PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2022 | nr 2(18) | 49--59
Tytuł artykułu

How Do the Lengths of the Lead Lag Time between Stocks Evolve? Tick-by-tick Level Measurements across Two Decades

Autorzy
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
There has been an extraordinary decrease in order execution time on stock exchanges in the past two decades. A related question is whether there has been a similar reduction in orders of magnitude for the lengths of the lead lag time between stocks. If the answer is affirmative, and the lengths of the lead lag time have long fallen below the human reaction time, algorithms have taken over information diffusion from one stock to another. Otherwise, humans continue to be in authority. In this study, the lengths of the lead lag time within pairs of stocks of large US companies are estimated using the Hayashi-Yoshida estimator, for each year from 2000 to 2022. We first construct stock pairs, with each pair containing two stocks from the same industrial sector. The median length of the lead lag time for each year shows a general trend of decline over time. From 2000 to 2005, the median lengths are a few seconds. By 2021 and 2022, they are less than 10 milliseconds. We also study a second construct in which stock pairs are randomly formed, but each pair contains stocks from two different sectors. The median length of the lead lag time for each year shows a decline over time, similar to the first construct. Overall, the lengths of the lead lag time in the second construct are not remarkably longer than those in the first construct. This shows that being in the same sector, at the tick-by-tick level, is not an important factor in determining the length of the lead lag time between stocks. (original abstract)
Rocznik
Numer
Strony
49--59
Opis fizyczny
Twórcy
  • California Polytechnic State University, USA
Bibliografia
  • 1. Anderson, B. (2016). Stock price leads and lags before the golden age of high-frequency trading. Applied Economics Letters, 23(3), 212-216. https://doi.org/10.1080/13504851.2015.1066481
  • 2. Ballester, L., & González-Urteaga, A. (2020). Is there a connection between sovereign CDS spreads and the stock market? Evidence for European and US returns and volatilities. Mathematics, 8(10), 1667. https://doi. org/10.3390/math8101667
  • 3. Ben Ameur, H., Jawadi, F., Louhichi, W., & Idi Cheffou, A. (2018). Modeling international stock price comovements with high-frequency data. Macroeconomic Dynamics, 22, 1875-1903. https://doi.org/10.1017/ s1365100516000924
  • 4. Blume, M. E., & Goldstein, M. A. (1997). Quotes, order flow, and price discovery. The Journal of Finance, 52(1), 221-244. https://doi.org/10.1017/s1365100516000924
  • 5. Chakravarty, S., Gulen, H., & Mayhew, S. (2004). Informed trading in stock and option markets. The Journal of Finance, 59, 1235-1257. https://doi.org/10.1111/j.1540-6261.2004.00661.xChakravarty, S., Gulen, H., & Mayhew, S. (2004). Informed trading in stock and option markets. The Journal of Finance, 59, 1235-1257. https://doi.org/10.1111/j.1540-6261.2004.00661.x
  • 6. Chan, K. (1992). A further analysis of the lead-lag relationship between the cash market and stock index futures market. Review of Financial Studies, 5, 123-152. https://doi.org/10.1093/rfs/5.1.123
  • 7. Chen, Y. L., Lee, Y. H., Chou, R. K., & Chang, Y. K. (2021). Arbitrage trading and price discovery of the regular and mini Taiwan stock index futures. Journal of Futures Markets, 41, 1-23. https://doi.org/10.1002/fut.22192
  • 8. Ciaian, P., Rajcaniova, M., & Kancs, D. (2018). Virtual relationships: Short- and long-run evidence from Bitcoin and altcoin markets. Journal of International Financial Markets Institutions & Money, 52, 173-195. https://doi. org/10.1016/j.intfin.2017.11.001
  • 9. Corbet, S., Meegan, A., Larkin, C., Lucey, B., & Yarovaya, L. (2018). Exploring the dynamic relationships between cryptocurrencies and other financial assets. Economics Letters, 165, 28-34. https://doi.org/10.2139/ssrn.3070288
  • 10. Dao, T. M., McGroarty, F., & Urquhart, A. (2018). Ultra-high-frequency lead-lag relationship and information arrival. Quantitative Finance, 18(5), 725-735. https://doi.org/10.1080/14697688.2017.1414484
  • 11. Diebold, F. X., & Yalmaz, K. (2009). Measuring financial asset return and volatility spillovers, with application to global equity markets. The Economic Journal, 119, 158-171. https://doi.org/10.3386/w13811^Google Scholar
  • 12. Easley, D., de Prado, M. M. L., & O'Hara, M. (2012). Flow toxicity and liquidity in a high-frequency world. Review of Financial Studies, 25, 1457-1493. https://doi.org/10.1093/rfs/hhs053
