The Volatility Forecasting Power of Financial Network Analysis
This investigation connects two crucial economic and financial fields, financial networks, and forecasting. From the financial network’s perspective, it is possible to enhance forecasting tools, since econometrics does not incorporate into standard economic models, second-order effects, nonlineariti...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/7051402 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849307913057730560 |
|---|---|
| author | Nicolás S. Magner Jaime F. Lavin Mauricio A. Valle Nicolás Hardy |
| author_facet | Nicolás S. Magner Jaime F. Lavin Mauricio A. Valle Nicolás Hardy |
| author_sort | Nicolás S. Magner |
| collection | DOAJ |
| description | This investigation connects two crucial economic and financial fields, financial networks, and forecasting. From the financial network’s perspective, it is possible to enhance forecasting tools, since econometrics does not incorporate into standard economic models, second-order effects, nonlinearities, and systemic structural factors. Using daily returns from July 2001 to September 2019, we used minimum spanning tree and planar maximally filtered graph techniques to forecast the stock market realized volatility of 26 countries. We test the predictive power of our core models versus forecasting benchmarks models in and out of the sample. Our results show that the length of the minimum spanning tree is relevant to forecast volatility in European and Asian stock markets, improving forecasting models’ performance. As a new contribution, the evidence from this work establishes a road map to deepening the understanding of how financial networks can improve the quality of prediction of financial variables, being the latter, a crucial factor during financial shocks, where uncertainty and volatility skyrocket. |
| format | Article |
| id | doaj-art-1d72071b35d74a63b9cf1bcf7f3263a3 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-1d72071b35d74a63b9cf1bcf7f3263a32025-08-20T03:54:37ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/70514027051402The Volatility Forecasting Power of Financial Network AnalysisNicolás S. Magner0Jaime F. Lavin1Mauricio A. Valle2Nicolás Hardy3Facultad de Economía y Negocios, Universidad Finis Terrae, Pedro de Valdivia 1509, Providencia, Santiago 7501015, ChileEscuela de Negocios, Universidad Adolfo Ibáñez, Diagonal Las Torres 2640, Peñalolén, Santiago 7941169, ChileFacultad de Economía y Negocios, Universidad Finis Terrae, Pedro de Valdivia 1509, Providencia, Santiago 7501015, ChileFacultad de Economía y Negocios, Universidad Finis Terrae, Pedro de Valdivia 1509, Providencia, Santiago 7501015, ChileThis investigation connects two crucial economic and financial fields, financial networks, and forecasting. From the financial network’s perspective, it is possible to enhance forecasting tools, since econometrics does not incorporate into standard economic models, second-order effects, nonlinearities, and systemic structural factors. Using daily returns from July 2001 to September 2019, we used minimum spanning tree and planar maximally filtered graph techniques to forecast the stock market realized volatility of 26 countries. We test the predictive power of our core models versus forecasting benchmarks models in and out of the sample. Our results show that the length of the minimum spanning tree is relevant to forecast volatility in European and Asian stock markets, improving forecasting models’ performance. As a new contribution, the evidence from this work establishes a road map to deepening the understanding of how financial networks can improve the quality of prediction of financial variables, being the latter, a crucial factor during financial shocks, where uncertainty and volatility skyrocket.http://dx.doi.org/10.1155/2020/7051402 |
| spellingShingle | Nicolás S. Magner Jaime F. Lavin Mauricio A. Valle Nicolás Hardy The Volatility Forecasting Power of Financial Network Analysis Complexity |
| title | The Volatility Forecasting Power of Financial Network Analysis |
| title_full | The Volatility Forecasting Power of Financial Network Analysis |
| title_fullStr | The Volatility Forecasting Power of Financial Network Analysis |
| title_full_unstemmed | The Volatility Forecasting Power of Financial Network Analysis |
| title_short | The Volatility Forecasting Power of Financial Network Analysis |
| title_sort | volatility forecasting power of financial network analysis |
| url | http://dx.doi.org/10.1155/2020/7051402 |
| work_keys_str_mv | AT nicolassmagner thevolatilityforecastingpoweroffinancialnetworkanalysis AT jaimeflavin thevolatilityforecastingpoweroffinancialnetworkanalysis AT mauricioavalle thevolatilityforecastingpoweroffinancialnetworkanalysis AT nicolashardy thevolatilityforecastingpoweroffinancialnetworkanalysis AT nicolassmagner volatilityforecastingpoweroffinancialnetworkanalysis AT jaimeflavin volatilityforecastingpoweroffinancialnetworkanalysis AT mauricioavalle volatilityforecastingpoweroffinancialnetworkanalysis AT nicolashardy volatilityforecastingpoweroffinancialnetworkanalysis |