Abstract
This paper analyses high frequency MTS data to comprehensively evaluate the liquidity of the European sovereign bond markets before and during the European sovereign debt crisis for eleven countries. The Hill index, Generalized Hurst exponent and Dynamic Conditional Score are employed to evaluate the properties of the bid-ask spread. Sovereign bonds exhibit the stylized facts reported for a range of financial markets. The 1-min interval analysis indicates the level of bid-ask spread exhibits long-memory and the change in bid-ask spread experiences volatility clustering. In a dynamic setting, the volatility of bid-ask spread also exhibits long-memory in most European sovereign bond markets across all three maturities. Long-memory effects diminish (disappear) for 5-min (15-min) interval, and for short-term maturity (peripheral countries) is stronger than long-term maturity (core countries). Analysis of sub-periods indicates that long-memory process reached its peak during European sovereign debt crisis from May 2010 to December 2011. This analysis suggests that estimating long-memory parameters for high-frequency data could be a useful tool to monitor market stability.
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Notes
MTS data is the European equivalent of GovPX data in the U.S.
A significant barrier to analyzing high-frequency financial data is access to software which has the capacity to manage large datasets. We thank Kx Systems, Palo Alto, and their European partner, First Derivatives, for providing their KDB+ database management software which was used in this paper to manage the bond market data.
Given our focus on European sovereign debt markets we refer interested readers to the US literature to maintain the clarity and focus of our analysis: Chakravarty and Sarkar (1999), Fleming (2003), Fleming and Remolona (1999), Fleming and Mizrach (2009), Engle et al. (2012), Pasquariello and Vega (2007), Pasquariello and Vega (2012), Goyenko et al. (2011).
Alternative trading platforms include ICAP, EUREX, EBS and D2002.
This depends on particular requirements, i.e. the principal amount outstanding and the available number of dealers and may acquire the Euro “benchmark” status.
This is an exclusive interdealer market composed of large capitalized banks. Individual investors cannot access this market.
See Beirlant et al. (2004) for a detailed explanation of Extreme Value Theory (EVT) and Hill index.
There are other methods to estimate the Hurst index: detrended fluctuation analysis (Ausloos 2000), wavelet transform module maxima (WTMM) method (Percival and Walden 2006), multi-affine analysis (Peng et al. 1994), periodogram regression (Geweke and Porter-Hudak, 1983), the moving-average analysis technique (Ellinger 2000), multi-fractal/multi-affine analysis (Ivanova and Ausloos 1999). In the empirical finance literature, Liu et al. (1997, 1999); Plerou et al. (2005), and Gu et al. (2007) applied detrended fluctuation analysis to investigate the long-range correlation of bid-ask spread in various developed and emerging equity markets. However, most of them suffer from sensitivity and lack of robustness as discussed above.
The average bid-ask spread on the local MTS is slightly smaller than the EuroMTS. Our finding is consistent with Cheung et al. (2005). Caporale and Girardi (2013) find the local trading platforms play a dominant role in price discovery. Therefore, we only show the results for the local platforms in the following sections as they’re qualitatively similar, lead to the same overall conclusions and in the interest of parsimony.
Extreme Value Theory (EVT) statistically deals with the behaviour of the relative extremes, or ‘tails’, of PDFs. Intuitively, fat-tails simply reflect the empirical fact that we observe more frequently extreme observations than would be predicted by the normal distribution, see Beirlant et al. (2004) for more details.
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Youwei Li acknowledges the support of National Natural Science Foundation of China (No. 71571197).