  • 13. Finucane, T. J. (1999). A new measure of the direction and timing of information flow between markets. Journal of Financial Markets, 2, 135-151. https://doi.org/10.1016/s1386-4181(98)00010-x
  • 14. Gonzalo, J., & Granger, C. (1995). Estimation of common long-memory components in cointegrated systems. Journal of Business and Economic Statistics, 13(1), 27-35. https://doi.org/10.1017/ccol052179207x.013
  • 15. Haldane, A. G. (2011). The race to zero [Speech at the International Economic Association Sixteenth World Congress]. Retrieved July 17, 2022, from https://www.bankofengland.co.uk/speech/2011/the-race-to-zerospeech- by-andy-haldane
  • 16. Hasbrouck, J. (1995). One security, many markets: Determining the contributions to price discovery. Journal of Finance, 50, 1175-1199. https://doi.org/10.1111/j.1540-6261.1995.tb04054.x
  • 17. Hasbrouck, J. (2003). Intraday price formation in U.S. equity index markets. Journal of Finance, 58(6), 2375-2399. https://doi.org/10.2139/ssrn.252304
  • 18. Hayashi, T., & Yoshida, N. (2005). On covariance estimation of non-synchronously observed diffusion processes. Bernoulli, 11(2), 359-379. https://doi.org/10.3150/bj/1116340299
  • 19. Hoffmann, M., Rosenbaum, M., & Yoshida, N. (2013). Estimation of the lead-lag parameter from non-synchronous data. Bernoulli, 19(2), 426-461. https://doi.org/10.3150/11-bej407
  • 20. Hou, K. (2007). Industry information diffusion and the lead-lag effect in stock returns. Review of Financial Studies, 20(4), 1113-1138. https://doi.org/10.2139/ssrn.463005
  • 21. Huth, N., & Abergel, F. (2014). High frequency lead-lag relationships - Empirical facts. Journal of Empirical Finance, 26, 41-58. https://doi.org/10.1016/j.jempfin.2014.01.003
  • 22. Ji, Q., Bouri, E., Lau, C. K. M. & Roubaud, D. (2019). Dynamic connectedness and integration in cryptocurrency markets. International Review of Financial Analysis, 63, 257-272. https://doi.org/10.1016/j.irfa.2018.12.002
  • 23. Kawaller, I. G., Koch, P.D., & Koch, T. W. (1987). The temporal price relationship between S&P 500 futures and the S&P 500 index. Journal of Finance, 42, 1309-1329. https://doi.org/10.2307/2328529
  • 24. Koutmos, D. (2018). Return and volatility spillovers among cryptocurrencies. Economics Letters, 173, 122-127. https://doi.org/10.1016/j.econlet.2018.10.004
  • 25. Lucey, B. M., Larkin, C., & O'Connor, F. A. (2013). London or New York: Where and when does the gold price originate? Applied Economics Letters, 20, 813-817. https://doi.org/10.2139/ssrn.2161905
  • 26. Mensi, W., Rehman, M. U., Al-Yahyaee, K. H., Al-Jarrah, I. M. W., & Kang, S. H. (2019). Time frequency analysis of the commonalities between Bitcoin and major cryptocurrencies: Portfolio risk management implications. North American Journal of Economics and Finance, 48, 283-294. https://doi.org/10.1016/j.najef.2019.02.013
  • 27. Mizrach, B., & Neely, C. J. (2008). Information shares in the US Treasury market. Journal of Banking and Finance, 32, 1221 - 1233. https://doi.org/10.20955/wp.2005.070
  • 28. Sapp, S. G. (2002). Price leadership in the spot foreign exchange market. Journal of Financial and Quantitative Analysis, 37, 425-448. https://doi.org/10.2307/3594987
  • 29. Schei, B. N. (2019). High-frequency lead-lag relationships in the Bitcoin market [Copenhagen Business School Thesis]. Retrieved July 17, 2022, from https://kryptografen.no/wp-content/uploads/2019/06/High-Frequency- Lead-Lag-Relationships-in-The-Bitcoin-Market.pdf
  • 30. Scholtus, M., van Dijk, D., & Frijns, B. (2014). Speed, algorithmic trading, and market quality around macroeconomic news announcements. Journal of Banking and Finance, 38, 89-105. https://doi.org/10.2139/ssrn.2174901
  • 31. Tolikas, K. (2018). The lead-lag relation between the stock and the bond markets. The European Journal of Finance, 24(10), 849-866. https://doi.org/10.1080/1351847x.2017.1340320
  • 32. Wang, Z., Bouri, E., Ferreira, P., Shahzad, S. J. H., & Ferrer, R. (2022). A grey-based correlation with multi-scale analysis: S&P 500 VIX and individual VIXs of large US company stocks. Finance Research Letters, 48, 102872. https://doi.org/10.1016/j.frl.2022.102872
  • 33. Xu, L., & Yin, X. K. (2017). Exchange traded funds and stock market volatility. International Review of Finance, 17(4), 525-560. https://doi.org/10.1111/irfi.12121
  • 34. Zhang, L. (2011). Estimating covariation: Epps effect, microstructure noise. Journal of Econometrics, 160(1), 33-47. https://doi.org/10.2139/ssrn.885438
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171682962

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.