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Microstructure effect on firm’s volatility risk

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  • Flavia Barsotti

    () (ISFA, University Lyon 1, France)

  • Simona Sanfelici

    () (Dipartimento di Economia, Universita' di Parma)

Abstract

Equity returns and firm's default probability are strictly interrelated financial measures capturing the credit risk profile of a firm. Following the idea proposed in [20] we use high-frequency equity prices in order to estimate the volatility risk component of a firm within Merton [17] structural model. Differently from [20] we consider a more general framework by introducing market microstructure noise as a direct effect of using noisy high-frequency data and propose the use of non- parametric estimation techniques in order to estimate equity volatility. We conduct a simulation analysis to compare the performance of different non-parametric volatil- ity estimators in their capability of i) filtering out the market microstructure noise, ii) extracting the (unobservable) true underlying asset volatility level, iii) predicting default probabilies calibrated from Merton [17] model.

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File URL: http://www.dmd.unifi.it/RePEc/flo/WorkingPapers/2012/DiMaDWP2012-05.pdf
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Bibliographic Info

Paper provided by Dipartimento di Matematica per le Decisioni, Universita' degli Studi di Firenze in its series DiMaD Working Papers with number 2012-05.

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Length: 14 pages
Date of creation: Oct 2012
Date of revision:
Handle: RePEc:flo:wpaper:2012-05

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Related research

Keywords: market microstructure noise; high-frequency data; non-parametric volatility estimation; Merton model; default probabilities; volatility risk;

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  1. Ole E. Barndorff-Nielsen & Peter R. Hansen & Asger Lunde & Neil Shephard, 2006. "Subsampling realised kernels," OFRC Working Papers Series 2006fe06, Oxford Financial Research Centre.
  2. Merton, Robert C., 1973. "On the pricing of corporate debt: the risk structure of interest rates," Working papers 684-73., Massachusetts Institute of Technology (MIT), Sloan School of Management.
  3. Ole E. Barndorff-Nielsen & Neil Shephard, 2000. "Econometric analysis of realised volatility and its use in estimating stochastic volatility models," Economics Papers 2001-W4, Economics Group, Nuffield College, University of Oxford, revised 05 Jul 2001.
  4. Duan, Jin-Chuan & Fulop, Andras, 2009. "Estimating the structural credit risk model when equity prices are contaminated by trading noises," Journal of Econometrics, Elsevier, vol. 150(2), pages 288-296, June.
  5. Mancino, M.E. & Sanfelici, S., 2008. "Robustness of Fourier estimator of integrated volatility in the presence of microstructure noise," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2966-2989, February.
  6. Jan Ericsson, 2005. "Estimating Structural Bond Pricing Models," The Journal of Business, University of Chicago Press, vol. 78(2), pages 707-735, March.
  7. 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.
  8. Jin-Chuan Duan, 1994. "Maximum Likelihood Estimation Using Price Data Of The Derivative Contract," Mathematical Finance, Wiley Blackwell, vol. 4(2), pages 155-167.
  9. Maria Elvira Mancino & Paul Malliavin, 2002. "Fourier series method for measurement of multivariate volatilities," Finance and Stochastics, Springer, vol. 6(1), pages 49-61.
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