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Can idiosyncratic volatility help forecast stock market volatility?

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  • Taylor, Nicholas

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  • Taylor, Nicholas, 2008. "Can idiosyncratic volatility help forecast stock market volatility?," International Journal of Forecasting, Elsevier, vol. 24(3), pages 462-479.
  • Handle: RePEc:eee:intfor:v:24:y:2008:i:3:p:462-479
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    1. Andersen T. G & Bollerslev T. & Diebold F. X & Labys P., 2001. "The Distribution of Realized Exchange Rate Volatility," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 42-55, March.
    2. Lamoureux, Christopher G & Lastrapes, William D, 1990. " Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects," Journal of Finance, American Finance Association, vol. 45(1), pages 221-229, March.
    3. Giot, Pierre & Laurent, Sebastien, 2004. "Modelling daily Value-at-Risk using realized volatility and ARCH type models," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 379-398, June.
    4. A. Ronald Gallant & Chien-Te Hsu & George Tauchen, 1999. "Using Daily Range Data To Calibrate Volatility Diffusions And Extract The Forward Integrated Variance," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 617-631, November.
    5. Jacob A. Mincer & Victor Zarnowitz, 1969. "The Evaluation of Economic Forecasts," NBER Chapters,in: Economic Forecasts and Expectations: Analysis of Forecasting Behavior and Performance, pages 3-46 National Bureau of Economic Research, Inc.
    6. Chan, Choon Chat & Fong, Wai Mun, 2006. "Realized volatility and transactions," Journal of Banking & Finance, Elsevier, vol. 30(7), pages 2063-2085, July.
    7. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    8. John Y. Campbell, 2001. "Have Individual Stocks Become More Volatile? An Empirical Exploration of Idiosyncratic Risk," Journal of Finance, American Finance Association, vol. 56(1), pages 1-43, February.
    9. Guo, Hui & Savickas, Robert, 2006. "Idiosyncratic Volatility, Stock Market Volatility, and Expected Stock Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 43-56, January.
    10. Pong, Shiuyan & Shackleton, Mark B. & Taylor, Stephen J. & Xu, Xinzhong, 2004. "Forecasting currency volatility: A comparison of implied volatilities and AR(FI)MA models," Journal of Banking & Finance, Elsevier, vol. 28(10), pages 2541-2563, October.
    11. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    12. Philip Hans Franses & Michael McAleer, 2002. "Financial volatility: an introduction," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 419-424.
    13. John L. Evans & Stephen H. Archer, 1968. "Diversification And The Reduction Of Dispersion: An Empirical Analysis," Journal of Finance, American Finance Association, vol. 23(5), pages 761-767, December.
    14. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    15. Ole E. Barndorff‐Nielsen & Neil Shephard, 2002. "Econometric analysis of realized volatility and its use in estimating stochastic volatility models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 253-280, May.
    16. Harvey, David I & Leybourne, Stephen J & Newbold, Paul, 1998. "Tests for Forecast Encompassing," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 254-259, April.
    17. Foster, F Douglas & Viswanathan, S, 1993. " Variations in Trading Volume, Return Volatility, and Trading Costs: Evidence on Recent Price Formation Models," Journal of Finance, American Finance Association, vol. 48(1), pages 187-211, March.
    18. Lee, Dongin & Schmidt, Peter, 1996. "On the power of the KPSS test of stationarity against fractionally-integrated alternatives," Journal of Econometrics, Elsevier, vol. 73(1), pages 285-302, July.
    19. Artyom Durnev & Randall Morck & Bernard Yeung & Paul Zarowin, 2003. "Does Greater Firm‐Specific Return Variation Mean More or Less Informed Stock Pricing?," Journal of Accounting Research, Wiley Blackwell, vol. 41(5), pages 797-836, December.
    20. Brailsford, Timothy J. & Faff, Robert W., 1996. "An evaluation of volatility forecasting techniques," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 419-438, April.
    21. Bollerslev, Tim & Jubinski, Dan, 1999. "Equity Trading Volume and Volatility: Latent Information Arrivals and Common Long-Run Dependencies," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 9-21, January.
