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Financial crises, price discovery, and information transmission: a high-frequency perspective

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Abstract

This paper examines the price discovery processes before and during the 2007–2009 subprime and financial crisis, as well as the subsequent European sovereign crisis, for American and German stock and bond markets, as well as for U.S. Dollar/Euro FX. Based on 5-s intervals, we analyze how asset prices interact conditional on macroeconomic announcements from the USA and Germany. Our results show significant co-movement and spillover effects in returns and volatility, reflecting systematic information transmission mechanisms among asset markets. We document strong state dependence with a substantial increase in inter-asset spillovers and feedback effects during times of crisis.

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Notes

  1. Ederington and Lee (1993) document that most of the price adjustments to major announcements occur within the first minute. See also Christensen et al. (2011) for a literature overview on sampling intervals over the last four decades.

  2. We refrained from using quotes mainly for two reasons. First, in our sample, we usually see only low liquidity at the top levels of the bid and ask side. This concern was further exacerbated by the upcoming discussion on order spoofing and other manipulation attempts. We therefore decided to use transaction data. Secondly, we find substantial differences in the market interactions for our three different subsamples, but all three subsamples should be affected similarly by bounces in transaction prices between trades at the bid and ask quote (negative autocorrelation).

  3. To convert the irregularly spaced tick-by-tick prices into time series with fixed time intervals, we take the last transaction price within the interval [t, t+1] as the price, \(P_t\).

  4. We remove the opening and closing prices resulting from the respective auctions at 8:00 and 22:00 when the German futures markets open and close. When the underlying markets open at 9:00, we find a spike in the futures markets, showing that there is a daily volatility pattern associated with stock market opening and closing.

  5. We also find significant negative first-order autocorrelations in asset returns with higher-order autocorrelations around or within the 95% confidence band. The volatilities of all five markets follow a long-memory process.

  6. We also analyzed announcement data from the European Monetary Union (EMU). However, we find that aggregate EMU news is generally insignificant, with the exception of a minor impact on the USD/EUR exchange rate. One explanation for the insignificant effect of macroeconomic data from the EMU lies in the non-revision of forecasts. Only some survey participants revise their reported forecasts for the EMU after announcements from single countries, but most do not, which leads to a biased measurement of surprises.

  7. In total we use 18 different macroeconomic indicators from the US and Germany. For all US announcements we have 72 observations except for initial unemployment rate with 313 observations. For Germany the number varies between 61 and 71. For German GDP we only have 23 observations.

  8. It is important to note that only those events that coincide with announcements are listed. For example, the Lehman Brothers collapse took place when no macroeconomic news was released.

  9. We do not apply a minimum threshold as a deviation from the expectation (as, e.g., in Hanousek and Kocenda 2011), as this would only eliminate very few observations.

  10. Chaboud et al. (2010) use a USD/EUR dataset from ICAP and demonstrate that 15- to 20-s intervals can be used without contaminating the realized variance with market microstructure noise. For a simple realized kernel estimator, they find no noticeable bias, even for 2- to 5-s intervals. They show that sampling intervals may be even shorter for days when US macroeconomic data are released.

  11. We compare the realized variance with the realized kernel for DAX and Bund futures and for the USD/EUR exchange rate for different sampling frequencies. The realized kernels converge to a stable level at a frequency of roughly 1 min, while realized volatility approaches true volatility at a 20-min frequency. The volatilities of E-mini S&P500 and 10-year Treasury note futures tend to be underestimated using realized kernels at lower frequencies. Realized kernels based on 5-s to 1-min intervals are closer to the true volatilities. We also use Bandi and Russel’s (2005) MSE approach to calculate the optimal time interval for realized volatility, which confirms the results from the signature plot (i.e., an optimal interval of about 20 min).

  12. We also extend the time window to one and a half hours after the announcements. However, the results do not change materially. Furthermore, a 1-h time window is sufficient to capture the volatility response.

  13. Note that \({\hat{\sigma }}_{d(t)}\) is the daily unconditional volatility based on a realized kernel, whereas \(|\varepsilon _t |\) is the conditional (mean) volatility on a 1-min sampling frequency. Related to the unbiased estimate based on realized kernels, the conditional volatility \(|\varepsilon _t |\) converges toward the 1-min adjusted unconditional volatility \({\hat{\sigma }}_{d(t)}\) in the long term. Under this assumption, the 1-min adjusted expected value of \({\hat{\sigma }}_{d(t)}\) equals the expected value of \(|\varepsilon _t |\).

  14. Because of its periodic patterns (see Fig. 1), we further filter the USD/EUR exchange rate volatility by using a flexible Fourier form (FFF), \(\sum \nolimits _{q=1}^{Q}\left( \varphi _{q}\ \cos (\frac{q2\pi t}{720})+\eta _{q}\ \sin \left( \frac{q2\pi t}{720}\right) \right) \) as in Andersen et al. (2003b). We also tested other ARFIMA(p,d,q) model specifications. However, we found that the selected filter is most suitable for whitening the volatility process.

  15. In order to keep the number of lags consistent for all five OLS regressions, we choose the lag length based on a VAR model. We also test specific lag lengths for each single OLS regression model. However, the lag lengths vary only slightly and the results are qualitatively the same.

