arXiv 3 Feb 2020 · Finance — Statistical Finance · 3 citations (OpenAlex)
arXiv:2002.00724 · PDF · DOI · OpenAlex · Extracted main text
In time-series analysis, the term "lead-lag effect" is used to describe a delayed effect on a given time series caused by another time series. lead-lag effects are ubiquitous in practice and are specifically critical in formulating investment strategies in high-frequency trading. At present, there are three major challenges in analyzing the lead-lag effects. First, in practical applications, not all time series are observed synchronously. Second, the size of the relevant dataset and rate of change of the environment is increasingly faster, and it is becoming more difficult to complete the computation within a particular time limit. Third, some lead-lag effects are time-varying and only last for a short period, and their delay lengths are often affected by external factors. In this paper, we propose NAPLES (Negative And Positive lead-lag EStimator), a new statistical measure that resolves all these problems. Through experiments on artificial and real datasets, we demonstrate that NAPLES has a strong correlation with the actual lead-lag effects, including those triggered by significant macroeconomic announcements.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Takaki Hayashi and Yuta Koike (2018) Wavelet-based methods for high-frequency lead-lag analysis | 0.843 | 3 | 3 | 100% |
| 2 | M. Hoffmann, M. Rosenbaum, and N. Yoshida (2013) Estimation of the lead-lag parameter from non-synchronous data | 0.737 | 3 | 2 | 100% |
| 3 | Kohei Chiba (2019) Estimation of the leadlag parameter between two stochastic processes driven by fractional brownian motions | 0.644 | 2 | 2 | 100% |
| 4 | Takaki Hayashi and Yuta Koike (2017) Multi-scale analysis of lead-lag relationships in high-frequency financial markets, 2017 | 0.644 | 2 | 2 | 100% |
| 5 | Takaki Hayashi and Nakahiro Yoshida (2005) On covariance estimation of non-synchronously observed diffusion processes | 0.585 | 3 | 1 | 100% |
| 6 | Dobrislav Dobreva and Ernst Schaumburgb (2017) High-frequency cross-market trading: Model free measurement and applications, 2017 | 0.511 | 2 | 1 | 100% |
| 7 | Hamad Alsayed and Frank McGroarty (2014) Ultra-high-frequency algorithmic arbitrage across international index futures | 0.405 | 1 | 1 | 100% |
| 8 | Bhaskkar Sinha and Sumati Sharma (2008) Lead - lag relationship in indian stock market: Empirical evidence | 0.405 | 1 | 1 | 100% |
| 9 | Bruno Biais, Thierry Foucault, and Sophie Moinas (2015) Equilibrium fast trading | 0.405 | 1 | 1 | 100% |
| 10 | Nicolas P.B. Bollen, Michael J. O'Neill, and Robert E. Whaley (2016) Tail wags dog: Intraday price discovery in VIX markets | 0.405 | 1 | 1 | 100% |
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