Modeling price clustering in high-frequency prices

References

• Ahn, H.J., Cai, J. and Cheung, Y.L., Price clustering on the limit-order book: Evidence from the stock exchange of Hong Kong. J. Financ. Markets, 2005, 8, 421–451.
   Web of Science ®Google Scholar
• Aitken, M., Brown, P., Buckland, C., Izan, H.Y. and Walter, T., Price clustering on the Australian stock exchange. Pacific-Basin Finance J., 1996, 4, 297–314.
   Google Scholar
• Alexander, G.J. and Peterson, M.A., An analysis of trade-size clustering and its relation to stealth trading. J. Financ. Econ., 2007, 84, 435–471.
   Web of Science ®Google Scholar
• ap Gwilym, O., Clare, A. and Thomas, S., Extreme price clustering in the London equity index futures and options markets. J. Bank. Finance, 1998, 22, 1193–1206.
   Web of Science ®Google Scholar
• ap Gwilym, O. and Verousis, T., Price clustering in individual equity options: Moneyness, maturity, and price level. J. Futures Markets, 2013, 33, 55–76.
   Web of Science ®Google Scholar
• Baig, A., Blau, B.M. and Sabah, N., Price clustering and sentiment in bitcoin. Finance Res. Lett., 2019, 29, 111–116.
   Web of Science ®Google Scholar
• Ball, C.A., Torous, W.N. and Tschoegl, A.E., The degree of price resolution: The case of the gold market. J. Futures Markets, 1985, 5, 29–43.
   Web of Science ®Google Scholar
• Barndorff-Nielsen, O.E., Pollard, D.G. and Shephard, N., Integer-valued Lévy processes and low latency financial econometrics. Quant. Finance, 2012, 12, 587–605.
   Web of Science ®Google Scholar
• Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A. and Shephard, N., Designing realized kernels to measure the ex post variation of equity prices in the presence of noise. Econometrica, 2008, 76, 1481–1536.
   Web of Science ®Google Scholar
• Barndorff-Nielsen, O.E., Hansen, P.R., Lunde, A. and Shephard, N., Realized kernels in practice: Trades and quotes. Econom. J., 2009, 12, 1–32.
   Web of Science ®Google Scholar
• Bharati, R., Crain, S.J. and Kaminski, V., Clustering in crude oil prices and the target pricing zone hypothesis. Energy Econ., 2012, 34, 1115–1123.
   Web of Science ®Google Scholar
• Blasques, F., Holý, V. and Tomanová, P., Zero-inflated autoregressive conditional duration model for discrete trade durations with excessive zeros, 2020. https://arxiv.org/abs/1812.07318.
   Google Scholar
• Blasques, F., Koopman, S.J. and Lucas, A., Information-theoretic optimality of observation-driven time series models for continuous responses. Biometrika, 2015, 102, 325–343.
   Web of Science ®Google Scholar
• Blau, B.M., Price clustering and investor sentiment. J. Behavioral Finance, 2019, 20, 19–30.
   Web of Science ®Google Scholar
• Blau, B.M. and Griffith, T.G., Price clustering and the stability of stock prices. J. Bus. Res., 2016, 69, 3933–3942.
   Web of Science ®Google Scholar
• Boehmer, E., Fong, K. and Wu, J., Algorithmic trading and market quality: International evidence. J. Financ. Quant. Anal., 2020, 56, 2659–2688.
   Web of Science ®Google Scholar
• Bollerslev, T., Generalized autoregressive conditional heteroskedasticity. J. Econom., 1986, 31, 307–327.
   Web of Science ®Google Scholar
• Bollerslev, T., A conditionally heteroskedastic time series model for speculative prices and rates of return. Rev. Econ. Statist., 1987, 69, 542–547.
   Web of Science ®Google Scholar
• Bourguignon, M., Rodrigues, J. and Santos-Neto, M., Extended Poisson INAR(1) processes with equidispersion, underdispersion and overdispersion. J. Appl. Stat., 2019, 46, 101–118.
