The following analysis provides an update on some of the metrics I use to classify market risk. The word classify is more appropriate as I think that in essence you cannot forecast risk but rather attempt to adjust to it into a timely fashion. Clearly risk would not be a risk if you could forecast it accurately. However as there is generally some degree of persistence or clustering in risk regimes, using a dynamic classification may be a useful approach for portfolio rebalancing and hedging. In this report I use the VIX as a measure of global financial market risk. The same methodology can be successfully applied to other inputs such as implied currency volatility. Feel free to contact me at Pierre@argonautae.com for more detailled information on this analysis and how it can be embedded in an investment process.
My approach acknowledges that the nominal level of implied volatility is a crude metric of risk therefore I consider other measures. The VIX Volga, a measure of uncertainty of risk and the ShockIndex a measure of market dislocation. VIX Volga is simply the volatility of the VIX over a given period. This measure highlights how uncertain and unstable the level of risk has become. Though positively correlated to the level of the VIX the VIX Volga is not necessarily dependent on it. You can have a high level of volga whilst the VIX is trading at rather innocuous levels. This is not a trivial observation as the leverage undertaken by market participants tends to be an inverse function of market volatility which implies a greater vulnerability when volatility becomes uncertain at low levels and cannot be accurately budgeted for (Investors become risk seeking in low volatility environement though it is from those levels that you observe the greatest adverse changes in terms of risk). The ShockIndex is a ratio between the Volga and VIX at the beginning the historical window chosen to evaluate the Volga. It provides a measure of acceleration in risk levels. Historically it has proven to be a good classifying measure for market event risks.
The below charts shows those metrics both relative to a time axis and their historical distribution. The red lines are 95% confidence intervals, the purple line identify the median. The blue line highlight are the current level. The VIX Volga and ShockIndex in the below analysis are evaluated over a period of 14 days. The medians and 95% confidence intervals are calculated over the full history going back to 1990 though some of the charts focus on the more recent years.
At close of business the 2018-10-10 the VIX was trading at 23 at the 76.2 percentile. The 14-day VIX Volga was estimated at 23.5 its 85.1 percentile and the shockindex at 1.8 or its 95.1 percentile.
The above charts are useful, however their visualisation is quite limiting. On the one hand we need quite a few charts to present the data on the other hand it is difficult to show the full VIX history going back to 1990 as this would make the charts unreadable. Also this does not highlight any relationship between the metrics. Therefore clustering and aggregating the whole data into a single chart could be more useful to support an investment decision. To answer this I use a mapping technique developed by Kohonen in the 1980′. It uses an unsupervised neural network to re-arrange data around meaningful clusters. Though computationally complex is a practical way to summarise multidimensional data into a low (usually 2) dimensional system.
The below chart shows how the VIX price history split into 4 distinct clusters. Those clusters are computed as a function of the VIX level , VIX volga and Shockindex.
Since 1990 the VIX traded 43 % of the time in Cluster 1, 39 % in Cluster 2, 15 % in Cluster 3 and 2 % in Cluster 4. Overall the layering provided seems quite intuitive as the increase in risk and time spent in each cluster points toward what would generally be expected from market risk regimes ranging from low to high risk.
In the chart below we zoom on the various regimes within which the VIX has been trading for the current year. so far it traded 55 % of the time in Cluster 1, 23 % in Cluster 2, 21 % in Cluster 3 and 0 % in Cluster 4.
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## 2: colnames(chartdata) <- c("Low","Moderate","High","Extreme")
## 3: chart.TimeSeries(chartdata['2007::',]),
Concluding the above analysis the below chart shows a Self Organising Map of the considered risk metrics. The data has been grouped and colored as a function of four clusters of increasing market risk regimes. Obviously as shown on the map, the minimum level of volatility pertains to cluster 1 and the highest to cluster4. The current regime and its progression from 21 days ago is also highlighted on the map.
Always happy to discuss any of the above and its possible applications, feel free to reach me at: Pierre@argonautae.co.uk