Monthly Archives: December 2015

US MUTUAL FUND FLOWS REPORT UPDATE

Wed Dec 30 19:06:56 2015

Fund flows are important as they reflect the general investor preference for a specific asset class given current and expected economic conditions and market risk. They may also highlight non-sustainable market positioning. The ICI in the US tracks about 98% of the inflows and outflows in US mutual funds and makes its data freely available on its website. The following is a summarised report of the data it publishes every Wednesday. The first charts shows the cumulative inflows/outflows in each of the asset classes buckets since 2007

plot of chunk cumulative

During the month of December we have seen flows of US$ -22.2Bn in Domestic equities,US$ -8.12Bn in international equities, US$ -10.7Bn in Hybrid products,US$ -27.7 Bn in taxable bond funds and US$ 3.66Bn in non taxable bond funds.

plot of chunk month to date
The Charts below shows the distribution in percentage terms of the US$ -34.7Bn that have flowed into US$ Mutual funds over the last 12-month.

plot of chunk distribution

The below charts show the monthly inflows/outflows for each type of fund and plot them both within their 95% confidence intervals and also relative to their historical distribution. This provides a level of information in respect of how “out of line” or not the current month inflows/outflows may be relative to their past history. In the distribution charts The current month is highlited in blue whereas the vertical red lines represent the 95% confidence intervals.

plot of chunk flowdistribution

The chart below plot the inflows/outflows T-statistics for each of the funds cathegories considered. The Map chart provides information for period ranging from 2 years to 3 months.The greater the square the more important the inflows (green) outflows(red) over a given period.

plot of chunk flowmap

WTI Update….

Ok we are getting toward year end and the US$ has been the darling of active managers…but now that we are entering a notoriously illiquid time of the year is there some room for further appreciation ?

Whatever the market being traded, there always will be a a question being asked at one moment: How far can this thing 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. This report aims to contribute to this.

The below chart shows the WTI Spot Price over the period of January 1986 to December 2015 . On the 29 December 2015 it was trading around 37.84.

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.

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Finally I plot the last 125 days and a trend forecast derived from an ARIMA(1,1,0) 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

EUR-USD Break Analysis…

In the following I us an R package BFAST designed to detect strucutural breaks in time series.The script Iteratively detects breaks in the seasonal and trend component of a 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 could be used as an overbought/oversold indicator. Anyway, just work in progress…so any input / suggestions are always welcome as usual. Feel free to contact me at:pollux@argonautae.com

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Trade Weighted Currency Indices Stretch Map

Trade Weighted Currency Indices Report

Tue Dec 29 22:05:42 2015

The following report aims to provide a gauge to the current strenght of major currencies. For doing so I use the Bank of England Trade weighted Exchange rate indices and a standardised statistical measures of price deviation to provide an estimate of how stretched major currencies are on a trade weighted perspective.

plot of chunk linechart

I first calculate the T-stat of the mean price deviations over a rolling period of 61 days. The charts below show the results for each currency over the last 500 days. The purple line represents the median value since 1990-01-03 and the red lines represent the 95% confidence intervals. Therefore if the value is above or below those the deviation of the given currency would be deemed as atypical relative to what #would be expected under a normal distribution and therefore overbought/oversold.

plot of chunk rolling chart

The following Map chart shows how stretched the currencies are over time horizons ranging from 1-month to 1-year. The bigger the square the most significant the upside (green) or downside (red) of currencies over the given period.

plot of chunk stretch map
The charts below show how the daily changes in the Trade weighted indices have correlated since January 1990 and since the begining of 2015.

plot of chunk correlation
Finally, the following provide an ARIMA forecast for each of the trade weighted indices. My script selects the best ARIMA fit over the previous 250-day to generate a forecast for the next 21 days.
It also shows the forecast confidence intervals.

plot of chunk arimaforecastplot of chunk arimaforecastplot of chunk arimaforecast

US Stock Market Risk Report Update…

The following report 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 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. Feel free to contact me at pollux@argonautae.com for more information on the subject.

In my approach I recognise that the nominal level of implied volatility is a crude metric of risk therefore I also use two 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 therefore cannot be accurately budgeted fo r. The ShockIndex is the ratio between the Volga and VIX at the beginning the historical window chosen to evaluate the Volga. It quantifies sharp changes and acceleration in risk levels. Historically it has proven to be a good classifying measure for market event risks.

