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Volatility

Edited by Torben G. Andersen, Nathan S. and Mary P. Sharp Distinguished Professor of Finance, Kellogg School of Management, Northwestern University and Tim Bollerslev, Juanita and Clifton Kreps Distinguished Professor of Economics and Professor of Finance, Duke University, US
Volatility ranks among the most active and successful areas of research in econometrics and empirical asset pricing finance over the past three decades. This two-volume collection of papers comprises some of the most influential published works from this burgeoning literature, both classic and contemporary. Topics covered include GARCH, stochastic and multivariate volatility models as well as forecasting, evaluation and high-frequency data. Together with an original introduction by the editors, this definitive compilation presents the most important milestones and contributions that helped pave the way to today’s understanding of volatility.
Two volume set
Extent: 1,760 pp
Hardback Price: $895.00 Web: $805.50
Publication Date: 2018
ISBN: 978 1 78811 061 7
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Volatility ranks among the most active and successful areas of research in econometrics and empirical asset pricing finance over the past three decades. This two-volume collection of papers comprises some of the most influential published works from this burgeoning literature, both classic and contemporary. Topics covered include GARCH, stochastic and multivariate volatility models as well as forecasting, evaluation and high-frequency data. Together with an original introduction by the editors, this definitive compilation presents the most important milestones and contributions that helped pave the way to today’s understanding of volatility.
‘This anthology of classical and recent articles will be very useful to all researchers and students interested in the various econometric aspects of volatility measurement, modeling, forecasting, and their applications in finance. The introductory chapter by Andersen and Bollerslev – well-known top experts in the field – offers the needed guidance to fully benefit from the collected papers.’
– Luc Bauwens, Université catholique de Louvain, Belgium

‘The measurement and prediction of volatility is one of the most active areas in empirical finance and financial econometrics. This book provides a well-balanced and state-of-the art compilation of seminal papers in the literature over the last three decades. By covering the milestones in the methodological development and application of GARCH models, stochastic volatility models and high-frequency-based approaches, the book is a comprehensive compendium for any quantitative practitioner and researcher.’
– Nikolaus Hautsch, University of Vienna, Austria
65 articles, dating from 1973 to 2011
Contributors include: F. Black, F.X. Diebold, R.F. Engle, P.R. Hansen, J. Hull, D. B. Nelson, E. Renault, G.W. Schwert, N. Shephard, G. Tauchen
Contents:

Acknowledgements

Introduction Torben G. Andersen and Tim Bollerslev

PART I PROLOGUE
1. Fischer Black (1976), ‘Studies of Stock Price Volatility Changes’, Proceedings of the 1976 Meetings of the American Statistical Association, Business and Economic Statistics Section, 177–81

PART II GARCH MODELS
2. Robert F. Engle (1982), ’Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation’, Econometrica, 50 (4), July, 987–1007

3. Tim Bollerslev (1986), ‘Generalized Autoregressive Conditional Heteroskedasticity’, Journal of Econometrics, 31 (3), April, 307–27

4. Robert F. Engle, David M. Lilien and Russell P. Robins (1987), ‘Estimating Time Varying Risk Premia in the Term Structure: The ARCH-M Model’, Econometrica, 55 (2), March, 391–407

5. Kenneth R. French, G. William Schwert and Robert F. Stambaugh (1987), ‘Expected Stock Returns and Volatility’, Journal of Financial Economics, 19 (1), September, 3–29

6. G. William Schwert (1989), ‘Why Does Stock Market Volatility Change Over Time?’, Journal of Finance, XLIV (5), December, 1115–53

7. Tim Bollerslev (1987), ‘A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return’, Review of Economics and Statistics, 69 (3), August, 542–7

8. Tim Bollerslev and Jeffrey M. Wooldridge (1992), ‘Quasi-Maximum Likelihood Estimation and Inference in Dynamic Models with Time-Varying Covariances’, Econometric Reviews, 11 (2), 143–72

