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Estimating Risk in Illiquid Markets: a Model of Market Friction with Stochastic Volatility

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We deal with the problem of estimating the volatility of a financial security in a market with frictions. To this end, it is proposed a microstructure model in which the trading price varies only if the value of the information signal is large enough to guarantee a profit in excess of transaction costs. The main statistical properties of such a model are derived and discussed extensively. Using transaction data only, the proposed approach allows to recover: (i) the conditional volatility of the information signal, which is thus cleaned out by market frictions, (ii) an estimate of transaction costs. Our analysis reveals that, after correcting for frictions, the risk of illiquid securities is substantially different from what predicted by traditional volatility models. Furthermore, in periods of high volatility, our estimate of transaction costs remains highly correlated with bid-ask spreads, whereas alternative illiquidity proxies, such as the fraction of zero returns, loose their explanatory power.

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  • Giuseppe Buccheri & Stefano Grassi & Giorgio Vocalelli, 2021. "Estimating Risk in Illiquid Markets: a Model of Market Friction with Stochastic Volatility," CEIS Research Paper 506, Tor Vergata University, CEIS, revised 08 Nov 2021.
    • Handle: RePEc:rtv:ceisrp:506
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    1. F. Blasques & S. J. Koopman & A. Lucas, 2015. "Information-theoretic optimality of observation-driven time series models for continuous responses," Biometrika, Biometrika Trust, vol. 102(2), pages 325-343.
    2. Lucas, André & Opschoor, Anne & Schaumburg, Julia, 2016. "Accounting for missing values in score-driven time-varying parameter models," Economics Letters, Elsevier, vol. 148(C), pages 96-98.
    3. Creal, Drew & Koopman, Siem Jan & Lucas, André, 2011. "A Dynamic Multivariate Heavy-Tailed Model for Time-Varying Volatilities and Correlations," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(4), pages 552-563.
    4. Jianping Mei & Jose A. Scheinkman & Wei Xiong, 2009. "Speculative Trading and Stock Prices: Evidence from Chinese A-B Share Premia," Annals of Economics and Finance, Society for AEF, vol. 10(2), pages 225-255, November.
    5. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    6. Harvey,Andrew C., 2013. "Dynamic Models for Volatility and Heavy Tails," Cambridge Books, Cambridge University Press, number 9781107630024.
    7. Tata Subba Rao & Granville Tunnicliffe Wilson & Andrew Harvey & Rutger-Jan Lange, 2017. "Volatility Modeling with a Generalized t Distribution," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(2), pages 175-190, March.
    8. Geert Bekaert & Campbell R. Harvey & Christian Lundblad, 2007. "Liquidity and Expected Returns: Lessons from Emerging Markets," The Review of Financial Studies, Society for Financial Studies, vol. 20(6), pages 1783-1831, November.
    9. Lesmond, David A., 2005. "Liquidity of emerging markets," Journal of Financial Economics, Elsevier, vol. 77(2), pages 411-452, August.
    10. Nelson, Daniel B. & Foster, Dean P., 1995. "Filtering and forecasting with misspecified ARCH models II : Making the right forecast with the wrong model," Journal of Econometrics, Elsevier, vol. 67(2), pages 303-335, June.
    11. Andersen, Torben G. & Dobrev, Dobrislav & Schaumburg, Ernst, 2012. "Jump-robust volatility estimation using nearest neighbor truncation," Journal of Econometrics, Elsevier, vol. 169(1), pages 75-93.
    12. Stoll, Hans R, 1978. "The Supply of Dealer Services in Securities Markets," Journal of Finance, American Finance Association, vol. 33(4), pages 1133-1151, September.
    13. Harvey, A. & Liao, Y., 2019. "Dynamic Tobit models," Cambridge Working Papers in Economics 1913, Faculty of Economics, University of Cambridge.
    14. Gryglewicz, Sebastian, 2011. "A theory of corporate financial decisions with liquidity and solvency concerns," Journal of Financial Economics, Elsevier, vol. 99(2), pages 365-384, February.
    15. Randi Næs & Johannes A. Skjeltorp & Bernt Arne Ødegard, 2008. "Liquidity and the business cycle," Working Paper 2008/11, Norges Bank.