The below charts shows those three measures both relative to a time axis and their historical distribution. The red lines are the 95% confidence intervals, the purple line the median. The blue line highlight the current level. The VIX Volga and ShockIndex in this report 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 the charts shows only the recent years.

plot of chunk riskchart

At close of business the 2015-12-28 the VIX was trading at 16.9 at the 43.9 percentile. The 14-day VIX Volga was estimated at 27.8 its 91.2 percentile and the shockindex at 1.8 or its 96.9 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. Therefore clustering and aggregating the whole data into a single chart should be useful to the end user. 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 was split into 4 distinct clusters. Those clusters where computed not only as a function of the VIX level but also as a function of the other variables, namely VIX volga and Shockindex.

Since 1990 the VIX traded 49 % of the time in Cluster 1, 39 % in Cluster 2, 10 % 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 52 % of the time in Cluster 1, 29 % in Cluster 2, 19 % in Cluster 3 and 0 % in Cluster 4.

plot of chunk ytdriskchart

Finally the below chart shows a Self Organising Map of the above mentioned 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, feel free to reach me at: pollux@argonautae.com

Trade Weighted Currency Indices Stretch Map

Trade Weighted Currency Indices Report

Wed Dec 23 21:34:02 2015

The following report aims to provide a gauge to the current strenght of major currencies. For doing so I use the Bank of England Trade weighted Exchange rate indices and a standardised statistical measures of price deviation to provide an estimate of how stretched major currencies are on a trade weighted perspective.

plot of chunk linechart

I first calculate the T-stat of the mean price deviations over a rolling period of 61 days. The charts below show the results for each currency over the last 500 days. The purple line represents the median value since 1990-01-03 and the red lines represent the 95% confidence intervals. Therefore if the value is above or below those the deviation of the given currency would be deemed as atypical relative to what #would be expected under a normal distribution and therefore overbought/oversold.

plot of chunk rolling chart

The following Map chart shows how stretched the currencies are over time horizons ranging from 1-month to 1-year. The bigger the square the most significant the upside (green) or downside (red) of currencies over the given period.

plot of chunk stretch map
The charts below show how the daily changes in the Trade weighted indices have correlated since January 1990 and since the begining of 2015.

plot of chunk correlation
Finally, the following provide an ARIMA forecast for each of the trade weighted indices. My script selects the best ARIMA fit over the previous 250-day to generate a forecast for the next 21 days.
It also shows the forecast confidence intervals.

plot of chunk arimaforecastplot of chunk arimaforecastplot of chunk arimaforecast

US MUTUAL FUND FLOWS REPORT UPDATE

Wed Dec 23 21:33:47 2015

Fund flows are important as they reflect the general investor preference for a specific asset class given current and expected economic conditions and market risk. They may also highlight non-sustainable market positioning. The ICI in the US tracks about 98% of the inflows and outflows in US mutual funds and makes its data freely available on its website. The following is a summarised report of the data it publishes every Wednesday. The first charts shows the cumulative inflows/outflows in each of the asset classes buckets since 2007

plot of chunk cumulative

During the month of December we have seen flows of US$ -20.6Bn in Domestic equities,US$ -5.54Bn in international equities, US$ -9.1Bn in Hybrid products,US$ -21.9 Bn in taxable bond funds and US$ 2.4Bn in non taxable bond funds.

plot of chunk month to date
The Charts below shows the distribution in percentage terms of the US$ -24.3Bn that have flowed into US$ Mutual funds over the last 12-month.

plot of chunk distribution

The below charts show the monthly inflows/outflows for each type of fund and plot them both within their 95% confidence intervals and also relative to their historical distribution. This provides a level of information in respect of how “out of line” or not the current month inflows/outflows may be relative to their past history. In the distribution charts The current month is highlited in blue whereas the vertical red lines represent the 95% confidence intervals.

plot of chunk flowdistribution

The chart below plot the inflows/outflows T-statistics for each of the funds cathegories considered. The Map chart provides information for period ranging from 2 years to 3 months.The greater the square the more important the inflows (green) outflows(red) over a given period.

plot of chunk flowmap

US Stock Market Risk Report Update…

The following report 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 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. Feel free to contact me at pollux@argonautae.com for more information on the subject.

