The Volatility Forecasting Power of Financial Network Analysis

Abstract

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.

The predictive power of stock market’s expectations volatility: A financial synchronization phenomenon

“…Useful network methods for study markets´ behavior are the Minimum Spanning Tree (MST) and the Planar Maximally Filtered Graph (PMFG). With these techniques, it is possible to build a connected network of financial assets to identify topological features related to the emergence of returns synchronization in stock markets [ 4 ]. For example, evidence indicates that during synchronization of returns or collective behavior–where financial assets exhibit a similar tendency, the asset´s network displays a change in their topology related to the “small–world” property of Watts and Strogatz [ 5 ].…”

“…al., [ 7 ] use the MST to categorize the Chinese stock market in central and peripheral stocks, finding that the network’s peripheral ones, being less synchronized with the rest of the market stocks, offer a similar return but with lower levels of risk, making them more attractive to increase portfolio diversification. From a systemic perspective, Magner et al [ 4 ] use the length of the MST (MSTL) and the correlation network, to represent the temporal dynamics of the synchronization phenomenon of regional stock markets of America, Europa, Asia, and Oceania, and study how this dynamic has predictive power on the realized volatility of the stock indices of the main exchanges of the world. Their results provide practical implications for the investment management industry and for the regulator´s viewpoint.…”

Cryptocurrency Forecasting: More Evidence of the Meese-Rogoff Puzzle

“…A possible explanation for this predictive ability is synchronization. (See [51] for an example of synchronization between stock indices.) Moreover, [47,52] pointed out that an interesting feature of the cryptocurrency market is the interconnection between BITCOIN and the rest of crypto assets.…”

Stock Market Synchronization and Stock Volatility: The Case of an Emerging Market

“…Assets' synchronization also affects market volatility. Magner, Lavin, Valle & Hardy (2020) show that the global network of correlations in international financial markets has predictive power on stock indices' volatility. In such a case, an increase in the synchronization of assets -increased correlations between the indices' returns -is a predictor of increased market volatility in North America and Europe and Latin America and Asia.…”