    16. Glosten, Lawrence R. & Milgrom, Paul R., 1985. "Bid, ask and transaction prices in a specialist market with heterogeneously informed traders," Journal of Financial Economics, Elsevier, vol. 14(1), pages 71-100, March.
    17. Linton, Oliver & Wu, Jianbin, 2020. "A coupled component DCS-EGARCH model for intraday and overnight volatility," Journal of Econometrics, Elsevier, vol. 217(1), pages 176-201.
    18. Nikolaus Hautsch & Peter Malec & Melanie Schienle, 2014. "Capturing the Zero: A New Class of Zero-Augmented Distributions and Multiplicative Error Processes," Journal of Financial Econometrics, Oxford University Press, vol. 12(1), pages 89-121.
    19. Joel Hasbrouck, 1999. "The Dynamics of Discrete Bid and Ask Quotes," Journal of Finance, American Finance Association, vol. 54(6), pages 2109-2142, December.
    20. Tina Hviid Rydberg & Neil Shephard, 2003. "Dynamics of Trade-by-Trade Price Movements: Decomposition and Models," Journal of Financial Econometrics, Oxford University Press, vol. 1(1), pages 2-25.
    21. Lesmond, David A & Ogden, Joseph P & Trzcinka, Charles A, 1999. "A New Estimate of Transaction Costs," The Review of Financial Studies, Society for Financial Studies, vol. 12(5), pages 1113-1141.
    22. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    23. repec:bla:jfinan:v:44:y:1989:i:2:p:479-86 is not listed on IDEAS
    24. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    25. Stoll, Hans R, 1978. "The Pricing of Security Dealer Services: An Empirical Study of NASDAQ Stocks," Journal of Finance, American Finance Association, vol. 33(4), pages 1153-1172, September.
    26. 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.
    27. John M. Griffin & Patrick J. Kelly & Federico Nardari, 2010. "Do Market Efficiency Measures Yield Correct Inferences? A Comparison of Developed and Emerging Markets," The Review of Financial Studies, Society for Financial Studies, vol. 23(8), pages 3225-3277, August.
    28. Harvey, Andrew & Ito, Ryoko, 2020. "Modeling time series when some observations are zero," Journal of Econometrics, Elsevier, vol. 214(1), pages 33-45.
    29. O. E. Barndorff-Nielsen & P. Reinhard Hansen & A. Lunde & N. Shephard, 2009. "Realized kernels in practice: trades and quotes," Econometrics Journal, Royal Economic Society, vol. 12(3), pages 1-32, November.
    30. Randi Næs & Johannes A. Skjeltorp & Bernt Arne Ødegaard, 2011. "Stock Market Liquidity and the Business Cycle," Journal of Finance, American Finance Association, vol. 66(1), pages 139-176, February.
    31. Nelson, Daniel B & Foster, Dean P, 1994. "Asymptotic Filtering Theory for Univariate ARCH Models," Econometrica, Econometric Society, vol. 62(1), pages 1-41, January.
    32. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    33. Goyenko, Ruslan Y. & Holden, Craig W. & Trzcinka, Charles A., 2009. "Do liquidity measures measure liquidity?," Journal of Financial Economics, Elsevier, vol. 92(2), pages 153-181, May.
    34. Drew Creal & Siem Jan Koopman & André Lucas, 2013. "Generalized Autoregressive Score Models With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(5), pages 777-795, August.
    35. Francis A. Longstaff, 2009. "Portfolio Claustrophobia: Asset Pricing in Markets with Illiquid Assets," American Economic Review, American Economic Association, vol. 99(4), pages 1119-1144, September.
    36. Sandmann, Gleb & Koopman, Siem Jan, 1998. "Estimation of stochastic volatility models via Monte Carlo maximum likelihood," Journal of Econometrics, Elsevier, vol. 87(2), pages 271-301, September.
    37. Amihud, Yakov & Mendelson, Haim, 1987. "Trading Mechanisms and Stock Returns: An Empirical Investigation," Journal of Finance, American Finance Association, vol. 42(3), pages 533-553, July.
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    More about this item

    Keywords

    Market microstructure; Illiquidity; Volatility estimation; Score-driven models;
    All these keywords.

    JEL classification:

    • B26 - Schools of Economic Thought and Methodology - - History of Economic Thought since 1925 - - - Financial Economics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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