9. Alexander J. McNeil and Rüdiger Frey (2000), ‘Estimation of Tail-Related Risk Measures for Heteroscedastic Financial Time Series: An Extreme Value Approach’, Journal of Empirical Finance: Special Issue on Risk Management, 7 (3–4), November, 271–300

10. Lawrence R. Glosten, Ravi Jagannathan and David E. Runkle (1993), ‘On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks’, Journal of Finance, XLVIII (5), December, 1779–801

11. Jean-Michel Zakoian (1994), ‘Threshold Heteroskedastic Models’, Journal of Economic Dynamics and Control, 18 (5), September, 931–55

12. Daniel B. Nelson (1991), ‘Conditional Heteroskedasticity in Asset Returns: A New Approach’, Econometrica, 59 (2), March, 347–70

13. Zhuanxin Ding, Clive W. J. Granger and Robert F. Engle (1993), ‘A Long Memory Property of Stock Market Returns and a New Model’, Journal of Empirical Finance, 1 (1), June, 83–106

14. Richard T. Baillie, Tim Bollerslev and Hans Ole Mikkelsen (1996), ‘Fractionally Integrated Generalized Autoregressive Conditional Heteroskedasticity’, Journal of Econometrics, 74 (1), September, 3–30

15. Peter R. Hansen and Asger Lunde (2005), ‘A Forecast Comparison of Volatility Models: Does Anything Beat a GARCH(1,1)?’, Journal of Applied Econometrics, 20 (7), December, 873–89

PART III STOCHASTIC VOLATILITY MODELS
16. Peter K. Clark (1973), ‘A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices’, Econometrica, 41 (1), January, 135–55

17. George E. Tauchen and Mark Pitts (1983), ‘The Price Variability-Volume Relationship on Speculative Markets’, Econometrica, 51 (2), March, 485–505

18. Torben G. Andersen (1996), ‘Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility’, Journal of Finance, LI (1), March, 169–204

19. Stephen J. Taylor (1982), ‘Financial Returns Modelled by the Product of Two Stochastic Processes – A Study of Daily Sugar Prices, 1961–79’, in Oliver D. Anderson (ed.), Time Series Analysis: Theory and Practice 1: Proceedings of the International Conference Held at Valencia, Spain, June 1981, Amsterdam, the Netherlands: North-Holland Publishing Company, 203–26

20. Torben G. Andersen (1994), ‘Stochastic Autoregressive Volatility: A Framework for Volatility Modeling’, Mathematical Finance, 4 (2), April, 75–102
21. C. Gourieroux, A. Monfort and E. Renault (1993), ‘Indirect Inference’, Journal of Applied Econometrics, Supplement: Special Issue on Econometric Inference Using Simulation Techniques, 8 (S1), December, S85–S118

22. A. Ronald Gallant and George Tauchen (1996), ‘Which Moments to Match?’, Econometric Theory, 12 (4), October, 657–81

23. Torben G. Andersen and Jesper Lund (1997), ‘Estimating Continuous-Time Stochastic Volatility Models of the Short-Term Interest Rate’, Journal of Econometrics, 77 (2), April, 343–77

24. Eric Jacquier, Nicholas G. Polson and Peter E. Rossi (1994), ‘Bayesian Analysis of Stochastic Volatility Models’, Journal of Business and Economic Statistics, 12 (4), October, 371–89

25. Nour Meddahi and Eric Renault (2004), ‘Temporal Aggregation of Volatility Models’, Journal of Econometrics: Dynamic Factor Models, 119 (2), April, 355–79

26. Fabienne Comte and Eric Renault (1998), ‘Long Memory in Continuous-Time Stochastic Volatility Models’, Mathematical Finance, 8 (4), October, 291–323

27. Laurent Calvet and Adlai Fisher (2002), ‘Multifractality in Asset Returns: Theory and Evidence’, Review of Economics and Statistics, LXXXIV (3), August, 381–406

PART IV MULTIVARIATE VOLATILITY MODELS
28. Tim Bollerslev, Robert F. Engle and Jeffrey M. Wooldridge (1988), ‘A Capital Asset Pricing Model with Time-varying Covariances’, Journal of Political Economy, 96 (1), February, 116–31

