Category Archives: Uncategorized

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 Pierre@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 2017-03-06 the VIX was trading at 11.2 at the 3.1 percentile. The 14-day VIX Volga was estimated at 7.8 its 11.3 percentile and the shockindex at 0.7 or its 37.9 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 38 % of the time in Cluster 1, 45 % 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.

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 46 % of the time in Cluster 1, 32 % in Cluster 2, 22 % 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: Pierre@argonautae.co.uk

GBP TWI 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:Pierre@argonautae.com

plot of chunk plot plot of chunk plot

Brazil 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 CBOE Brazil ETF Volatility Index (VIX Brazil) as a measure of stock market risk for Brazil . 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 Brazil 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 Brazil the VIX Brazil Volga is not necessarily dependent on it. You can have a high level of volga whilst the VIX Brazil 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 2016-04-25 the VIX Brazil was trading at 43.4 at the 82.6 percentile. The 14-day VIX Brazil Volga was estimated at 29.6 its 89 percentile and the Brazil shockindex at 0.6 or its 58.3 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 03/2011 the VIX Brazil traded 53 % of the time in Cluster 1, 25 % in Cluster 2, 3 % in Cluster 3 and 19 % 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 59 % of the time in Cluster 1, 25 % in Cluster 2, 8 % in Cluster 3 and 8 % 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

UK Investor Allocation Update

The below is a generic asset allocation report produced from the perspective of a UK investor. I use the Barclay UK Gilts all maturities index, the MSCI World ex UK and the MSCI UK Gross indices (i.e dividends re-invested) as proxies for bonds and equities holdings. As time goes I will add a few more asset buckets such as EM, commodities and properties. So see this as a first attempt to an evolutive product.

The below charts shows the rolling 36-month return, volatility and risk adjusted return for each of the assets used in the final portfolio. Clearly equities have a higher volatility than bonds but also higher/lower localised returns highliting that timing is key in unlocking those higher returns.

plot of chunk Summary charts
The below summary performance statistics show that a UK investor would have got the best risk adjusted return by holding a broad basket of Gilts. Over the long term the returns would have been quite similar accross asset classes. However the risk as expressed by the annualised volatility of the monthly returns and the maximum drawdown would have been at it highest for equities and particularly for World Ex. UK stocks.

##                                 Gilts World Ex UK Stocks UK Stocks
## Annualized Return                8.91              10.53     10.26
## Annualized Standard Deviation    6.55              15.95     15.91
## Annualized Sharpe Ratio (Rf=0%)  1.36               0.66      0.64
## Worst Drawdown                  11.42              52.51     44.04

In the following I use a mean-variance model to compute the weights of the portfolio that maximises the information ratio on the efficient frontier.The model is optimised for “long only” and weights adding to one constraints. I use a rolling window of 36-month to estimate the returns, volatility and correlation input fed into the Markovitz model. The use of a rolling window implies that the momentum effect in the input is captured by the optimisation. Therefore if an asset becomes more attractive through time in terms of its risk adjusted return and/or diversification potential its participation into the final portfolio should increase and vice versae.

The two charts below show how the optimised portfolio weights have changed throughout time and also what were the weights at the end of the last month.

plot of chunk weights_chart
Using the above weights I then calculate the return of the portfolio for the folowing period assuming a cost of 0.25% of adjusted notional for each monthly rebalancement. The performance is compared to the return of a portfolio composed of 60% Gilts and 40% UK equities.

plot of chunk Opt_porfolio_charts

**Summary Performance Statistics

##                                 Benchmark 60/40 Optimal Portfolio
## Annualized Return                          8.55              8.14
## Annualized Standard Deviation              7.84              5.89
## Annualized Sharpe Ratio (Rf=0%)            1.09              1.38
## Worst Drawdown                            13.54             11.26