    22. French, Kenneth R. & Roll, Richard, 1986. "Stock return variances : The arrival of information and the reaction of traders," Journal of Financial Economics, Elsevier, vol. 17(1), pages 5-26, September.
    23. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    24. Jin, Li & Myers, Stewart C., 2006. "R2 around the world: New theory and new tests," Journal of Financial Economics, Elsevier, vol. 79(2), pages 257-292, February.
    25. Epps, Thomas W & Epps, Mary Lee, 1976. "The Stochastic Dependence of Security Price Changes and Transaction Volumes: Implications for the Mixture-of-Distributions Hypothesis," Econometrica, Econometric Society, vol. 44(2), pages 305-321, March.
    26. Melvin, Michael & Yin, Xixi, 2000. "Public Information Arrival, Exchange Rate Volatility, and Quote Frequency," Economic Journal, Royal Economic Society, vol. 110(465), pages 644-661, July.
    27. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    28. Richardson, Matthew & Smith, Tom, 1994. "A Direct Test of the Mixture of Distributions Hypothesis: Measuring the Daily Flow of Information," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 29(01), pages 101-116, March.
    29. Foster, F Douglas & Viswanathan, S, 1995. "Can Speculative Trading Explain the Volume-Volatility Relation?," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(4), pages 379-396, October.
    30. Bessembinder, Hendrik & Seguin, Paul J., 1993. "Price Volatility, Trading Volume, and Market Depth: Evidence from Futures Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 28(01), pages 21-39, March.
    31. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    32. Galbraith, John W. & KI[#x1e63]Inbay, Turgut, 2005. "Content horizons for conditional variance forecasts," International Journal of Forecasting, Elsevier, vol. 21(2), pages 249-260.
    33. Jones, Charles M. & Lamont, Owen & Lumsdaine, Robin L., 1998. "Macroeconomic news and bond market volatility," Journal of Financial Economics, Elsevier, vol. 47(3), pages 315-337, March.
    34. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    35. Stivers, Christopher T., 2003. "Firm-level return dispersion and the future volatility of aggregate stock market returns," Journal of Financial Markets, Elsevier, vol. 6(3), pages 389-411, May.
    36. Jones, Charles M. & Kaul, Gautam & Lipson, Marc L., 1994. "Information, trading, and volatility," Journal of Financial Economics, Elsevier, vol. 36(1), pages 127-154, August.
    37. Miguel A. Ferreira & Paul A. Laux, 2007. "Corporate Governance, Idiosyncratic Risk, and Information Flow," Journal of Finance, American Finance Association, vol. 62(2), pages 951-989, April.
    38. Yock Y. Chong & David F. Hendry, 1986. "Econometric Evaluation of Linear Macro-Economic Models," Review of Economic Studies, Oxford University Press, vol. 53(4), pages 671-690.
    39. Karpoff, Jonathan M., 1987. "The Relation between Price Changes and Trading Volume: A Survey," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(01), pages 109-126, March.
    40. Patton, Andrew J., 2011. "Volatility forecast comparison using imperfect volatility proxies," Journal of Econometrics, Elsevier, vol. 160(1), pages 246-256, January.
    41. Tim Bollerslev & Jonathan H. Wright, 2001. "High-Frequency Data, Frequency Domain Inference, And Volatility Forecasting," The Review of Economics and Statistics, MIT Press, vol. 83(4), pages 596-602, November.
    42. Art Durnev & Randall Morck & Bernard Yeung, 2004. "Value-Enhancing Capital Budgeting and Firm-specific Stock Return Variation," Journal of Finance, American Finance Association, vol. 59(1), pages 65-105, February.
    43. Jeff Fleming, 2001. "The Economic Value of Volatility Timing," Journal of Finance, American Finance Association, vol. 56(1), pages 329-352, February.
    44. Liesenfeld, Roman, 2001. "A generalized bivariate mixture model for stock price volatility and trading volume," Journal of Econometrics, Elsevier, vol. 104(1), pages 141-178, August.
