Monthly Archives: February 2016

You say oil….I say Tullow !

It seems that oil has been bottoming around 30 for a while….could be time to look at those “oilers” again…So here a quick analysis of the relationship between the share price of Tullow Oil Plc price and the GBP Oil price

The below chart shows the cumulative percentage return both for the Tullow Oil Plc price and the GBP Oil price . Clearly the relationship has been positive over time.

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The rolling 52-week correlation confirms that though time varying, has been strongly positively correlated.

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The below plot is a bi-density chart of the Tullow Oil Plc Share price versus the GBP Oil price. The contour lines delimit the empirical joint distribution. The blue line is the best fit derived from a locally weighted scatterplot smoothing. The dotted red lines delimit the quantiles for the Tullow Oil Plc price. Whilst depicting the direction of the relationship, this chart aims also to answer the question: Does the Tullow Oil Plc share price tend to appreciate/depreciate more depending on which level GBP Oil price is trading. The cross-hair shows where the stock trade relative to the GBP Oil price
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Finally I compute a granger causality test over a rolling period of 52-week in order to investigate the possibility of a one step ahead lead / lag relationship between Tullow Oil Plc price and the GBP Oil price share price. The two below charts show the rolling P Values of the test for both the share causing the Tullow Oil Plc price and vice versa.

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Have we seen the bottom in Oil ?

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 February 2016 . On the 26 February 2016 it was trading around 33.09.

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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(3,1,2) 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.

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GBP-USD……

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 GBPUSD Spot Price over the period of January 1990 to February 2016 . On the 24 February 2016 it was trading around 1.3976.

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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(3,1,0) 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.

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You say Randgold…I say Gold !

Ok I bought a few Randgold shares a while ago hoping for a rebound in the price of Gold…anyhow I just wanted to make sure I bough gold. So here a quick analysis of the relationship between the share price of RandGold price and the Gold Spot Price GBP

The below chart shows the cumulative percentage return both for the RandGold price and the Gold Spot Price GBP . Clearly the relationship has been positive over time.

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The rolling 52-week correlation confirms that though time varying, has been strongly positively correlated.

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The below plot is a bi-density chart of the RandGold Share price versus the Gold Spot Price GBP. The contour lines delimit the empirical joint distribution. The blue line is the best fit derived from a locally weighted scatterplot smoothing. The dotted red lines delimit the quantiles for the RandGold price. Whilst depicting the direction of the relationship, this chart aims also to answer the question: Does the RandGold share price tend to appreciate/depreciate more depending on which level Gold Spot Price GBP is trading. The cross-hair shows where the stock trade relative to the Gold Spot Price GBP
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Finally I compute a granger causality test over a rolling period of 52-week in order to investigate the possibility of a one step ahead lead / lag relationship between RandGold price and the Gold Spot Price GBP share price. The two below charts show the rolling P Values of the test for both the share causing the RandGold price and vice versa.

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GBP-USD…the smell of BREXIT…..

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 GBPUSD Spot Price over the period of January 1990 to February 2016 . On the 22 February 2016 it was trading around 1.4149.

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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(0,1,2) 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.

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US Stock Market Risk Report Update…Sailing toward blue seas again….

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.

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At close of business the 2016-02-22 the VIX was trading at 19.3 at the 56.5 percentile. The 14-day VIX Volga was estimated at 22.7 its 83.6 percentile and the shockindex at 0.8 or its 63.9 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 48 % of the time in Cluster 1, 40 % 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.

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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 53 % of the time in Cluster 1, 29 % in Cluster 2, 19 % in Cluster 3 and 0 % in Cluster 4.

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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.

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

Trade Weighted Currency Indices Stretch Map

Trade Weighted Currency Indices Report

Mon Feb 22 06:08:47 2016

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.

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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.

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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.

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

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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.

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US MUTUAL FUND FLOWS REPORT UPDATE

Sun Feb 21 14:15:08 2016

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

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During the month of February we have seen flows of US$ -1.31Bn in Domestic equities,US$ 8Bn in international equities, US$ 0.662Bn in Hybrid products,US$ -6.22 Bn in taxable bond funds and US$ 2.62Bn in non taxable bond funds.

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The Charts below shows the distribution of the US$ -203Bn that have flowed into US$ Mutual funds over the last 12-month.

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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.

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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.

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NIKKEI 225…overdone indeed…..

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 Nikkei 225 Index over the period of January 1985 to February 2016 . On the 18 February 2016 it was trading around 1.6258 × 104.

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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.

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Growing Randomness of Currency Markets…

Back in 20114 I wrote a chapter for The Role of Currency in Institutional Portfolio by Professor Levich and M. Pojarliev.In my research I debated about the growing efficiency in foreign exchange markets potentially making a more arid ground for active managers to generate alpha. Clearly some could argue about the timing of my publication on the subject as since then some strong trends have occurred in US$ crosses. In fact the last quarter of 2014 proved to be a significant localised alpha bonanza for many currency managers. This fed into much enthusiasm from managers and a regain of interest for active currency management. However since then those trends have abated and it is lean times again for currency managers who use single factor strategies. Anyhow, my study focus on long term dynamics and the secular growing efficiency of market which I suggest is driven by a cocktail of world globalisation and advances in information technology. This has enhanced in an unprecedented way the availability of information, access to market and provided a level field market pricing to market participant.

In a seminal paper Emmanuel Acar laid the theoretical background demonstrating that the expected return of directional trading rules can be attributed mainly to autocorrelations (i.e. how the daily returns of an asset are correlated from one period to another) and drift (i.e. the absolute percentage deviation of the price series). In my paper I proposed a methodology based on his finding to classify financial time price series. The below shows what was the drift, autocorrelation and volatilities of the 45 G10 FX cross exchange rates over the period 1996 to 2015.

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Using significance tests for the drift and first order autocorrelation of the time series over a rolling windows of 125 days it is possible to classify each of the 45 G10 FX crosses into 6 specific behaviours, namely: Strong trend, strong mean reversion, short term trend, Long term trend + short term reversion, Long term trend + white noise, random walk. More details on this can be found in my paper. In the below I have aggregated the time dimension (i.e. long and short term) so as to end up only with three states: Trending, Mean Reverting and Random walk. The bar chart shows the percentage of time that each currency pair spent in each of those state. It is quite apparent that some currencies have had a greater propensity to trend than other (i.e. US$ and JPY crosses) and also that currencies spent most of their time in a random walk state. It is still possible to generate value in the later as long as the risk is compensated by a high level of carry. Clearly this has not been the case over recent time and may explain why so many currency managers had poor perfpormace.

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The following chart shows the number of US$ crosses that have been in trending regime over the previous 750 days. It is quite clear that aside the last quarter of 2014, trends have been seldom.

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Finally, the last charts shows the number of currencies that would have been classify as trending, mean reverting or random on a rolling basis since the seventies. It is quite clear that currencies have become more random over the last few decades. This in turn means that currency manager performance has become far much more dependent on the level of carry and volatility. I am always happy to have a natter about what I produce so feel free to contact me at Pierre@Argonautae.co.uk.

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