29. Robert F. Engle and Kenneth F. Kroner (1995), ‘Multivariate Simultaneous Generalized ARCH’, Econometric Theory, 11 (1), February, 122–50

30. Francis X. Diebold and Marc Nerlove (1989), ‘The Dynamics of Exchange Rate Volatility: A Multivariate Latent Factor ARCH Model’, Journal of Applied Econometrics, 4 (1), January–March, 1–21

31. Tim Bollerslev (1990), ‘Modelling the Coherence in Short-Run Nominal Exchange Rates: A Multivariate Generalized ARCH Model’, Review of Economics and Statistics, 72 (3), August, 498–505

32. Andrew Harvey, Esther Ruiz and Neil Shephard (1994), ‘Multivariate Stochastic Variance Models’, Review of Economic Studies, 61 (2), April, 247–64

33. Robert Engle (2002), ‘Dynamic Conditional Correlation: A Simple Class of Multivariate Generalized Autoregressive Conditional Heteroskedasticity Models’, Journal of Business and Economic Statistics, 20 (3), July, 339–50

34. Andrew J. Patton (2006), ‘Modelling Asymmetric Exchange Rate Dependence’, International Economic Review, 47 (2), May, 527–56



Volume II

Contents

Acknowledgements

Introduction An introduction to both volumes by the editors appears in Volume I

PART I OPTIONS AND VOLATILITY
1. Henry A. Latané and Richard J. Rendleman, Jr. (1976), ‘Standard Deviations of Stock Price Ratios Implied in Option Prices’, Journal of Finance, XXXI (2), May, 369–81, Correction

2. John Hull and Alan White (1987), ‘The Pricing of Options on Assets with Stochastic Volatilities’, Journal of Finance, XLII (2), June, 281–300

3. Steven L. Heston (1993), ‘A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options’, Review of Financial Studies, 6 (2), April, 327–43

4. Jin-Chuan Duan (1995), ‘The GARCH Option Pricing Model’, Mathematical Finance, 5 (1), January, 13–32

5. David S. Bates (1996), ‘Jumps and Stochastic Volatility: Exchange Rate Processes Implicit in Deutsche Mark Options’, Review of Financial Studies, 9 (1), January, 69–107

6. Bjørn Eraker, Michael Johannes and Nicholas Polson (2003), ‘The Impact of Jumps in Volatility and Returns’, Journal of Finance, LVIII (3), June, 1269–300

7. Mark Britten-Jones and Anthony Neuberger (2000), ‘Option Prices, Implied Price Processes, and Stochastic Volatility’, Journal of Finance, LV (2), April, 839–66

8. Peter Carr and Liuren Wu (2009), ‘Variance Risk Premiums’, Review of Financial Studies, 22 (3), March, 1311–41

9. Tim Bollerslev, George Tauchen and Hao Zhou (2009), ‘Expected Stock Returns and Variance Risk Premia’, Review of Financial Studies, 22 (11), November, 4463–92

PART II VOLATILITY FORECASTING AND EVALUATION
10. Daniel B. Nelson (1992), ‘Filtering and Forecasting with Misspecified ARCH Models I: Getting the Right Variance with the Wrong Model’, Journal of Econometrics, 52 (1–2), April–May, 61–90

11. Dean P. Foster and Dan B. Nelson (1996), ‘Continuous Record Asymptotics for Rolling Sample Variance Estimators ’, Econometrica, 64 (1), January, 139–74

12. Torben G. Andersen and Tim Bollerslev (1997), ‘Intraday Periodicity and Volatility Persistence in Financial Markets’, Journal of Empirical Finance: High Frequency Data, Part 1, 4 (2–3), June, 115–58

13. Torben G. Andersen and Tim Bollerslev (1998), ‘Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts’, International Economic Review: Symposium on Forecasting and Empirical Methods in Macroeconomics and Finance, 39 (4), November, 885–905