In my approach I recognise that the nominal level of implied volatility is a crude metric of risk therefore I also use two 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 therefore cannot be accurately budgeted fo r. The ShockIndex is the ratio between the Volga and VIX at the beginning the historical window chosen to evaluate the Volga. It quantifies sharp changes and acceleration in risk levels. Historically it has proven to be a good classifying measure for market event risks.

The below charts shows those three measures both relative to a time axis and their historical distribution. The red lines are the 95% confidence intervals, the purple line the median. The blue line highlight the current level. The VIX Volga and ShockIndex in this report 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 the charts shows only the recent years.

plot of chunk riskchart

At close of business the 2015-12-22 the VIX was trading at 16.6 at the 41.6 percentile. The 14-day VIX Volga was estimated at 27.5 its 91 percentile and the shockindex at 1.8 or its 96.4 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. Therefore clustering and aggregating the whole data into a single chart should be useful to the end user. 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 was split into 4 distinct clusters. Those clusters where computed not only as a function of the VIX level but also as a function of the other variables, namely VIX volga and Shockindex.

Since 1990 the VIX traded 58 % of the time in Cluster 1, 31 % in Cluster 2, 10 % 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 60 % of the time in Cluster 1, 21 % in Cluster 2, 20 % in Cluster 3 and 0 % in Cluster 4.

plot of chunk ytdriskchart

Finally the below chart shows a Self Organising Map of the above mentioned 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, feel free to reach me at: pollux@argonautae.com

US MUTUAL FUND FLOWS REPORT UPDATE

Wed Dec 16 19:06:51 2015

Fund flows are important as they reflect the general investor preference for a specific asset class given current and expected economic conditions and market risk. They may also highlight non-sustainable market positioning. The ICI in the US tracks about 98% of the inflows and outflows in US mutual funds and makes its data freely available on its website. The following is a summarised report of the data it publishes every Wednesday. The first charts shows the cumulative inflows/outflows in each of the asset classes buckets since 2007

plot of chunk cumulative

During the month of December we have seen flows of US$ -13.2Bn in Domestic equities,US$ -2.11Bn in international equities, US$ -3.52Bn in Hybrid products,US$ -9.22 Bn in taxable bond funds and US$ 1.75Bn in non taxable bond funds.

plot of chunk month to date
The Charts below shows the distribution in percentage terms of the US$ 4.1Bn that have flowed into US$ Mutual funds over the last 12-month.

plot of chunk distribution

The below charts show the monthly inflows/outflows for each type of fund and plot them both within their 95% confidence intervals and also relative to their historical distribution. This provides a level of information in respect of how “out of line” or not the current month inflows/outflows may be relative to their past history. In the distribution charts The current month is highlited in blue whereas the vertical red lines represent the 95% confidence intervals.

plot of chunk flowdistribution

The chart below plot the inflows/outflows T-statistics for each of the funds cathegories considered. The Map chart provides information for period ranging from 2 years to 3 months.The greater the square the more important the inflows (green) outflows(red) over a given period.

plot of chunk flowmap

Impact of the Fed annoucements on the VIX

The following report shows what were the cumulative returns of the VIX 21 days prior and following the FOMC meetings. The green line is the average of the sample. It also shows how the delivered volatility of the VIX  prior and after the Fed annoucements. I have split my analysis to show the market reaction as for when when there was not change in the Fed Funds target rate, when a cut in the rate occured and when there was hike.

From 1990-02-07 to 2015-10-28 there was 261 Fed meetings.Out of those 31 meetings translated in an increase in the target rate and 32 in a cut. The below charts shows when those took place and also the distribution fo the changes in the Fed’s target rate.

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The charts below show the VIX response for all of the Fed’s meetings and the delivered volatility 21 days prior and after the meetings. The green line shows the
average response to teh even.

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The charts below show the VIX response and delivered volatility for all of meetings where a cut occured

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The charts below show the VIX response and delivered volatility for all of meetings where a hike occured

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The charts below show the VIX response an delivered volatility for all of meetings where there was no change in the Target rate.

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There does not seem to be any clear pattern in the way the VIX trade or its volatility prior or after the Fed’s annoucements….contrarily to what is observed for the S&P 500