Drawdowns Table

##         From     Trough         To  Depth Length To Trough Recovery
## 1 1994-01-31 1994-05-31 1995-05-31 -11.26        17     17        5
## 2 1990-01-31 1990-04-30 1990-11-30  -9.49        11     11        4
## 3 1986-09-30 1986-09-30 1987-01-31  -6.06         5      5        1
## 4 2009-01-31 2009-01-31 2009-08-31   -5.1         8      8        1
## 5 2008-01-31 2008-06-30 2008-12-31  -5.07        12     12        6

Monthly Returns

##       Jan  Feb  Mar  Apr  May  Jun  Jul  Aug  Sep  Oct  Nov  Dec YEARLY
## 1984  1.6 -1.9  4.1  0.2 -4.4  1.7 -1.6  7.0  2.9  2.1  2.1  0.9   14.6
## 1985  1.6  1.8 -1.1  0.7  1.4  0.2  1.1  1.8  1.2  1.0  0.8  0.8   11.3
## 1986 -0.1  5.0  7.2  1.9 -0.2 -0.5  0.2  2.4 -6.1  1.0  0.0  3.1   13.9
## 1987  3.6  2.9  3.2  2.2  1.3 -1.0 -1.1 -0.5  0.6 -2.3 -0.4 -0.3    8.1
## 1988  2.9  2.0  1.0 -0.1  0.4  0.3  1.0 -1.7  2.7  1.8 -1.8  1.5   10.0
## 1989  3.2 -0.3  0.8  0.8  0.0  0.8  3.5  0.6 -1.3  0.8  0.1  1.9   11.1
## 1990 -3.5 -2.1 -2.5 -1.7  5.8  2.0 -0.3 -1.3 -0.8  3.9  3.0  0.3    2.8
## 1991  3.7  1.9  1.2  0.4  0.3  0.3  2.3  1.9  2.4  0.4 -0.4  1.3   15.8
## 1992  2.5  1.3 -2.4  4.1  2.1 -0.4 -0.2 -1.0  4.0  5.2 -1.0  2.5   16.6
## 1993  1.3  2.2  0.8 -1.3  0.5  3.3  2.4  3.4  0.1  1.3  1.9  3.6   19.8
## 1994 -0.1 -3.6 -3.3 -1.1 -3.7  0.5  1.4  0.9 -1.2  1.0  2.1 -0.5   -7.4
## 1995  1.1  0.5  1.4  1.3  3.6 -2.2  2.3  1.4  0.4  1.2  3.7  1.3   15.9
## 1996  0.9 -1.9  0.2  1.9 -0.5  1.6 -0.1  0.7  2.1  0.0  2.3 -0.9    6.3
## 1997  2.3  1.1 -1.7  1.9  2.2  1.0  1.6  0.0  3.8  0.2  0.6  1.8   14.8
## 1998  1.9  0.2  1.7  0.9  1.2 -0.3  0.9  3.1  3.2  0.0  3.1  2.2   18.1
## 1999  1.1 -1.7  0.8  0.1 -1.6 -0.1 -1.0  1.2 -2.2  2.1  1.6 -0.5   -0.2
## 2000 -1.7  1.7  1.4  0.9  0.5  0.4  0.0  0.0  0.4  1.0  1.8  0.6    7.2
## 2001  0.5 -0.4 -0.3 -0.9 -0.6 -0.4  1.9  1.1 -0.9  3.3 -0.2 -2.0    1.0
## 2002  1.2 -0.4 -1.5  0.7 -0.1  1.2  0.2  2.2  0.3  0.1 -0.1  1.0    4.7
## 2003  0.3  1.0 -0.6  1.2  2.4 -0.5 -1.1  0.4  0.4 -1.4  0.4  2.4    4.7
## 2004 -0.4  1.0  0.5 -0.7 -0.9  1.1  0.1  1.6  1.1  1.0  1.3  0.8    6.6
## 2005  0.1 -0.1  0.3  0.9  2.3  1.6  0.0  1.1  0.3 -0.4  1.8  1.6    9.6
## 2006  0.9  0.3 -0.6 -1.2 -0.7  0.0  1.3  0.9  0.6  1.2  0.0 -0.6    2.1
## 2007 -1.3  1.4 -0.2  0.3 -0.3 -1.0  1.2  1.1  0.7  1.6  0.2  1.5    5.1
## 2008 -2.0  0.3  0.2 -0.1 -1.3 -2.2  1.4  2.7 -2.3 -1.1  3.9  5.0    4.4
## 2009 -5.1  0.2  2.7 -0.1 -0.3  0.4  0.3  4.2  0.7 -0.4  1.2 -2.0    1.6
## 2010  0.1  0.1  1.4  0.4  1.5  0.8  0.5  4.0  0.2 -1.0 -0.8  0.6    7.7
## 2011 -1.8  1.0  0.2  2.1  1.0 -0.6  2.7  0.5  2.4  2.4  1.7  1.6   13.2
## 2012  0.7 -0.5 -0.6 -0.3  2.8 -0.1  1.9  0.1 -0.3 -0.6  1.1 -0.2    4.0
## 2013  0.5  2.0  1.9  1.0 -1.1 -2.6  2.2 -2.1  0.7  1.8 -0.7 -0.6    2.9
## 2014  0.6  1.0  0.2  0.4  1.4 -0.5  0.7  3.5 -0.9  1.3  3.3  0.8   11.8
## 2015  4.0 -2.1  1.6 -1.1  0.6 -3.5  1.9 -1.6  0.2  0.5  0.9 -1.1    0.4
## 2016  2.2  1.3   NA   NA   NA   NA   NA   NA   NA   NA   NA   NA    3.5