14. Torben G. Andersen, Tim Bollerslev and Nour Meddahi (2004), ‘Analytical Evaluation of Volatility Forecasts,’ International Economic Review, 45 (4), November, 1079–110

15. Andrew J. Patton (2011), ‘Volatility Forecast Comparison Using Imperfect Volatility Proxies’, Journal of Econometrics: Realized Volatility, 160 (1), January, 246–56

16. Jeff Fleming, Chris Kirby and Barbara Ostdiek (2003), ‘The Economic Value of Volatility Timing Using “Realized” Volatility’, Journal of Financial Economics, 67 (3), March, 473–509

PART III HIGH-FREQUENCY DATA AND REALIZED VOLATILITIES
17. Torben G. Andersen, Tim Bollerslev, Francis X. Diebold and Paul Labys (2001), ‘The Distribution of Realized Exchange Rate Volatility’, Journal of the American Statistical Association, 96 (453), March, 42–55, Correction

18. Torben G. Andersen, Tim Bollerslev, Francis X. Diebold and Paul Labys (2003), ‘Modeling and Forecasting Realized Volatility’, Econometrica, 71 (2), March, 579–625

19. Fulvio Corsi (2009), ‘A Simple Approximate Long-Memory Model of Realized Volatility’, Journal of Financial Econometrics, 7 (2), Spring, 174–96

20. Eric Ghysels, Pedro Santa-Clara and Rossen Valkanov (2006), ‘Predicting Volatility: Getting the Most out of Return Data Sampled at Different Frequencies’, Journal of Econometrics, 131 (1–2), March–April, 59–95

21. Torben G. Andersen, Tim Bollerslev, Francis X. Diebold and Clara Vega (2003), ‘Micro Effects of Macro Announcements: Real-Time Price Discovery in Foreign Exchange’, American Economic Review, 93 (1), March, 38–62

22. Ole E. Barndorff-Nielsen and Neil Shephard (2004), ‘Power and Bipower Variation with Stochastic Volatility and Jumps’, Journal of Financial Econometrics, 2 (1), January, 1–37

23. Torben G. Andersen, Tim Bollerslev and Francis X. Diebold (2007), ‘Roughing it up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility’, Review of Economics and Statistics, 89 (4), November, 701–20

24. Cecilia Mancini (2009), ‘Non-parametric Threshold Estimation for Models with Stochastic Diffusion Coefficient and Jumps’, Scandinavian Journal of Statistics, 36 (2), June, 270–96

25. Peter R. Hansen and Asger Lunde (2006), ’Realized Variance and Market Microstructure Noise’, Journal of Business and Economic Statistics, 24 (2), April, 127–61

26. Bin Zhou (1996), ‘High-Frequency Data and Volatility in Foreign-Exchange Rates’, Journal of Business and Economic Statistics, 14 (1), January, 45–52

27. Ole E. Barndorff-Nielsen, Peter Reinhard Hansen, Asger Lunde and Neil Shephard (2008), ‘Designing Realized Kernels to Measure the Ex Post Variation of Equity Prices in the Presence of Noise’, Econometrica, 76 (6), November, 1481–536

28. Lan Zhang, Per A. Mykland and Yacine Aït-Sahalia (2005), ‘A Tale of Two Time Scales: Determining Integrated Volatility With Noisy High-Frequency Data’, Journal of the American Statistical Association, 100 (472), December, 1394–411

29. Jean Jacod, Yingying Li, Per A. Mykland, Mark Podolskij and Mathias Vetter (2009), ‘Microstructure Noise in the Continuous Case: The Pre-Averaging Approach’, Stochastic Processes and their Applications, 119 (7), July, 2249–76

30. Thomas W. Epps (1979), ‘Comovements in Stock Prices in the Very Short Run’, Journal of the American Statistical Association, 74 (366a), June, 291–8

31. Ole E. Barndorff-Nielsen and Neil Shephard (2004), ‘Econometric Analysis of Realized Covariation: High Frequency Based Covariance, Regression, and Correlation in Financial Economics’, Econometrica, 72 (3), May, 885–925

Index