   Web of Science ®Google Scholar
• Box, T. and Griffith, T., Price clustering asymmetries in limit order flows. Financ. Manage., 2016, 45, 1041–1066.
   Web of Science ®Google Scholar
• Brent, R.P., Algorithms for Minimization Without Derivatives, 1972 (Prentice-Hall: Englewood Cliffs).
   Google Scholar
• Brooks, R., Harris, E. and Joymungul, Y., Price clustering in Australian water markets. Appl. Econ., 2013, 45, 677–685.
   Web of Science ®Google Scholar
• Brown, P. and Mitchell, J., Culture and stock price clustering: Evidence from The Peoples' Republic of China. Pacific-Basin Finance J., 2008, 16, 95–120.
   Google Scholar
• Buccheri, G., Bormetti, G., Corsi, F. and Lillo, F., A score-driven conditional correlation model for noisy and asynchronous data: An application to high-frequency covariance dynamics. J. Bus. Econ. Stat., 2021, 39, 920–936.
   Web of Science ®Google Scholar
• Capelle-Blancard, G. and Chaudhury, M., Price clustering in the CAC 40 index options market. Appl. Financ. Econ., 2007, 17, 1201–1210.
   Google Scholar
• Chiao, C. and Wang, Z.M., Price clustering: Evidence using comprehensive limit-order data. Financ. Rev., 2009, 44, 1–29.
   Google Scholar
• Christie, W.G. and Schultz, P.H., Why do NASDAQ market makers avoid odd-eighth quotes? J. Finance, 1994, 49, 1813–1840.
   Web of Science ®Google Scholar
• Chung, H. and Chiang, S., Price clustering in e-mini and floor-traded index futures. J. Futures Markets, 2006, 26, 269–295.
   Web of Science ®Google Scholar
• Chung, K.H., Kim, K.A. and Kitsabunnarat, P., Liquidity and quote clustering in a market with multiple tick sizes. J. Financ. Res., 2005, 28, 177–195.
   Google Scholar
• Chung, K.H., Van Ness, B.F. and Van Ness, R.A., Trading costs and quote clustering on the NYSE and NASDAQ after decimalization. J. Financ. Res., 2004, 27, 309–328.
   Google Scholar
• Cooney, J.W., Van Ness, B. and Van Ness, R., Do investors prefer even-eighth prices? Evidence from NYSE limit orders. J. Bank. Finance, 2003, 27, 719–748.
   Web of Science ®Google Scholar
• Creal, D., Koopman, S.J. and Lucas, A., Generalized autoregressive score models with applications. J. Appl. Econom., 2013, 28, 777–795.
   Web of Science ®Google Scholar
• Das, S. and Kadapakkam, P.R., Machine over mind? Stock price clustering in the era of algorithmic trading. North Amer. J. Econ. Finance, 2020, 51, 100831.
   Google Scholar
• Davis, R.L., Van Ness, B.F. and Van Ness, R.A., Clustering of trade prices by high-frequency and non-high-frequency trading firms. Financ. Rev., 2014, 49, 421–433.
   Google Scholar
• Efron, B., Double exponential families and their use in generalized linear regression. J. Am. Stat. Assoc., 1986, 81, 709–721.
   Web of Science ®Google Scholar
• Engle, R.F., The econometrics of ultra-high-frequency data. Econometrica, 2000, 68, 1–22.
   Web of Science ®Google Scholar
• Engle, R.F. and Russell, J.R., Autoregressive conditional duration: A new model for irregularly spaced transaction data. Econometrica, 1998, 66, 1127–1162.
   Web of Science ®Google Scholar
• Goodhart, C. and Curcio, R., The clustering of Bid/Ask prices and the spread in the foreign exchange market, 1991. https://www.fmg.ac.uk/publications/discussion-papers/clustering-bidask-prices-and-spread-foreign-exchange-market.
   Google Scholar
• Gorgi, P., Koopman, S.J. and Lit, R., The analysis and forecasting of tennis matches by using a high dimensional dynamic model. J. R. Statist. Soc.: Series A (Statistics in Society), 2019, 182, 1393–1409.