If you need more information or have questions about the above, feel free to contact me at pollux@argonautae.com

Oil….Have we missed the bottom… ?

Ok a lot of analysts are calling for lower oil prices, possibly to pre-2000 levels….does it mean  that we are close to the bottom …or possibly that have missed it ?

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 Reference Price for the OPEC Crude Oil Basket over the period of January 2003 to February 2015 . at Close of business 16 February 2015 it was trading at 56.43. plot of chunk chartdata In the below I used an R script written by The Sytematic Investor to plot the previous 250 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 250 days in black, and overlay similar historical patterns in grey. It Also shows what has been the price path for the following 250 days as well as the observed quartiles. plot of chunk pattern Finally I plot the last 250 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 750 historical values whilst ignoring the last 250 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

Quick Update on Market Risk……

Ok we have had our fair share of event risk  so far this year. Ukraine, Israel, Portugal  to name but a few….and  we now have Yellen telling us that the Fed may hike the rates earlier than forecast. And guess what ? The Vix is still trading in the low bottom quartile.vix

Though my shock Index  (ratio of  VIX volga and Vix over  21 days) did venture above its long term median value on the back of the Portugal  news, all  of my risk indicators remain  well entrenched in risk seeking  territory….

riskindex

My  2-state Markov model continue to sell volatility…I am not sure I would but I can t fault the outcome of the model so far.

markov

Bearing in mind the above I am  still  a bit quizzical  as of why risk is  trading so low. We are getting  indeed very little reaction from events that  once would have sent the VIX flying into the 20 to  25% range . The only rationale I have is that Investors are now accepting that  growth is significantly  taking hold and therefore investment flows are  logically channelling  into risky assets and carry trades. Also there  is  now a strong understanding  that central banks   are determined in doing  whatever is necessary to support growth and rid of systemic risk. This clearly  has a strong dampening effect  on any risk spikes, but lets not get too complacent about it…

Is The Fed Changing Its View About Inflation ?

Yesterday in her  Semi-annual Monetary Policy Report to the Congress,  Chairman  Janet Yellen  stated the following: “If the labor market continues to improve more quickly than anticipated by the Committee… then increases in the federal funds rate target likely would occur sooner and be more rapid than currently envisioned.”  However Treasury yields  have barely moved and the dollar appreciation  again the Greenback remained muted . The Euro depreciated only by 0.6% against the US Dollar since her speech.  As shown from the chart below though the dollar against a broad basket of currencies is fairly well priced this is not the case for 10-year yields. US 10 year yierld traded weighted Against all odds, Yellen  seems to have managed to contain market expectations yet again. This is quite outstanding when one looks at the hard data. Unemployment is clearly below the 6.5%  Fed target level. Also the rise in industrial production is quite telling of  an even lower unemployment rate in the months to come. unemployement USINDPRO

