Inference for Time-Varying Lead-Lag Relationships from Ultra High Frequency Data

2017 ◽  
Author(s):  
Yuta Koike
Keyword(s):  
High Frequency ◽  
High Frequency Data ◽  
Time Varying ◽  
Frequency Data ◽  
Ultra High Frequency

scholarly journals Multiple STL decomposition in discovering a multi-seasonality of intraday trading volume

2021 ◽  
Vol 12 (1) ◽  
pp. 61-74
Author(s):  
Josip Arneric
Keyword(s):  
High Frequency ◽  
Trading Volume ◽  
Missing Values ◽  
High Frequency Data ◽  
Seasonal Patterns ◽  
Time Varying ◽  
Frequency Data ◽  
Seasonal Component ◽  
Univariate Time Series ◽  
Over Time

The seasonal and trend decomposition of a univariate time-series based on Loess (STL) has several advantages over traditional methods. It deals with any periodicity length, enables seasonality change over time, allows missing values, and is robust to outliers. However, it does not handle trading day variation by default. This study offers how to deal with this drawback. By applying multiple STL decompositions of 15-minute trading volume observations, three seasonal patterns were discovered: hourly, daily, and monthly. The research objective was not only to discover if multi-seasonality exists in trading volume by employing high-frequency data but also to determine which seasonal component is most time-varying, and which seasonal components are the strongest or weakest when comparing the variation in the magnitude between them. The results indicate that hourly seasonality is the strongest, while daily seasonality changes the most. A better understanding of trading volume multiple patterns can be very helpful in improving the performance of trading algorithms.

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