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Managing Event Risk in 2017

From politics to weather and more

Event risk provided some abrupt market turnarounds in 2016, emanating from political processes such as the Brexit referendum and US elections. This year is shaping up with the potential for even more turbulence from event risk – and not just from politics, but possibly from changing patterns in economic data and even the weather.

Characterising event risk

We like to categorise event risk into three distinct types. First, there are “binary dates,” such as elections, with near-certain potential for a divergent outcome. Second, there are “information dates,” which are typically data releases, and which have the ability to surprise with an unexpectedly extreme data point. Finally, we have “surprise!”, where neither the date nor the event/outcome is anticipated. Surprises can come from political statements, military actions, or natural disasters, as a few examples.

Here, we look at each category and provide some commentary around potential dates to watch in 2017. Then, we will explore a few different approaches to managing event risk, highlighting sophisticated options.

(1) Binary dates

Brexit and the US elections were the poster children for binary-date events in 2016. There are known dates for binary outcomes (i.e. on/off, yes/ no, liberal/conservative, this/that, etc.) but the probabilities for the divergent scenarios continue to shift right up until the actual event. Elections with highly polarised choices, or central bank policy meetings to shift policy or not, both fit into this category. From a statistical perspective, the key point is that prior to these known dates with binary outcome types, bimodal return expectation distributions may develop, which converge to single-mode expectations as soon as the outcome is known. Put another way, the pre-event market activity is definitely not characterised by anything resembling a normal or single-mode bell-shaped probability distribution. Historical volatility provides little information. Market moves are driven by factors that may influence the probability of one outcome versus the other, which can be quite a different set of market drivers than might be observed in more typical times.

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