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Information Loss in Volatility Measurement with Flat Price Trading

Author

Listed:
  • Peter C.B.Phillips

    (Yale University, University of Auckland,University of York & Singapore Management University)

  • Jun Yu

    (Sim Kee Boon Institute for Financial Economics, Singapore Management University)

Abstract

A model of financial asset price determination is proposed that incorporates flat trading features into an e¡é cient price process. The model involves the superposition of a Brownian semimartin- gale process for the efficient price and a Bernoulli process that determines the extent of flat price trading. The approach is related to sticky price modeling and the Calvo pricing mecha- nism in macroeconomic dynamics. A limit theory for the conventional realized volatility (RV) measure of integrated volatility is developed. The results show that RV is still consistent but has an inflated asymptotic variance that depends on the probability of flat trading. Estimated quarticity is similarly affected, so that both the feasible central limit theorem and the infer- ential framework suggested in Barndorff-Nielson and Shephard (2002) remain valid under flat price trading even though there is information loss due to flat trading e¡èects. The results are related to work by Jacod (1993) and Mykland and Zhang (2006) on realized volatility measures with random and intermittent sampling, and to ACD models for irregularly spaced transac- tions data. Extensions are given to include models with microstructure noise. Some simulation results are reported. Empirical evaluations with tick-by-tick data indicate that the effect of flat trading on the limit theory under microstructure noise is likely to be minor in most cases, thereby affirming the relevance of existing approaches.

Suggested Citation

  • Peter C.B.Phillips & Jun Yu, 2008. "Information Loss in Volatility Measurement with Flat Price Trading," Working Papers CoFie-01-2008, Singapore Management University, Sim Kee Boon Institute for Financial Economics.
    • Handle: RePEc:skb:wpaper:cofie-01-2008
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    File URL: http://www.smu.edu.sg/institutes/skbife/downloads/CoFiE/Working%20Papers/Information%20Loss%20in%20Volatility%20Measurement%20with%20Flat%20Price%20Trading.pdf
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    Cited by:

    1. Kolokolov, Aleksey & Livieri, Giulia & Pirino, Davide, 2020. "Statistical inferences for price staleness," Journal of Econometrics, Elsevier, vol. 218(1), pages 32-81.
    2. Huang, Shirley J. & Yu, Jun, 2010. "Bayesian analysis of structural credit risk models with microstructure noises," Journal of Economic Dynamics and Control, Elsevier, vol. 34(11), pages 2259-2272, November.
    3. Peter C. B. Phillips & Jun Yu, 2024. "Information loss in volatility measurement with flat price trading," Advanced Studies in Theoretical and Applied Econometrics, in: Subal C. Kumbhakar & Robin C. Sickles & Hung-Jen Wang (ed.), Advances in Applied Econometrics, pages 501-543, Springer.
    4. Ying Jiang & Shamim Ahmed & Xiaoquan Liu, 2017. "Volatility forecasting in the Chinese commodity futures market with intraday data," Review of Quantitative Finance and Accounting, Springer, vol. 48(4), pages 1123-1173, May.
    5. Bandi, Federico M. & Pirino, Davide & Renò, Roberto, 2024. "Systematic staleness," Journal of Econometrics, Elsevier, vol. 238(1).
    6. Federico M. Bandi & Aleksey Kolokolov & Davide Pirino & Roberto Renòo, 2020. "Zeros," Management Science, INFORMS, vol. 66(8), pages 3466-3479, August.

    More about this item

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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