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Appendix: Robustness test of Hurst index estimation
Appendix: Robustness test of Hurst index estimation
Maturity | Test | AU | BEL | GER | SPA | FIN | FRA | GRE | IRE | ITA | NET | POR |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
Panel A: Robustness test in 5 min interval | ||||||||||||
3 Years | H(1) | 0.514 (0.038) | 0.493 (0.032) | 0.453 (0.042) | 0.547 (0.036) | 0.505 (0.035) | 0.461 (0.032) | 0.562 (0.029) | 0.584 (0.045) | 0.518 (0.029) | 0.488 (0.033) | 0.624 (0.025) |
H(2) | 0.333 (0.031) | 0.309 (0.027) | 0.285 (0.034) | 0.385 (0.031) | 0.343 (0.033) | 0.308 (0.026) | 0.289 (0.014) | 0.305 (0.023) | 0.363 (0.030) | 0.317 (0.025) | 0.325 (0.013) | |
6 Years | H(1) | 0.510 (0.033) | 0.481 (0.028) | 0.432 (0.040) | 0.527 (0.032) | 0.528 (0.036) | 0.466 (0.034) | 0.555 (0.007) | 0.550 (0.033) | 0.488 (0.032) | 0.478 (0.033) | 0.604 (0.028) |
H(2) | 0.325 (0.026) | 0.289 (0.021) | 0.276 (0.032) | 0.371 (0.026) | 0.354 (0.034) | 0.302 (0.025) | 0.286 (0.003) | 0.286 (0.018) | 0.342 (0.029) | 0.300 (0.026) | 0.317 (0.015) | |
10 Years | H(1) | 0.477 (0.036) | 0.457 (0.025) | 0.412 (0.037) | 0.503 (0.027) | 0.511 (0.036) | 0.442 (0.031) | 0.518 (0.018) | 0.427 (0.031) | 0.479 (0.034) | 0.447 (0.031) | 0.551 (0.027) |
H(2) | 0.315 (0.031) | 0.279 (0.018) | 0.260 (0.031) | 0.368 (0.023) | 0.343 (0.033) | 0.285 (0.022) | 0.267 (0.008) | 0.216 (0.015) | 0.348 (0.032) | 0.281 (0.025) | 0.285 (0.014) | |
Panel B: Robustness test in 15 min interval | ||||||||||||
3 Years | H(1) | 0.348 (0.043) | 0.346 (0.042) | 0.292 (0.046) | 0.356 (0.052) | 0.347 (0.038) | 0.306 (0.032) | 0.462 (0.020) | 0.402 (0.046) | 0.350 (0.056) | 0.328 (0.045) | 0.477 (0.052) |
H(2) | 0.205 (0.032) | 0.194 (0.029) | 0.168 (0.038) | 0.229 (0.041) | 0.204 (0.032) | 0.181 (0.025) | 0.238 (0.010) | 0.207 (0.024) | 0.222 (0.043) | 0.193 (0.036) | 0.247 (0.028) | |
6 Years | H(1) | 0.378 (0.028) | 0.342 (0.043) | 0.290 (0.032) | 0.346 (0.055) | 0.385 (0.033) | 0.318 (0.031) | 0.493 (0.025) | 0.385 (0.054) | 0.324 (0.046) | 0.317 (0.049) | 0.454 (0.056) |
H(2) | 0.225 (0.019) | 0.193 (0.029) | 0.170 (0.022) | 0.227 (0.046) | 0.225 (0.026) | 0.193 (0.023) | 0.257 (0.013) | 0.196 (0.029) | 0.216 (0.036) | 0.179 (0.038) | 0.235 (0.030) | |
10 Years | H(1) | 0.325 (0.038) | 0.323 (0.041) | 0.280 (0.035) | 0.343 (0.053) | 0.352 (0.040) | 0.304 (0.035) | 0.444 (0.024) | 0.294 (0.018) | 0.296 (0.049) | 0.306 (0.038) | 0.423 (0.058) |
H(2) | 0.190 (0.031) | 0.188 (0.028) | 0.168 (0.027) | 0.243 (0.044) | 0.205 (0.032) | 0.194 (0.026) | 0.232 (0.012) | 0.147 (0.010) | 0.195 (0.038) | 0.177 (0.029) | 0.217 (0.030) | |
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Sun, Z., Hamill, P.A., Li, Y. et al. Did long-memory of liquidity signal the European sovereign debt crisis?. Ann Oper Res 282, 355–377 (2019). https://doi.org/10.1007/s10479-018-2850-y
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DOI: https://doi.org/10.1007/s10479-018-2850-y