   Web of Science ®Google Scholar
• Hameed, A. and Terry, E., The effect of tick size on price clustering and trading volume. J. Bus. Finance. Account., 1998, 25, 849–867.
   Google Scholar
• Hansen, P.R. and Lunde, A., Realized variance and market microstructure noise. J. Bus. Econ. Stat., 2006, 24, 127–161.
   Web of Science ®Google Scholar
• Harris, L., Stock price clustering and discreteness. Rev. Financ. Stud., 1991, 4, 389–415.
   Web of Science ®Google Scholar
• Harvey, A., Hurn, S. and Thiele, S., Modeling directional (circular) time series, 2019. https://doi.org/10.17863/cam.43915
   Google Scholar
• Harvey, A.C., Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series, 1st ed. 2013 (Cambridge University Press: New York).
   Google Scholar
• Heinen, A., Modelling time series count data: An autoregressive conditional Poisson model, 2003. https://ssrn.com/abstract=1117187.
   Google Scholar
• Holý, V. and Tomanová, P., Streaming approach to quadratic covariation estimation using financial ultra-high-frequency data. Comput. Econ., 2021. https://doi.org/10.1007/s10614-021-10210-w.
   Google Scholar
• Holý, V. and Zouhar, J., Modelling time-varying rankings with autoregressive and score-driven dynamics, 2021. https://arxiv.org/abs/2101.04040.
   Google Scholar
• Hu, B., Jiang, C., McInish, T. and Zhou, H., Price clustering on the shanghai stock exchange. Appl. Econ., 2017, 49, 2766–2778.
   Web of Science ®Google Scholar
• Hu, B., McInish, T., Miller, J. and Zeng, L., Intraday price behavior of cryptocurrencies. Finance Res. Lett., 2019, 28, 337–342.
   Web of Science ®Google Scholar
• Ikenberry, D.L. and Weston, J.P., Clustering in US stock prices after decimalisation. Eur. Financ. Manag., 2008, 14, 30–54.
   Web of Science ®Google Scholar
• Kahn, C., Pennacchi, G. and Sopranzetti, B., Bank deposit rate clustering: Theory and empirical evidence. J. Finance, 1999, 54, 2185–2214.
   Web of Science ®Google Scholar
• Kandel, S., Sarig, O. and Wohl, A., Do investors prefer round stock prices? Evidence from Israeli IPO auctions. J. Bank. Finance, 2001, 25, 1543–1551.
   Web of Science ®Google Scholar
• Koopman, S.J., Lit, R. and Lucas, A., Intraday stochastic volatility in discrete price changes: The dynamic skellam model. J. Am. Stat. Assoc., 2017, 112, 1490–1503.
   Web of Science ®Google Scholar
• Koopman, S.J., Lit, R., Lucas, A. and Opschoor, A., Dynamic discrete copula models for high-frequency stock price changes. J. Appl. Econom., 2018, 33, 966–985.
   Web of Science ®Google Scholar
• Li, X., Li, S. and Xu, C., Price clustering in bitcoin market – An extension. Finance Res. Lett., 2020, 32, 101072.
   Google Scholar
• Lien, D., Hung, P.H. and Hung, I.C., Order price clustering, size clustering, and stock price movements: Evidence from the Taiwan stock exchange. J. Empir. Financ., 2019, 52, 149–177.
   Web of Science ®Google Scholar
• Liu, H.C., Timing of price clustering and trader behavior in the foreign exchange market: Evidence from Taiwan. J. Econ. Finance, 2011, 35, 198–210.
   Google Scholar
• Liu, H.C. and Witte, M.D., Price clustering in the U.S. dollar/Taiwan dollar swap market. Financ. Rev., 2013, 48, 77–96.
   Google Scholar
• Mbanga, C.L., The day-of-the-week pattern of price clustering in bitcoin. Appl. Econ. Lett., 2019, 26, 807–811.
