For investors seeking broad diversification across asset classes and around the world, weighing the risks and rewards of investing outside of the advanced economies is critical in the decision-making process.
Emerging and frontier markets may indeed exhibit a lack of correlation with the G7 countries, for example, but how do they react to major geopolitical events that could impact the volatilities of all asset classes, industry sectors and countries? In his current research, Nobel Laureate Robert F. Engle [1], the Michael Armellino Professor of Finance at New York University’s Stern School of Business, is tackling the subject of financial volatility and geopolitical risk. He discussed this work and some of his findings at the recent Bloomberg Quant Seminar.
For a world in constant flux, what is geopolitical risk?
The term “geopolitical risk” covers a number of categories and situations; some events may be fleeting, having a significant impact for a day or two, while others have a more sustained effect on the markets. The scenarios may be economic or political, encompassing a range of threats (terrorism, war, cyber incidents) as well as normal occurrences that take place frequently in democratic nations (elections, congressional or parliamentary actions, and the push and pull of political movements in general). Risk is sometimes defined as uncertainty, particularly with possible surprise; one key to managing risk effectively is to have timely quantitative information and to interpret it correctly.
In general, global volatility has been quite low since the end of the global financial crisis, with occasional spikes: e.g., around the Brexit vote and the Trump election. However, even as the volatilities across assets, asset classes, sectors and countries intensify and ease, they tend to be correlated. The idea of shocks to volatilities and their correlation is a new and testable observation. Further, the information uncovered by the data may be used to help forecast or hedge geopolitical risks.
A new tool for risk management and hedging: The Geopolitical Volatility Factor (GPVF)
Forecasting asset returns in the financial markets is notoriously difficult, but, oddly enough, volatility is more predictable: both ARCH and GARCH models show that shocks to volatility are persistent and exhibit long half-lives. Such shocks can be extracted from volatility estimators and, with this in mind, Professor Engle has developed a new Geopolitical Volatility Factor (GPVF). The GPVF is based on the standardized residuals from a factor model with GARCH-style residuals. He then tested the model by applying estimation algorithms to nine U.S. sector ETFs and 45 MSCI country ETFs and studying the results through Monte Carlo simulations.
In these sector and country models, extreme events and factor loadings can be readily identified. High-ranking examples include the day the Federal Reserve made the biggest rate cut in almost 24 years (January 22, 2008); the day after the 9/11 attacks in the U.S. (September 12, 2001); the Brexit vote (June 24, 2016); the day the Swiss National Bank abandoned the cap on the CHF (January 15, 2015); and the U.S. election of 2016 (November 9, 2016). From a portfolio perspective, it is possible to see which assets are less sensitive to such geopolitical shocks and also to determine which countries appear to be less impacted by events in the rest of the world. With the information gleaned from the GPVF, geopolitical risk may be reduced or hedged by including an additional criterion in the portfolio optimization process.
Other risk measures and future research
Other models to consider include the GPR, which is a measure of geopolitical risk (see Caldara and Iacoviello, 2017), the GEPU, which quantifies Global Economic Policy Uncertainty (see Baker, Bloom, and Davis, 2016), and BlackRock’s Geopolitical Risk Dashboard. Or, faced with too much risk, as Professor Engle joked, we could just retreat to a cabin in the woods. Humor aside, his model has demonstrated useful properties; in future research, he plans to study more asset classes and to examine geopolitical volatility dynamics as an adaptive system.
Lightning talks
Following a short Q&A session, Bruno Dupire, the host of the event, kicked off a series of “lightning talks,” 5-minute presentations where industry experts, researchers and academics present a wide range of subjects to stimulate fresh thinking and interaction between various disciplines. Each talk examines a way that the industry is evolving and serves as an essential exploratory aspect of the Bloomberg Quant Seminar series.
In this session, Espen Gaarder Haug of the Norwegian University of Life Sciences described the geometry of high-speed trading; Arturo Cifuentes of Columbia University explained how pricing auction guarantees work in the art market; and Nataliya Naumova of NYIT addressed the effects of ASU 842, which pertains to codification improvements in FASB accounting standards for leases.
In addition, Alexandra Sedlovskaya of Harvard University explored biases in self and identity, particularly between public/workplace and private personas. Roza Galeeva of NYU offered thoughts on decoding the dynamics of oil correlation, and Adam Lynne of Bloomberg L.P. shared insights on the quant world.
About the Bloomberg Quant seminar series
The Bloomberg Quant (BBQ) seminar series is held in New York and covers a wide range of topics in quantitative finance. Each session is chaired by Bruno Dupire, head of Quantitative Research at Bloomberg L.P., and features a keynote speaker presenting his or her current research. This presentation is followed by several 5-minute “lightning talks” in quick succession. This format gives the audience the opportunity to be exposed to a wider variety of topics. Sign up to receive invitations to future events in this series.
[1] In 2003, Professor Engle received the Sveriges Riksbank Prize in Economic Sciences for developing methods of analyzing economic time series with time-varying volatility (ARCH). The prize was divided equally with Clive W. J. Granger for his work on methods of analyzing economic time series with common trends (cointegration).