>14.5 Conclusion 452</p> <p><b>15. HAR Modeling for Realized Volatility Forecasting 453</b></p> <p>15.1 Introduction 453</p> <p>15.2 Stylized Facts 455</p> <p>15.3 Heterogeneity and Volatility Persistence 457</p> <p>15.4 HAR Extensions 463</p> <p>15.5 Multivariate Models 469</p> <p>15.6 Applications 473</p> <p>15.7 Conclusion 478</p> <p><b>16. Forecasting volatility with MIDAS 481</b></p> <p>16.1 Introduction 481</p> <p>16.2 MIDAS Regression Models and Volatility Forecasting 482</p> <p>16.3 Likelihood-based Methods 492</p> <p>16.4 Multivariate Models 505</p> <p>16.5 Conclusion 507</p> <p><b>17. Jumps 509</b></p> <p>17.1 Introduction 509</p> <p>17.2 Estimators of Integrated Variance and Integrated Covariance 519</p> <p>17.3 Testing for the Presence of Jumps 548</p> <p>17.4 Conclusion 563</p> <p><b>18. Jumps, Periodicity and Microstructure Noise 565</b></p> <p>18.1 Introduction 565</p> <p>18.2 Model 568</p> <p>18.3 Price Jump Detection Method 570</p> <p>18.4 Simulation Study 576</p> <p>18.5 Comparison on NYSE-Stock Prices 581</p> <p>18.6 Conclusion 583</p> <p><b>19. Volatility Forecasts Evaluation and Comparison 585</b></p> <p>19.1 Introduction 585</p> <p>19.2 Notation 588</p> <p>19.3 Single Forecast Evaluation 590</p> <p>19.4 Loss Functions and the Latent Variable Problem 593</p> <p>19.5 Pairwise Comparison 597</p> <p>19.6 Multiple Comparison 601</p> <p>19.7 Consistency of the Ordering and Inference on Forecast Performances 607</p> <p>19.8 Conclusion 613</p> <p>Index 615</p> <p>Bibliography 629 </p>
<p>"Conceived and written by over two-dozen experts in the fi eld, the book cohesively demonstrates how 'volatile' certain statistical decision-making techniques can be when solving a range of financial problems." (<i>Zentralblatt MATH</i> 2016)</p>
<p>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.</p> <p>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.</p> <p>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.</p>
A complete guide to the theory and practice of volatility models in financial engineering<br /> <br /> <p>Volatility has become a hot topic in this era of instant communications, spawning a great deal of research in empirical finance and time series econometrics. Providing an overview of the most recent advances, Handbook of Volatility Models and Their Applications explores key concepts and topics essential for modeling the volatility of financial time series, both univariate and multivariate, parametric and non-parametric, high-frequency and low-frequency.</p> <p>Featuring contributions from international experts in the field, the book features numerous examples and applications from real-world projects and cutting-edge research, showing step by step how to use various methods accurately and efficiently when assessing volatility rates. Following a comprehensive introduction to the topic, readers are provided with three distinct sections that unify the statistical and practical aspects of volatility:</p> <ul> <li> <p>Autoregressive Conditional Heteroskedasticity and Stochastic Volatility presents ARCH and stochastic volatility models, with a focus on recent research topics including mean, volatility, and skewness spillovers in equity markets</p> </li> <li> <p>Other Models and Methods presents alternative approaches, such as multiplicative error models, nonparametric and semi-parametric models, and copula-based models of (co)volatilities</p> </li> <li> <p>Realized Volatility explores issues of the measurement of volatility by realized variances and covariances, guiding readers on how to successfully model and forecast these measures</p> </li> </ul> <p>Handbook of Volatility Models and Their Applications is an essential reference for academics and practitioners in finance, business, and econometrics who work with volatility models in their everyday work. The book also serves as a supplement for courses on risk management and volatility at the upper-undergraduate and graduate levels.</p>

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