Monthly Archives: February 2020

Stock Market Risk…Panic Mode

Riot Mode is usually a short lasted state….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.

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.

plot of chunk riskchart

At close of business the 2020-02-27 the VIX was trading at 39.2 at the 97.6 percentile. The 14-day VIX Volga was estimated at 45.6 its 97.7 percentile and the shockindex at 3 or its 99.2 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 54 % of the time in Cluster 1, 34 % in Cluster 2, 11 % 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.

plot of chunk cluster_chart

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 63 % of the time in Cluster 1, 22 % in Cluster 2, 15 % in Cluster 3 and 0 % in Cluster 4.

plot of chunk ytdriskchart

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.

plot of chunk SOM_chart

Always happy to discuss any of the above and its possible applications, feel free to reach me at: Pierre@argonautae.co.uk

S&P 500 Path Analysis…Or What Panic Looks Like

Whatever the market being traded, there always will be a a question being asked at one moment: How far can this go ? Clearly not an easy question to answer as this will invariably depends on factors that are partly unknown or difficult to estimate, such as fundamentals, market positioning or market risk amongst others. The first part is obviously to assess how atypical the move experienced in the given instrument is. The below aims to contribute to this in a statistical way.

The below chart shows the S&P 500 over the period of January 1985 to February 2020 . On the 27 February 2020 it was trading around 2978.76001.

plot of chunk chartdata

In the below I plot the previous 125 days against other similar historical periods that would have closely matched the recent history. The data has been normalised so as to be on the same scale. The chart shows the latest 125 days in black, and overlay similar historical patterns in grey. It Also shows what has been the price path for the following 125 days as well as the observed quartiles.

plot of chunk pattern

Finally I plot the last 125 days and a trend forecast derived from an ARIMA(0,1,0) model as well as the 95% confidence intervals. The ARIMA model is fitted to the past 625 historical values whilst ignoring the last 125 days, therefore we can look at the recent price path against the trend forecast and its confidence intervals to gauge how (a)typical the recent move has been.

plot of chunk arimaplot

S&P500 Break Analysis…Close to Overdone

In the following I use an R package BFAST designed to detect structural breaks in time series.The script iteratively detects breaks in the seasonal and trend component of time series. The first chart shows the various break and fitted regressions. The second chart shows the deviations from the regression lines and 95% interval of confidence. This can be used as an overbought/oversold indicator. Feel free to contact me at:Pierre@argonautae.com

plot of chunk plot plot of chunk plot

Time to Sell VIX?

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.

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.

plot of chunk riskchart

At close of business the 2020-02-26 the VIX was trading at 27.6 at the 88.7 percentile. The 14-day VIX Volga was estimated at 30.6 its 92.7 percentile and the shockindex at 2 or its 96.6 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 55 % of the time in Cluster 1, 31 % in Cluster 2, 11 % 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.

plot of chunk cluster_chart

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 63 % of the time in Cluster 1, 23 % in Cluster 2, 14 % in Cluster 3 and 0 % in Cluster 4.

plot of chunk ytdriskchart

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.

plot of chunk SOM_chart

Always happy to discuss any of the above and its possible applications, feel free to reach me at: Pierre@argonautae.co.uk

S&P500 Break Analysis…

In the following I use an R package BFAST designed to detect strucutural breaks in time series.The script iteratively detects breaks in the seasonal and trend component of time series. The first chart shows the various break and fitted regressions. The second chart shows the deviations from the regression lines and 95% interval of confidence. This can be used as an overbought/oversold indicator. Feel free to contact me at:Pierre@argonautae.com

plot of chunk plot plot of chunk plot

S&P500 Path Analysis

Whatever the market being traded, there always will be a a question being asked at one moment: How far can this go ? Clearly not an easy question to answer as this will invariably depends on factors that are partly unknown or difficult to estimate, such as fundamentals, market positioning or market risk amongst others. The first part is obviously to assess how atypical the move experienced in the given instrument is. The below aims to contribute to this in a statistical way.

The below chart shows the S&P 500 over the period of January 1985 to February 2020 . On the 26 February 2020 it was trading around 3116.389893.

plot of chunk chartdata

In the below I plot the previous 125 days against other similar historical periods that would have closely matched the recent history. The data has been normalised so as to be on the same scale. The chart shows the latest 125 days in black, and overlay similar historical patterns in grey. It Also shows what has been the price path for the following 125 days as well as the observed quartiles.

plot of chunk pattern

Finally I plot the last 125 days and a trend forecast derived from an ARIMA(1,1,2) model as well as the 95% confidence intervals. The ARIMA model is fitted to the past 625 historical values whilst ignoring the last 125 days, therefore we can look at the recent price path against the trend forecast and its confidence intervals to gauge how (a)typical the recent move has been.

plot of chunk arimaplot