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.

scholarly journals Realized Measures to Explain Volatility Changes over Time

2020 ◽  
Vol 13 (6) ◽  
pp. 125
Author(s):  
Christos Floros ◽  
Konstantinos Gkillas ◽  
Christoforos Konstantatos ◽  
Athanasios Tsagkanos
Keyword(s):  
High Frequency ◽  
Realized Volatility ◽  
High Frequency Data ◽  
Leverage Effect ◽  
Frequency Data ◽  
Specific Group ◽  
Volatility Feedback ◽  
Changes Over Time ◽  
The Relationship ◽  
Over Time

We studied (i) the volatility feedback effect, defined as the relationship between contemporaneous returns and the market-based volatility, and (ii) the leverage effect, defined as the relationship between lagged returns and the current market-based volatility. For our analysis, we used daily measures of volatility estimated from high frequency data to explain volatility changes over time for both the S&P500 and FTSE100 indices. The period of analysis spanned from January 2000 to June 2017 incorporating various market phases, such as booms and crashes. Based on the estimated regressions, we found evidence that the returns of S&P500 and FTSE100 indices were well explained by a specific group of realized measure estimators, and the returns negatively affected realized volatility. These results are highly recommended to financial analysts dealing with high frequency data and volatility modelling.

scholarly journals Price duration versus trading volume in high-frequency data for selected DAX companies

2016 ◽  
Vol 17 (2) ◽  
pp. 241
Author(s):  
Henryk Gurgul ◽  
Robert Syrek ◽  
Christoph Mitterer
Keyword(s):  
High Frequency ◽  
Trading Volume ◽  
High Frequency Data ◽  
Frequency Data ◽  
Price Duration

scholarly journals Inference for time-varying lead–lag relationships from ultra-high-frequency data

2021 ◽  
Author(s):  
Yuta Koike
Keyword(s):  
Financial Markets ◽  
High Frequency ◽  
High Frequency Data ◽  
Time Varying ◽  
Frequency Data ◽  
Statistical Methodology ◽  
Microstructure Noise ◽  
New Approach ◽  
Proposed Model ◽  
Derivative Products

AbstractA new approach for modeling lead–lag relationships in high-frequency financial markets is proposed. The model accommodates non-synchronous trading and market microstructure noise as well as intraday variations of lead–lag relationships, which are essential for empirical applications. A simple statistical methodology for analyzing the proposed model is presented, as well. The methodology is illustrated by an empirical study to detect lead–lag relationships between the S&P 500 index and its two derivative products.

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