Paper Info
Title
Clustering High-frequency Stock Data for Trading Volatility Analysis
Abstract
This paper proposes a Realized Trading Volatility (RTV) model for dynamically monitoring anomalous volatility in stock trading. Specifically, the RTV model first extracts the sequences for price volatility, volume volatility, and realized trading volatility. Then, the K-means algorithm is exploited for clustering the summary data of different stocks. The RTV model investigates the joint-volatility between share price and trading volume, and has the advantage of capturing anomalous trading volatility in a dynamic fashion. As a case study, we apply the RTV model for the analysis of real-world high-frequency stock data. For the resultant clusters, we focus on the categories with large volatility and study their statistical properties. Finally, we provide some empirical insights for the use of the RTV model.
Year
DOI
Venue
2010
10.1109/ICMLA.2010.56
ICMLA
Keywords
Field
DocType
realized trading volatility model,rtv model,summary data clustering,pattern clustering,price volatility,anomalous trading volatility,large volatility,volume volatility,realized trading volatility,trading volume,real-world high-frequency stock data,anomalous volatility,case study,trading volatility,clustering analysis,anomalous stock trading volatility dynamic monitoring,trading volatility analysis,high-frequency stock data clustering,clustering high-frequency stock data,k-means algorithm,stock trading,pricing,stock control,k means algorithm,clustering algorithms,cluster analysis,high frequency,data models,solid modeling
Econometrics,Stochastic volatility,Implied volatility,Volatility swap,Computer science,Share price,Volatility smile,Artificial intelligence,Forward volatility,Volatility (finance),Volatility risk premium,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-4244-9211-4
1
0.36
References 
Authors
0
4
Name
Order
Citations
PageRank
Xiao-Wei Ai121.09
Tianming Hu216219.81
Xi Li32212.44
Hui Xiong44958290.62