    45. Mitchell, Mark L & Mulherin, J Harold, 1994. " The Impact of Public Information on the Stock Market," Journal of Finance, American Finance Association, vol. 49(3), pages 923-950, July.
    46. Lamoureux, Christopher G & Lastrapes, William D, 1994. "Endogenous Trading Volume and Momentum in Stock-Return Volatility," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(2), pages 253-260, April.
    47. Zhou, Bin, 1996. "High-Frequency Data and Volatility in Foreign-Exchange Rates," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 45-52, January.
    48. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-155, January.
    49. Barclay, Michael J & Litzenberger, Robert H & Warner, Jerold B, 1990. "Private Information, Trading Volume, and Stock-Return Variances," Review of Financial Studies, Society for Financial Studies, vol. 3(2), pages 233-253.
    50. Statman, Meir, 1987. "How Many Stocks Make a Diversified Portfolio?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(03), pages 353-363, September.
    51. Bart Hobijn & Philip Hans Franses & Marius Ooms, 2004. "Generalizations of the KPSS-test for stationarity," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(4), pages 483-502.
    52. Hansen, Peter R. & Lunde, Asger, 2006. "Realized Variance and Market Microstructure Noise," Journal of Business & Economic Statistics, American Statistical Association, vol. 24, pages 127-161, April.
    53. Owens, John & Steigerwald, Douglas G, 2009. "Noise Reduced Realized Volatility: A Kalman Filter Approach," University of California at Santa Barbara, Economics Working Paper Series qt4n80536m, Department of Economics, UC Santa Barbara.
    54. Ole E. Barndorff‐Nielsen & Neil Shephard, 2001. "Non‐Gaussian Ornstein–Uhlenbeck‐based models and some of their uses in financial economics," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 167-241.
    55. Harris, Lawrence, 1987. "Transaction Data Tests of the Mixture of Distributions Hypothesis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 22(02), pages 127-141, June.
    56. Jennings, Robert H & Starks, Laura T & Fellingham, John C, 1981. "An Equilibrium Model of Asset Trading with Sequential Information Arrival," Journal of Finance, American Finance Association, vol. 36(1), pages 143-161, March.
    57. Donaldson, R.G. & Kamstra, M., 2001. "Volatility Forecasts, Trading Volume and the ARCH vs. Option-Implied Volatility Tradeoff," Discussion Papers dp01-1, Department of Economics, Simon Fraser University.
    58. Mark J. Flannery & Aris A. Protopapadakis, 2002. "Macroeconomic Factors Do Influence Aggregate Stock Returns," Review of Financial Studies, Society for Financial Studies, vol. 15(3), pages 751-782.
    59. Amit Goyal & Pedro Santa-Clara, 2003. "Idiosyncratic Risk Matters!," Journal of Finance, American Finance Association, vol. 58(3), pages 975-1008, June.
    60. Copeland, Thomas E, 1976. "A Model of Asset Trading under the Assumption of Sequential Information Arrival," Journal of Finance, American Finance Association, vol. 31(4), pages 1149-1168, September.
    61. Andersen, Torben G, 1996. " Return Volatility and Trading Volume: An Information Flow Interpretation of Stochastic Volatility," Journal of Finance, American Finance Association, vol. 51(1), pages 169-204, March.
    62. Ericsson, Neil R., 1992. "Parameter constancy, mean square forecast errors, and measuring forecast performance: An exposition, extensions, and illustration," Journal of Policy Modeling, Elsevier, vol. 14(4), pages 465-495, August.
    63. Kalev, Petko S. & Liu, Wai-Man & Pham, Peter K. & Jarnecic, Elvis, 2004. "Public information arrival and volatility of intraday stock returns," Journal of Banking & Finance, Elsevier, vol. 28(6), pages 1441-1467, June.
    64. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    1. Gharbi, Sami & Sahut, Jean-Michel & Teulon, Frédéric, 2014. "R&D investments and high-tech firms' stock return volatility," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 306-312.

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