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.

Connectedness between the Crude Oil Futures and Equity Markets During the Pre- and Post-Financialisation Eras

“…Lucey and Pardo (2005) show various seasonal effects in financial markets such as the value effect, the size effect, the holiday effect, the weekend effect, the momentum effect, the dividend yield effect, and the weather effect, among others. Recently, Alemany, Aragó, and Salvador (2019) assess intraday seasonality on volatility transmission between stock indexes and show that if seasonality is neglected, the model may lose important information on volatility transmission.…”

The distribution of index futures realised volatility under seasonality and microstructure noise

“…The estimate of ω 2 is obtained as follows. By varying the starting point, we obtain k distinct auxiliar realised variances, defined as RV (1) aux , RV (2) aux ...RV…”

“…However, the increasing availability of high-frequency data (HFD) has produced an explosive growth in the financial econometrics of volatility dynamics, allowing for the construction of more accurate daily volatility measures: realised volatility (RV). 1 Additionally, as volatility becomes observable, it can be modelled directly, rather than being treated as a latent variable. Therefore, we can model and forecast it using standard time-series techniques (Andersen et al 2001(Andersen et al , 2003.…”

“…The frequency of observations is critical, because it may affect the accuracy of estimates. Thus, we use different frequencies (1,5,10,20, and 30 minutes). As noted below, the early literature focuses on one frequency only.…”

“…Andersen and Bollerslev (1997a) provide a stylised intraday return model based on the Fourier flexible methodology to uncover intraday seasonality. 3 This enables us to obtain deseasonalised intraday returns and, there- 1 RV, constructed as a sum of squared intraday returns, has theoretical advantages in the construction of interdaily volatility forecast evaluation criteria (Andersen and Bollerslev, 1998a;Andersen et al, 2001b). 2 The theory of quadratic variation suggests that under certain conditions (basically, frequencies that tend to infinity and markets free of frictions (Merton, 1980;Andersen and Bollerslev, 1998a;Andersen et al, 2001c), RV is an unbiased and efficient estimation of return volatility; however, these circumstances are rarely met in practice.…”