The influence of intraday seasonality on volatility transmission pattern

Abstract

Using data on a five-minute interval basis, this article analyses the effects of intraday seasonality on volatility transmission between the spot and futures markets of the CAC40, DAX30 and FTSE100. Remarkable differences in the impulse response analysis and in the dynamic and directional measurement of volatility spillovers are encountered depending on whether the intraday periodic component is considered. Thus, the convenience of removing intraday seasonality seems to be critical to reduce the risk of spurious causality when employing high-frequency data in volatility transmission. Moreover, the impact of market microstructure noise seems negligible when using an optimal frequency of observations.

Keywords:

• High-frequency data
• Intraday periodic component
• Fourier flexible form
• Realized volatility
• Volatility spillover
• Microstructure noise

ORCID

N. Alemany http://orcid.org/0000-0002-7843-2570

V. Aragó http://orcid.org/0000-0002-2679-9223

E. Salvador http://orcid.org/0000-0001-6352-3210

Notes

1 Intraday volatility is often proxied by the average absolute returns.

2 Some markets exhibit a double U-shape pattern, one in the morning and the other one in the afternoon (Andersen et al. 2000, Harju and Hussain 2011).

3 Inference procedures implemented using high-frequency returns should consider, as noted by Andersen (2000), ‘The strong daily periodicity and the longer-run slow decay in the serial dependence’.

4 Based on the aforementioned idea that a suitable return frequency is more important than the bias correction methodology, we rely on observations on a five-minute interval basis to do the analysis without handling the market microstructure noise. Additionally, as robustness checks of our findings, we also analyze the extent to which market microstructure noise affects results.

5 Ross (1989) proves that in an arbitrage free economy, changes in conditional variances are directly related to the rate at which information flows to the market. Following this idea, one method of analysing how information flows between two assets is by examining their volatility relationships.

6 Soriano and Climent (2006) review the literature on volatility transmission and provide a broad vision of the state of the art on this topic.

7 For those readers interested in a comprehensive and detailed explanation about the kernel estimator see Barndorff-Nielsen et al. (2011).

8 The intervals corresponding to the opening of the US markets and the announcement of US macro news are the ones controlled by these dummies.

9 We employ the widely used parametric GARCH (1, 1) model to capture daily volatility. In most empirical applications, the GARCH (1, 1) is enough to reproduce the volatility dynamics of financial series, and thus the GARCH (1, 1) is the ‘workhorse’ model for both academics and practitioners (Engle 2001).

10 To our knowledge, this is the first study to examine the day of the week (DOW) effect and the expiration and maturity effects on volatility by means of the FFF.

11 Additionally, the day before expiration and the week before expiration have also been considered in our analysis. The results are available upon request.

12 For further details see Andersen and Bollerslev 1998, Andersen et al. 2001, 2003, Barndorff-Nielsen and Shephard 2002, McAleer and Medeiros 2008.

13 A more extensive theoretical explanation can be found in Hansen et al. 2012, 2014.

14 This leaves us with a sample of 3055 trading days for CAC40, 3070 trading days for DAX30 and 2982 days for FTSE100, each day consisting of 101 intraday returns.

15 To keep this article to a reasonable length the intraday average returns plot is not attached to this document. It is available upon request.

16 For the FTSE100 index this pattern occurs an hour earlier due to the different time zones.

17 Such as, Producer Price Index, Retail Sales, Consumer Price Index, Consumer Confidence, etc.

18 Concretely, volatility is remarkably higher at intervals 14:35–14:40 and 16:05 for CAC40 and DAX30, and at intervals 13:35–13:40 and 15:05–15:10 for FTSE (keep in mind that for the FTSE100 this pattern occurs an hour earlier due to different time zones).

19 To keep this article to a reasonable length, results are not attached to this article, but they are available upon request.

20 The optimal lag length for each bivariate VAR model has been set by means of the AIC/BIC criteria.

21 The Generalized impulse response function by Pesaran and Shin (1998) is applied.

22 It represents an 11 percentage increase in volatility approximately.

23 Meneu and Torró (2003) study the volatility transmission between the IBEX 35 Index and IBEX 35 Futures Index using daily data and extend their analysis implementing an impulse response analysis. They find evidence that shocks take a very long time to vanish (about 100 days).

24 The percentage increase in volatility is about 80, 60 and 200 for CAC40, DAX30 and FTSE indexes respectively.

25 Volatility increases about 10, 9 and 7 percent for CAC40, DAX30 and FTSE100 indexes respectively, after a shock hits the system.

26 When a shock hits the system, volatility increases 70, 60 and 90 percentage points for CAC40, DAX30 and FTSE100 indexes respectively.

27 For more details about this methodology, see Diebold and Yilmaz (2009, 2012).

28 We follow Diebold and Yilmaz (2012) and use generalized variance decompositions of 10-day ahead volatility forecast errors and estimate the time-varying volatility spillovers using a 200-day rolling sample framework. Additionally, the optimal lag length for each bivariate VAR model has been set by means of the AIC/BIC criteria. Note that even though the Diebold and Yilmaz (2012) results are based on vector autorregressions of order 4, these authors report that the total spillover plot is sensitive neither to the lag order of the VAR nor the choice of forecast horizon.

29 The net spillover for the spot market is calculated as a positive value, indicating that the spot market transmits spillovers to the futures market. On the contrary, when the net spillover has a negative value, the spot market receives spillovers from the futures market.

30 All cases involved except one, support this finding.

31 Results are available upon request.

32 As obtained in section 5.3, all cases involved except one, the VAR C in the FTSE100 index, support this finding.

Additional information

Funding

This work was supported by Spanish Ministerio de Economía y Competitividad [grant number Project ECO2014/55221- P, ECO 2017/85746-P]; Universitat Jaume I [grant number Project UJI-B2017-14, Research Personal Program PRE- DOC/2014/14].