   Web of Science ®Google Scholar
• Meng, L. and Verousis, T., A substitution effect between price clustering and size clustering in credit default swaps. J. Int. Financ. Markets, Inst. Money, 2013, 24, 139–152.
   Web of Science ®Google Scholar
• Mishra, A.K. and Tripathy, T., Price and trade size clustering: Evidence from the national stock exchange of India. Q. Rev. Econ. Finance, 2018, 68, 63–72.
   Web of Science ®Google Scholar
• Narayan, P.K. and Smyth, R., Has political instability contributed to price clustering on Fiji's stock market? J. Asian. Econ., 2013, 28, 125–130.
   Google Scholar
• Niederhoffer, V., Clustering of stock prices. Oper. Res., 1965, 13, 258–265.
   Web of Science ®Google Scholar
• Ohta, W., An analysis of intraday patterns in price clustering on the Tokyo stock exchange. J. Bank. Finance, 2006, 30, 1023–1039.
   Web of Science ®Google Scholar
• Osborne, M.F.M., Periodic structure in the Brownian motion of stock prices. Oper. Res., 1962, 10, 345–379.
   Web of Science ®Google Scholar
• Palao, F. and Pardo, A., Assessing price clustering in European carbon markets. Appl. Energy., 2012, 92, 51–56.
   Web of Science ®Google Scholar
• Robert, C.Y. and Rosenbaum, M., A new approach for the dynamics of ultra-high-frequency data: The model with uncertainty zones. J. Financ. Econom., 2011, 9, 344–366.
   Web of Science ®Google Scholar
• Roşu, I., Fast and slow informed trading. J. Financ. Markets, 2019, 43, 1–30.
   Web of Science ®Google Scholar
• Russell, J.R. and Engle, R.F., A discrete-state continuous-time model of financial transactions prices and times: The autoregressive conditional multinomial-autoregressive conditional duration model. J. Bus. Econ. Stat., 2005, 23, 166–180.
   Web of Science ®Google Scholar
• Schwartz, A.L., Van Ness, B.F. and Van Ness, R.A., Clustering in the futures market: Evidence from S&P 500 futures contracts. J. Futures Markets, 2004, 24, 413–428.
   Web of Science ®Google Scholar
• Sellers, K.F. and Morris, D.S., Underdispersion models: Models that are ‘Under the Radar’. Commun. Statist. – Theory Methods, 2017, 46, 12075–12086.
   Web of Science ®Google Scholar
• Shephard, N. and Yang, J.J., Continuous time analysis of fleeting discrete price moves. J. Am. Stat. Assoc., 2017, 112, 1090–1106.
   Web of Science ®Google Scholar
• Shkilko, A. and Sokolov, K., Every cloud has a silver lining: Fast trading, microwave connectivity, and trading costs. J. Finance, 2020, 75, 2899–2927.
   Web of Science ®Google Scholar
• Song, S., Wang, Y. and Xu, G., On the probability of default in a market with price clustering and jump risk. Math. Financ. Econ., 2020, 14, 225–247.
   Web of Science ®Google Scholar
• Sopranzetti, B.J. and Datar, V., Price clustering in foreign exchange spot markets. J. Financ. Markets, 2002, 5, 411–417.
   Web of Science ®Google Scholar
• Urquhart, A., Price clustering in bitcoin. Econ. Lett., 2017, 159, 145–148.
   Web of Science ®Google Scholar
• Verousis, T., Trade size clustering and the cost of trading at the London stock exchange. Int. Rev. Financ. Anal., 2013, 27, 91–102.
   Google Scholar
• Xu, H.Y., Xie, M., Goh, T.N. and Fu, X., A model for integer-valued time series with conditional overdispersion. Comput. Stat. Data. Anal., 2012, 56, 4229–4242.
   Web of Science ®Google Scholar
• Zou, Y., Geedipally, S.R. and Lord, D., Evaluating the double Poisson generalized linear model. Accident Anal. Prevention, 2013, 59, 497–505.
   PubMed Web of Science ®Google Scholar