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Estimating Stochastic Volatility and Jumps Using High-Frequency Data and Bayesian Methods

Listed author(s):
  • Milan Ficura

    ()

    (Faculty of Finance and Accounting, University of Economics, Prague)

  • Jiri Witzany

    ()

    (Faculty of Finance and Accounting, University of Economics, Prague)

We compare two approaches for estimation of stochastic volatility and jumps in the EUR//USD time series—the non-parametric power-variation approach using high-frequency returns and the parametric Bayesian approach (MCMC estimation of SVJD models) using daily returns. We have found that the estimated jump probabilities based on these two methods are surprisingly uncorrelated (using a rank correlation coefficient). This means that the two methods do not identify jumps on the same days. We further found that the non-parametrically identified jumps are in fact almost indistinguishable from the continuous price volatility at the daily frequency because they are too small. In most cases, the parametric approach using daily data does not in fact identify real jumps (i.e. discontinuous price changes) but rather only large returns caused by continuous price volatility. So if these unusually high daily returns are to be modelled, the parametric approach should be used, but if the goal is to identify the discontinuous price changes in the price evolution, the non-parametric high-frequency-based methods should be preferred. Among other results, we further found that the non-parametrically identified jumps exhibit only weak clustering (analyzed using the Hawkes process), but relatively strong size dependency. In the case of parametrically identified jumps, no clustering was present. We further found that after the beginning of 2012, the amount of jumps in the EUR//USD series greatly increased, but the results of our study still hold.

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Article provided by Charles University Prague, Faculty of Social Sciences in its journal Finance a uver - Czech Journal of Economics and Finance.

Volume (Year): 66 (2016)
Issue (Month): 4 (August)
Pages: 278-301

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Handle: RePEc:fau:fauart:v:66:y:2016:i:4:p:278-301
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  1. Torben G. Andersen & Tim Bollerslev & Peter F. Christoffersen & Francis X. Diebold, 2005. "Volatility Forecasting," PIER Working Paper Archive 05-011, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
  2. Corsi, Fulvio & Pirino, Davide & Renò, Roberto, 2010. "Threshold bipower variation and the impact of jumps on volatility forecasting," Journal of Econometrics, Elsevier, vol. 159(2), pages 276-288, December.
  3. 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.
  4. Jan Hanousek & Evzen Kocenda & Jan Novotny, 2014. "Price jumps on European stock markets," Borsa Istanbul Review, Research and Business Development Department, Borsa Istanbul, vol. 14(1), pages 10-22, March.
  5. Jiří Witzany, 2013. "Estimating Correlated Jumps and Stochastic Volatilities," Prague Economic Papers, University of Economics, Prague, vol. 2013(2), pages 251-283.
  6. Emmanuel Bacry & Iacopo Mastromatteo & Jean-Fran\c{c}ois Muzy, 2015. "Hawkes processes in finance," Papers 1502.04592, arXiv.org, revised May 2015.
  7. Ole E. Barndorff-Nielsen, 2004. "Power and Bipower Variation with Stochastic Volatility and Jumps," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(1), pages 1-37.
  8. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 2002. "Bayesian Analysis of Stochastic Volatility Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 69-87, January.
  9. Jacquier, Eric & Polson, Nicholas G & Rossi, Peter E, 1994. "Bayesian Analysis of Stochastic Volatility Models: Comments: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 413-417, October.
  10. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold, 2007. "Roughing It Up: Including Jump Components in the Measurement, Modeling, and Forecasting of Return Volatility," The Review of Economics and Statistics, MIT Press, vol. 89(4), pages 701-720, November.
  11. repec:bla:restud:v:65:y:1998:i:3:p:361-93 is not listed on IDEAS
  12. Bjørn Eraker, 2004. "Do Stock Prices and Volatility Jump? Reconciling Evidence from Spot and Option Prices," Journal of Finance, American Finance Association, vol. 59(3), pages 1367-1404, 06.
  13. Zhang, Lan & Mykland, Per A. & Ait-Sahalia, Yacine, 2005. "A Tale of Two Time Scales: Determining Integrated Volatility With Noisy High-Frequency Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1394-1411, December.
  14. Prosper Dovonon & Sílvia Gonçalves & Ulrich Hounyo & Nour Meddahi, 2016. "Bootstrapping high-frequency jump tests," CIRANO Working Papers 2016s-24, CIRANO.
  15. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2008. "Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise," Econometrica, Econometric Society, vol. 76(6), pages 1481-1536, November.
  16. Suzanne S. Lee & Per A. Mykland, 2008. "Jumps in Financial Markets: A New Nonparametric Test and Jump Dynamics," Review of Financial Studies, Society for Financial Studies, vol. 21(6), pages 2535-2563, November.
  17. Aït-Sahalia, Yacine & Cacho-Diaz, Julio & Laeven, Roger J.A., 2015. "Modeling financial contagion using mutually exciting jump processes," Journal of Financial Economics, Elsevier, vol. 117(3), pages 585-606.
  18. 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.
  19. Markku Lanne, 2006. "Forecasting Realized Volatility by Decomposition," Economics Working Papers ECO2006/20, European University Institute.
  20. Andersen, Torben G. & Dobrev, Dobrislav & Schaumburg, Ernst, 2014. "A Robust Neighborhood Truncation Approach To Estimation Of Integrated Quarticity," Econometric Theory, Cambridge University Press, vol. 30(01), pages 3-59, February.
  21. Viktor Todorov, 2010. "Variance Risk-Premium Dynamics: The Role of Jumps," Review of Financial Studies, Society for Financial Studies, vol. 23(1), pages 345-383, January.
  22. Shephard, Neil (ed.), 2005. "Stochastic Volatility: Selected Readings," OUP Catalogue, Oxford University Press, number 9780199257201.
  23. Andras Fulop & Junye Li & Jun Yu, 2015. "Self-Exciting Jumps, Learning, and Asset Pricing Implications," Review of Financial Studies, Society for Financial Studies, vol. 28(3), pages 876-912.
  24. Carla Ysusi, 2006. "Detecting Jumps in High-Frequency Financial Series Using Multipower Variation," Working Papers 2006-10, Banco de México.
  25. Novotný, Jan & Petrov, Dmitri & Urga, Giovanni, 2015. "Trading price jump clusters in foreign exchange markets," Journal of Financial Markets, Elsevier, vol. 24(C), pages 66-92.
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