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(Understanding, Optimizing, Using and Forecasting) Realized Volatility and Correlation

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Author Info

  • Torben G. Andersen
  • Tim Bollerslev
  • Francis X. Diebold
  • Paul Labys

Abstract

We review and synthesize our recent work on realized volatility in financial markets. This includes (1) constructing and interpreting realized volatilities for a variety of asset returns ("understanding"), (2) determining underlying sampling frequencies high enough to produce precise estimates yet low enough to mitigate microstructure bias ("optimizing"), (3) putting realized volatilities to work in various contexts, such as the production of standardized returns series with desirable properties ("using"), and (4) using predictions of realized volatility for improved financial risk management ("forecasting").

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File URL: http://www.stern.nyu.edu/fin/workpapers/papers99/wpa99061.pdf
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Bibliographic Info

Paper provided by New York University, Leonard N. Stern School of Business- in its series New York University, Leonard N. Stern School Finance Department Working Paper Seires with number 99-061.

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Date of creation: 26 Oct 1999
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Handle: RePEc:fth:nystfi:99-061

Contact details of provider:
Postal: U.S.A.; New York University, Leonard N. Stern School of Business, Department of Economics . 44 West 4th Street. New York, New York 10012-1126
Phone: (212) 998-0100
Web page: http://w4.stern.nyu.edu/finance/
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References

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  1. G. William Schwert, 1998. "Stock Market Volatility: Ten Years After the Crash," NBER Working Papers 6381, National Bureau of Economic Research, Inc.
  2. Torben Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 1999. "The Distribution of Exchange Rate Volatility," NBER Working Papers 6961, National Bureau of Economic Research, Inc.
  3. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
  4. Bollerslev, Tim, 1987. "A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return," The Review of Economics and Statistics, MIT Press, vol. 69(3), pages 542-47, August.
  5. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
  6. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2000. "Exchange Rate Returns Standardized by Realized Volatility are (Nearly) Gaussian," NBER Working Papers 7488, National Bureau of Economic Research, Inc.
  7. Merton, Robert C., 1980. "On estimating the expected return on the market : An exploratory investigation," Journal of Financial Economics, Elsevier, vol. 8(4), pages 323-361, December.
  8. Nelson, Daniel B., 1992. "Filtering and forecasting with misspecified ARCH models I : Getting the right variance with the wrong model," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 61-90.
  9. Bollerslev, Tim & Engle, Robert F. & Nelson, Daniel B., 1986. "Arch models," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 49, pages 2959-3038 Elsevier.
  10. Jeff Fleming, 2001. "The Economic Value of Volatility Timing," Journal of Finance, American Finance Association, vol. 56(1), pages 329-352, 02.
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Cited by:
  1. Katarzyna Bien & Ingmar Nolte & Winfried Pohlmeier, 2007. "An Inflated Multivariate Integer Count Hurdle Model: An Application to Bid and Ask Quote Dynamics," CoFE Discussion Paper 07-04, Center of Finance and Econometrics, University of Konstanz.
  2. Amir Safari & Detlef Seese, 2010. "Behavior of realized volatility and correlation in exchange markets," Articles of International Econometric Review (IER), Econometric Research Association, vol. 2(2), pages 73-96, September.
  3. Scott I. White & Adam E. Clements & Stan Hurn, 2004. "Discretised Non-Linear Filtering for Dynamic Latent Variable Models: with Application to Stochastic Volatility," Econometric Society 2004 Australasian Meetings 46, Econometric Society.
  4. Georgios Chortareas & John Nankervis & Ying Jiang, 2007. "Forecasting Exchange Rate Volatility with High Frequency Data: Is the Euro Different?," Money Macro and Finance (MMF) Research Group Conference 2006 79, Money Macro and Finance Research Group.
  5. Scott I White & Ralf Becker & Adam E Clements, 2004. "Forward looking information in S&P 500 options," Econometric Society 2004 Australasian Meetings 233, Econometric Society.
  6. Katarzyna Bien & Ingmar Nolte & Winfried Pohlmeier, 2006. "A Multivariate Integer Count Hurdle Model: Theory and Application to Exchange Rate Dynamics," CoFE Discussion Paper 06-06, Center of Finance and Econometrics, University of Konstanz.

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