It is true that we have not yet seen much wage inflation in the us. The annual rate of increase in the average hourly earnings remains below 2% as shown below.  Wages remain dampened by the spare labour capacity. average earnings Clearly the Fed has accepted to remain behind the curve for quite a while  so  as not  to compromise any renewed  growth  in the US. The current low level of the Fed fund rate is a good illustration of  this. Fed Fund Contributing to a generally  dim view on the US economy by Fed members  could be that many forecasting models use 2×10 spreads as an input to forecast GDP. The abnormally ultra-low fed fund and therefore current steepness of the curve  could contribute to why  the Fed  growth forecasts may not be as accurate as should be.  After all even the IMF got it wrong with its growth forecast for the UK. 2x10It is now noticeable that over the last few months,  Inflation has crept  above the 2% target of the fed as indicated by the below chart. This is not surprising as trend in consumerism and aptitude of  price increase  is somehow function of  employments and wages earned. Though arguably not yet at an alarming level it will be interesting to watch out how the Fed react to further developments. US CPI So is the Fed starting to worry about inflation ? Plotting a Word Cloud of Yellen’s  speech it is indeed quite clear that Inflation is the central issue …..   yellen speechBearing in mind the above  it will be interesting to watch out for changes in the pattern of the inflows/outflows in US mutual funds. My thought is that we could see a resuming of the exit of bond products which was compromised by the ultra dovish tone adopted by Yellen when she took office. As mentioned in my previous post , the relative inflow/outflow in US versus foreign equities  could also have a perverse effect on the valuation of the US Dollar. High rates are not necessarily a long term driver of currency , capital flows are more important.  As a remainder of the current trends in US mutual funds  below are  couple of charts of my previous post. They show the distribution of the inflows/outflows in US mutual funds by asset class since the beginning of the year and the cumulative   flows in domestic versus foreign equities products since 2007. distribution inflows 2014cumulative flows I’ ll stick to my Long global equities and short dollar view…..

Business as Usual… Buy Equities !

The latest batch of US mutual funds inflow/outflow data  has been released by the Investment Company Institute in the US today and guess what  ? It is business as usual for US investors….Buy international equities and sell bonds…guess no one has been paying scant attention to the equity market wobbles we have seen aside the wannabe Roubini and other scaremongers of this world. anyhow the usual charts are below….

risk   versus flows cumul

 

Rplot Rplot01

No doubt the international equities buying trend remain strong and  this despite the recent comments by Yellen and weaker than expected ( but not weak) US economic data as well as the recent jitters in the equity and emerging markets. Clearly this may have provided  a short term respite for bond holders but my thoughts are that all this will be short lived and that we will soon see the 10-year US trading above 3%. Also the continual purchase of international securities by US investor is likely to bring headwind for the US$. This is possibly highlighted by the high resilience of the EUR-USD bearing in mind the differences in yield and economic growth of both the US and Europe…..Bearing in mind the rumoured long US$ position of the market it may be tempting to become contrarian. Even at 1.3600 the Euro could still provide some good profit opportunities…..

 

Update on US Mutual Fund Flows and Market Risk

Another batch of data has came out from the Investment Company Institute and though  for the first time  in a long time we are seeing  modest inflows into taxable bonds  funds, the theme is still about buying International equities for US investors. as the below charts indicate (click on them if you want to see larger versions).

inflowoutflow Rplot01

The appetite for equities clearly affects market risk and as can be seen  through our  Markov regime switching analysis the market is still in risk on mode.

vix

The world  appetite for risk and returns seems to pick up and economies are doing better (even Europe does…). So if the Chinese GDP does not come much worse than expected or surprise us on the upside it could be an interesting few weeks ahead for us Equity Bulls…..If it is worse I guess it will provide laggard with a better level to enter the market.