  16. Note that we use \(\xi _t\) from the ARFIMA process as the filtered volatility, and not the absolute values, because the absolute returns, \(|R_t|\), already represent the volatility process. Thus, taking the absolute values of the residuals derived from the ARFIMA process would reflect the filtered volatility of the return volatility process. From the filtering process, we necessarily obtain positive and negative values; however, the negative deviations are not significantly different from zero.

  17. We also tried to include first and second leads in the model, but we found no pre-announcement impact on returns. The choice of minutes around the news releases shown in Figure  was done on the basis of significant price changes in the data, with effects mainly observed in the − 3 to \(+\) 8 min window around announcements.

  18. Table C1 in the Internet Appendix shows the results for the whole sample and for all macroeconomic news.

  19. We also added a variable for standardized trading volume in our return equation in order to capture liquidity effects on the volatility. However, the coefficients are economically small.

  20. We define the cumulative impact on return as the sum of continuous significant coefficients (as long as they do not involve a sign change). The accumulation of return responses begins from the first significant coefficient, and ceases when an insignificant coefficient or changing sign is observed. We impose this relatively conservative measure to avoid overestimation or amplification of the cumulative responses.

  21. We also examine the volatility response for 10 min before the news release. We find slight increases in volatility for some important economic indicators. However, the magnitude is very small, and there is no distinctive pattern.

  22. The polynomial response functions of Eqs. (5a) and (5b) replace the coefficients of news dummies in Eq. (4). The average intraday volatility accounts for most of the variation. Other factors (news dummies and average intraday volatility, as well as calendar, event, crisis, and day-of-the-week dummies) that influence volatilities are also incorporated into the volatility equation as discussed in the previous section. Thus, the polynomial responses capture only the macroeconomic news effects.

  23. By definition, positive US news has a negative impact on the USD/EUR exchange rate, while positive German news affects FX positively. Therefore, we observe opposite signs of conditional correlation coefficients between USD/EUR exchange rate and any other market in response to US and German news, e.g., \(\rho _{USD/EUR,Bund\,\,Futures}^{US;\,\,Pre-crisis}=0.479\) and \(\rho _{USD/EUR,Bund\,\,Futures}^{German;\,\,Pre-crisis}=-\,0.303\).

  24. We also calculated the Bravais-Pearson correlation coefficient and a realized volatility-based coefficient. Both qualitatively confirm our realized kernel-based results.

  25. To conserve space we do not report the full tables with the estimated coefficients here; however, they are shown in Table E1 in the Internet Appendix.

  26. We also run a placebo test by choosing days without announcements. Unsurprisingly, the robustness checks for selected days reported in Table D1 in the Internet Appendix show substantially lower estimates. This finding supports the assumption that information transmissions predominantly occur at the time of a news release.

  27. We used the same days without announcements that were used for the placebo test for the return spillovers (see footnote 26). The indicative robustness checks reported in Table D2 show again much smaller interaction coefficients.

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Acknowledgements

We are grateful to the anonymous referee as well as Yakov Amihud, Jean-Noël Barrot, Adam Clements, Alfonso Dufour, Falko Fecht, Reint Gropp, Ferenc Horvath, Olga Lebedeva, Bonnie F. Van Ness, Rico von Wyss, and the participants at the Brown Bag Seminar at the EBS Business School, the Eastern Finance Association annual meeting 2012, the Midwest Finance Association 2012 annual meeting, the 2012 FMA European Conference, the SGF Conference 2016, the IFABS 2016 Conference in Barcelona, the 4th Paris Financial Management Conference (PFMC) 2016, the 14th EUROFIDAI/AFFI/ESSEC Paris December Finance Meeting, and the Bundesbank Project Group on “Big Data” Workshop. We also thank the Deutsche Börse for providing the data, especially Axel Schorn and Holger Wohlenberg for MNI data about macroeconomic announcements.

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Authors and Affiliations

  1. School of Finance, Swiss Institute of Banking and Finance (s/bf), University of St. Gallen, Unterer Graben 21, 9000, St. Gallen, Switzerland

    Roland Füss

  2. Centre for European Economic Research (ZEW), Mannheim, Germany

    Roland Füss

  3. Center for Real Estate and Environmental Economics, NTNU Business School, Trondheim, Norway

    Roland Füss

  4. Department of Finance, Accounting and Real Estate, EBS Business School, EBS Universität für Wirtschaft und Recht, Gustav-Stresemann-Ring 3, 65189, Wiesbaden, Germany

    Ferdinand Mager

  5. Department of Financial Market Econometrics, Faculty for Economics and Business Administration, University of Duisburg-Essen, Universitaetsstr. 12, 45117, Essen, Germany

    Michael Stein

  6. IQ-KAP (The Private Institute for Quantitative Capital Market Research at the DekaBank) and Deka Investment GmbH, Mainzer Landstrasse 16, 60325, Frankfurt am Main, Germany

    Michael Stein

  7. Department of Financial Engineering, School of Finance, Southwestern University of Finance and Economics, Liutai Avenue 555, Wenjiang District, Chengdu, 611130, Sichuan, People’s Republic of China

    Lu Zhao

  8. Stockholm Business School, Stockholm University, 10691, Stockholm, Sweden

    Lu Zhao

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Correspondence to Roland Füss.

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Füss, R., Mager, F., Stein, M. et al. Financial crises, price discovery, and information transmission: a high-frequency perspective. Financ Mark Portf Manag 32, 333–365 (2018). https://doi.org/10.1007/s11408-018-0318-3

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