High frequency lead/lag relationships — Empirical facts

Abstract

Lead/lag relationships are an important stylized fact at high frequency. Some assets follow the path of others with a small time lag. We provide indicators to measure this phenomenon using tick-by-tick data. Strongly asymmetric cross-correlation functions are empirically observed, especially in the future/stock case. We confirm the intuition that the most liquid assets (short intertrade duration, narrow bid/ask spread, small volatility, high turnover) tend to lead smaller stocks. However, the most correlated stocks are those with similar levels of liquidity. This lead/lag phenomenon is not constant throughout the day, it shows an intraday seasonality with changes of behaviour at very specific times such as the announcement of macroeconomic figures and the US market opening. These lead/lag relationships become more and more pronounced as we zoom on significant events. We reach 60% of accuracy when forecasting the next midquote variation of the lagger using only the past information of the leader, which is significantly better than using the information of the lagger only. However, a naive strategy based on market orders cannot make any profit of this effect because of the bid/ask spread.

The Lead-Lag Relationship among East Asian Economies: A Wavelet Analysis

“…They also investigated the enlarged correlation matrix obtained from original and lagged indices and examine a network structure derived from it, thus showing connections between lagged and original indices that could not be well represented before. Huth and Abergel (2014) confirmed that the intuition that the most liquid assets (short inter-trade duration, narrow bid/ask spread, small volatility, high turnover) tend to lead smaller stocks. These lead/lag relationships become more and more pronounced as we zoom on significant events.…”

Tail Wags Dog: Intraday Price Discovery in VIX Markets

“…Hayashi and Yoshida (HY) discuss the problem of non-synchronous trading in terms of the errors that can arise when randomly spaced observations like trade arrivals are sampled at regular time frequencies. Huth and Abergel (2014) and Hoffmann, Rosenbaum, and Yoshida (2013) use the lagged version of the HY estimator to devise a lead/lag ratio where the lags may be dependent and assess whether one variable leads or lags another. In contrast, the HY cross-correlation estimator makes use of all data, regardless of the time intervals between samples, and thereby deals with the issues of non-synchronous trading and spurious correlations without a need to rely on sampling.…”

Ultra‐High‐Frequency Algorithmic Arbitrage Across International Index Futures

“…This implies that the pairwise causal link between the leading and lagging contracts deteriorates throughout the day (Huth and Abergel, 2014). This implies that the pairwise causal link between the leading and lagging contracts deteriorates throughout the day (Huth and Abergel, 2014).…”

“…7 A similar procedure is performed in Huth and Abergel (2014). If one monitors changes in the mid-quote, the first scenario will always yield a non-zero return, while the second scenario will always yield a zero return.…”

“…Our forecasting and trading devices are motivated by Huth and Abergel (2014) and Kozhan and Tham (2012). The trading strategy Knowledge of the temporal relationship between a pair of contracts can be used to build statistical arbitrage strategies based on exploiting the temporal disequilibria.…”