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Title: Inferring the global financial network from high-dimensional time-series of stock returns
Author: Tungsong, Sachapon
ISNI:       0000 0004 7660 7431
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2018
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Connectedness in a financial network refers to the structure of interlinkages among the financial institutions which encompasses three aspects: which institutions are linked, how many of the institutions are linked, and the magnitude of the linkages. This research measures time-varying connectedness in the global financial network using the following two frameworks: (1) vector autoregression-forecast error variance decomposition (VAR-FEVD) and (2) information filtering network-based algorithm LoGo-TMFG. In the first framework we construct a full connectedness network where each of the financial institutions is linked to the others using VAR. On the contrary, in the second framework we construct a sparse connectedness network where only significant links are kept and insignificant links are put to zeros using LoGo- TMFG, which is a novel sparse modeling algorithm. We show that both frameworks reveal strong variations of connectedness during past crises, but the connectedness measure computed on the sparse network can distinguish major crises better than that computed on the full network. This suggests that sparse modeling using the LoGo-TMFG algorithm increases the signal-to-noise ratio in the data and improves interpretability of the connectedness measure, which leads to better statistical inference of the result. In the first framework we analyze bank returns in North America, the European Union, and Southeast Asia from 2005 to 2016. We find that the North American system has the highest connectedness, suggesting that it is the most interconnected system. We perform Granger causality and transfer entropy tests which indicate that the connectedness of the North American system led that of the EU and Southeast Asia. Through our analysis we make technical improvements to the VAR-FEVD methodology and deal with the issues of outliers and overfitting of the VAR model. In the second framework we study rolling windows of high dimensional datasets comprising companies in the financial sector (GICS 40) globally from 1990 to 2016. Analyzing the global financial network as a system of ten economic regions, we find that the regions become more interconnected over time as evidenced by the increase in the number and size of inter-regional links. In addition, the regions are more interconnected during crises than during normal periods. North America and Europe, the two dominant regions, were connected to all other regions over the sample period from 1990 to 2016 and the links between the two regions were much stronger than those between the other regions. We find that North America, especially the U.S., was dominated by banks (GICS 4010) as they were the most impactful and vulnerable industry throughout the entire sample period. For the other regions, the dominant industry alternates between diversified financials (GICS 4020) and banks (GICS 4010). In this framework we contribute to the literature by addressing high dimensionality in financial data using the novel LoGo-TMFG algorithm which is the first application of the algorithm in connectedness measurement. In addition, our datasets are unique and much larger than those in other studies, where each rolling window contains up to 4,310 financial companies. By analyzing rolling windows of data, each of which contains companies that were active during the three-year period, we address the survival bias issue that many other studies do not. Our research findings are beneficial especially for policy makers, e.g., the central banks, who can use our connectedness metrics to enhance systemic risk monitoring. Practitioners in the macro research or macro trading desks at a bank or asset manager can also make use of both the methodologies we used as well as the research findings.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available