Munich Personal RePEc Archive
metadata only
A GARCH-based method for clustering of financial time series: International stock markets evidence
In this paper, we introduce a volatility-based method for clustering analysis of financial time series. Using the generalized autoregressive conditional heteroskedasticity (GARCH) models we estimate the distances between the stock return volatilities. The proposed method uses the volatility behavior
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Modelling and forecasting the volatility of the portuguese stock index PSI-20#1274918
- Modelling and forecasting the volatility of the portuguese stock index PSI-20#1274811
- Clustering of Time Series Data: Measures, Methods, and Applications#492525
- The Spline GARCH Model for Unconditional Volatility and its Global Macroeconomic Causes#942388
- GARCH-Informed Neural Networks for Volatility Prediction in Financial Markets#1115467
Record · ID 1274808
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive