A Guide to Global Financial Market Interconnectedness
0
0
Global markets have never been more interconnected due to globalization of commerce, cross-border access and financial trading. This interdependence makes small disruptions have greater repercussions across borders and can trigger risk events that lead to major financial crises.
Understanding these links is integral to global management, and there are effective techniques available such as the D-Y methodology that allow managers to gauge interconnectivity.
1. Stock Markets
Global stock markets have never been more interlinked due to globalization, cross-border commerce and easy trading access. Minor disruptions can quickly snowball into larger risk events.
Different markets and asset classes often show positive and negative correlations with one another, as a rising market will often influence other sectors to follow suit and rise as well. This phenomenon is commonly referred to as the butterfly effect – where small changes in macro economy create ripples of change throughout financial markets that lead to major shifts.
Some scholars have utilized equity indexes to model the interconnectivity of financial institutions within networks. Such methods have multiple applications, including mapping interconnectedness, estimating vector autoregression models and deriving standard network metrics such as centrality. Diebold and Yilmaz (2014) used equity return data from banking firms to estimate interconnectedness before using variance decomposition of VAR to derive to- and from-index measures that capture systemic risk contributions.
2. Commodity Markets
Financial markets are complex networks of interlinked systems that facilitate trading, investment and risk management globally. Their relationships may range from straightforward ones such as those between stocks and bonds to less tangible ones such as correlations among agricultural commodities, oil prices and inflation rates.
Financial markets operate according to similar principles as other economic networks; small disruptions in one market may have an outward ripple effect and affect other markets – this phenomenon is known as the butterfly effect and makes understanding these links paramount.
By employing network analysis tools such as Gephi and NodeXL, it’s possible to map the interconnections among financial firms and their counterparties. One approach involves using D-Y as a standard network measure – an estimator for vector autoregressive models using market data which produces both to- and from-indices of interconnectedness that capture exposures to systemic network shocks (outward spillover) as well as exposures within their own firms/entities within the network (inward spillover). These measures can then be used for visualization or for derivation of standard network metrics like centrality/closeness etc.
3. Bond Markets
All financial markets are interlinked – even those which appear unrelated, like municipal bond markets and gold prices. Some relationships are more obvious than others – like stocks versus bonds; nonetheless all markets affect each other. An increase in inflation will decrease bond values while increasing stock values will allow stocks to progress forward.
Instead of the D-Y approach which requires access to supervisory exposure data, this approach utilizes market data like equity returns or volatility to estimate interlinkages among financially exposed entities. “To-index” and “from-index” measures can capture contributions of individual financial firms to systemic network events (outward spillovers) as well as their exposures. Besides investment funds, banks and insurers can also utilize this methodology.
4. Currency Markets
As financial markets across the globe become ever more interlinked, understanding their interactions and what the repercussions for risk may be is becoming ever more crucial. Mapping interconnections between markets using standard network analysis tools such as Gephi or NodeXL is an invaluable way to gain insights into relationships underlying them as well as standard metrics like centrality (out-degree: number of links leaving an entity; in-degree: number of links entering an entity) and closeness can provide crucial data sets.
Markets may seem unrelated at first glance, but an unexpected event such as devaluation of Thai baht in 1997 had ripple effects that rippled throughout markets and asset classes, eventually sparking an economic crisis worldwide.
