Ioanna-Yvonni Tsaknaki, Fabrizio Lillo, Piero Mazzarisi
arXiv 5 Jul 2023 · Finance — Trading · publishedQuantitative Finance (2024) · 5 citations (OpenAlex)
arXiv:2307.02375 · PDF · DOI · OpenAlex · Extracted main text
Financial order flow exhibits a remarkable level of persistence, wherein buy (sell) trades are often followed by subsequent buy (sell) trades over extended periods. This persistence can be attributed to the division and gradual execution of large orders. Consequently, distinct order flow regimes might emerge, which can be identified through suitable time series models applied to market data. In this paper, we propose the use of Bayesian online change-point detection (BOCPD) methods to identify regime shifts in real-time and enable online predictions of order flow and market impact. To enhance the effectiveness of our approach, we have developed a novel BOCPD method using a score-driven approach. This method accommodates temporal correlations and time-varying parameters within each regime. Through empirical application to NASDAQ data, we have found that: (i) Our newly proposed model demonstrates superior out-of-sample predictive performance compared to existing models that assume i.i.d. behavior within each regime; (ii) When examining the residuals, our model demonstrates good specification in terms of both distributional assumptions and temporal correlations; (iii) Within a given regime, the price dynamics exhibit a concave relationship with respect to time and volume, mirroring the characteristics of actual large orders; (iv) By incorporating regime information, our model produces more accurate online predictions of order flow and market impact compared to models that do not consider regimes.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Bouchaud, J.-P., Gefen, Y., Potters, M., and Wyart, M (2004) Fluctuations and response in financial markets: the subtle nature of ‘random’ price changes | 1.000 | 5 | 4 | 100% |
| 2 | Lillo, F., Mike, S., and Farmer, J (2005) Theory for long memory in supply and demand self | 1.000 | 5 | 4 | 100% |
| 3 | Bouchaud, J.-P., Farmer, J. D., and Lillo, F (2009) How markets slowly digest changes in supply and demand self | 0.928 | 4 | 4 | 100% |
| 4 | Zarinelli, E., Treccani, M., Farmer, J., and Lillo, F (2015) Beyond the square root: Evidence for logarithmic dependence of market impact on size and participation rate self | 0.928 | 4 | 3 | 100% |
| 5 | Adams, R. P., and MacKay, D. J (2007) Bayesian online changepoint detection | 0.811 | 4 | 2 | 100% |
| 6 | Lillo, F. and Farmer, J. D (2004) The long memory of efficient market self | 0.737 | 3 | 2 | 100% |
| 7 | Creal, D., Koopman, S. J., and Lucas, A (2013) Generalized autoregressive score models with applications | 0.737 | 3 | 2 | 100% |
| 8 | Tóth, B., Palit, I., Lillo, F., and Farmer, J. D (2015) Why is equity order flow so persistent? Journal of Economic Dynamics and Control, 51:218–239 self | 0.737 | 3 | 2 | 100% |
| 9 | Tsaknaki, I.-Y., Lillo, F., and Mazzarisi, P (2023) A score-driven Bayesian online change-point detection model self | 0.737 | 3 | 2 | 100% |
| 10 | Harvey, A (2013) Dynamic models for volatility and heavy tails: with applications to financial and economic time series | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 46 scored citations.