Abstracting Steady Qualitative Descriptions over Time from Noisy, High-Frequency Data

1999 ◽  
pp. 281-290 ◽  
Author(s):  
Silvia Miksch ◽  
Andreas Seyfang ◽  
Werner Horn ◽  
Christian Popow
Keyword(s):  
High Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Over Time

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 Beauty and Ugliness of Aggregation over Time: A Survey

2017 ◽  
Vol 68 (3) ◽  
Author(s):  
Nlandu Mamingi
Keyword(s):  
Public Policy ◽  
Time Series ◽  
High Frequency ◽  
Low Frequency ◽  
High Frequency Data ◽  
Frequency Data ◽  
Economic Time Series ◽  
Negative Effects ◽  
Economic Time ◽  
Over Time

AbstractThis paper delivers an up-to-date literature review dealing with aggregation over time of economic time series, e.g. the transformation of high-frequency data to low frequency data, with a focus on its benefits (the beauty) and its costs (the ugliness). While there are some benefits associated with aggregating data over time, the negative effects are numerous. Aggregation over time is shown to have implications for inferences, public policy and forecasting.

The use of SHM to monitor and evaluate bridge movements – absolute and accumulated – at the structure’s bearings and expansion joints

2021 ◽  
Author(s):  
Niculin Meng ◽  
Thomas Richli ◽  
Colm O’Suilleabhain
Keyword(s):  
Health Monitoring ◽  
High Frequency ◽  
Continuous Monitoring ◽  
Early Stage ◽  
Cycle Performance ◽  
High Frequency Data ◽  
Frequency Data ◽  
Expansion Joints ◽  
Potential Problems ◽  
Over Time

<p>The movements a bridge experiences, both absolute and accumulated over time, can significantly influence the structure’s life-cycle performance – especially as it relates to the components that facilitate these movements. Structural health monitoring (SHM) systems, with sensors placed at – or ideally, integrated in – a bridge’s bearings and expansion joints, can be used to efficiently record and evaluate these movements, facilitating continuous monitoring of the components’ and the structure’s performance over time. This can enable potential problems to be recognised at an early stage, and maintenance (e.g. replacement of “wear parts” such as sliding materials) to be optimised. The significance of the frequency at which measurements are recorded must be appreciated, as high-frequency data can capture micro-movements (e.g. due to wind or traffic) that far exceed the slow thermal movements. This paper explores this topic with reference to a number of case studies.</p>

Forecasting Exchange Rate Realized Volatility Through Decomposition Using High-Frequency Data

2017 ◽  
Author(s):  
Rim mname Lamouchi ◽  
Russell mname Davidson ◽  
Ibrahim mname Fatnassi ◽  
Abderazak Ben mname Maatoug
Keyword(s):  
Exchange Rate ◽  
High Frequency ◽  
Realized Volatility ◽  
High Frequency Data ◽  
Frequency Data
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