Author Archives: Pierre

Non Farm Payrolls time again…

It is NFP time again, sweepstakes must be rife on trading floors around the world…..So it is time to use my NFP forecasting model which leverages on both an ARIMA forecast and a simple linear regression using the ADP as the independent variable to generate a mixed forecast of the NFP.

Not surprisingly the ADP and the NFP data releases are positively correlated, thoug this has been significantly time varying. Also the NFP tend to be generally twice as volatile than the ADP numbers, highlighting their challenging nature for a forecaster.

plot of chunk chartsplot of chunk charts

##       ADP               NFP         
##  Min.   :-845.95   Min.   :-823.00  
##  1st Qu.:  28.14   1st Qu.:  22.75  
##  Median : 148.17   Median : 143.00  
##  Mean   :  85.87   Mean   :  87.48  
##  3rd Qu.: 213.44   3rd Qu.: 227.75  
##  Max.   : 383.98   Max.   : 524.00

In the below chart I use a 24-month rolling Granger Causality test to investigate the causality at a lag of one between ADP and NFP releases. The chart shows the P-values of the test which indicate in which way the causality,if any, flows. Clearly sometime the ADP has been a leading indicator, other times not.

plot of chunk causality

In the below I use an optimising algorithm to find the best ARIMA over the entire sample so as to generate a trend forecast of the NFP. The wide confidence intervals clearly highlight that those forecasts are associated with a high degree of of uncertainty.

plot of chunk arimachart

Finally I use a mixed model to generate an estimate of what the next NFP release will be. The forecast is derived both from a linear regression model forecast with the ADP as the independent variable and also from the forecast generated by the previously fitted ARIMA model.

The LM model forecasts an NFP release of : 136,313 whilts the ARIMA calls for a release of: 170,383 . This contributes to a mixed model forecast of : 152,164

Trade Weighted Currency Indices Stretch Map

Trade Weighted Currency Indices Report

Thu Oct 05 21:43:57 2017

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

Weekly Changes in G10 FX Trade Weighted Indices

I always liked boxplots. I think they provide a great and very visual way to position current data relative to their history whilst highlighting outliers. This is particularly useful as it helps to put recent moves in context of their past opportunities and possibly highly reversals and/or opportunities. To illustrate this I wrote a quick script in R to grab the BOE G10 Trade weighted indices from the website of the bank of England and posititon the most recent one week move relative to its history of weekly move going back to 1990.
The blue dots represent the most recent observations, the orange dots are the outliers over the period 1990 to date. The boxes emcompasses the observations that fall between the 25% and 75% quantiles. The Blue lines in the box are the median value over the sample and the “wiskers” represent an interval of close to 95%.

plot of chunk chartdata

Non Farm Payrolls time again…

It is NFP time again, sweepstakes must be rife on trading floors around the world…..So it is time to use my NFP forecasting model which leverages on both an ARIMA forecast and a simple linear regression using the ADP as the independent variable to generate a mixed forecast of the NFP.

Not surprisingly the ADP and the NFP data releases are positively correlated, thoug this has been significantly time varying. Also the NFP tend to be generally twice as volatile than the ADP numbers, highlighting their challenging nature for a forecaster.

plot of chunk chartsplot of chunk charts

##       ADP               NFP         
##  Min.   :-845.95   Min.   :-823.00  
##  1st Qu.:  26.54   1st Qu.:  22.50  
##  Median : 148.10   Median : 143.00  
##  Mean   :  85.11   Mean   :  87.33  
##  3rd Qu.: 212.59   3rd Qu.: 230.50  
##  Max.   : 383.98   Max.   : 524.00

In the below chart I use a 24-month rolling Granger Causality test to investigate the causality at a lag of one between ADP and NFP releases. The chart shows the P-values of the test which indicate in which way the causality,if any, flows. Clearly sometime the ADP has been a leading indicator, other times not.

plot of chunk causality

In the below I use an optimising algorithm to find the best ARIMA over the entire sample so as to generate a trend forecast of the NFP. The wide confidence intervals clearly highlight that those forecasts are associated with a high degree of of uncertainty.

plot of chunk arimachart

Finally I use a mixed model to generate an estimate of what the next NFP release will be. The forecast is derived both from a linear regression model forecast with the ADP as the independent variable and also from the forecast generated by the previously fitted ARIMA model.

The LM model forecasts an NFP release of : 236,915 whilts the ARIMA calls for a release of: 203,302 . This contributes to a mixed model forecast of : 221,283

Weekly Changes in G10 FX Trade Weighted Indices

I always liked boxplots. I think they provide a great and very visual way to position current data relative to their history whilst highlighting outliers. This is particularly useful as it helps to put recent moves in context of their past opportunities and possibly highly reversals and/or opportunities. To illustrate this I wrote a quick script in R to grab the BOE G10 Trade weighted indices from the website of the bank of England and posititon the most recent one week move relative to its history of weekly move going back to 1990.
The blue dots represent the most recent observations, the orange dots are the outliers over the period 1990 to date. The boxes emcompasses the observations that fall between the 25% and 75% quantiles. The Blue lines in the box are the median value over the sample and the “wiskers” represent an interval of close to 95%.

plot of chunk chartdata

Trade Weighted Currency Indices Stretch Map

Trade Weighted Currency Indices Report

Tue Aug 22 05:23:24 2017

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

Weekly Changes in G10 FX Trade Weighted Indices

I always liked boxplots. I think they provide a great and very visual way to position current data relative to their history whilst highlighting outliers. This is particularly useful as it helps to put recent moves in context of their past opportunities and possibly highly reversals and/or opportunities. To illustrate this I wrote a quick script in R to grab the BOE G10 Trade weighted indices from the website of the bank of England and posititon the most recent one week move relative to its history of weekly move going back to 1990.
The blue dots represent the most recent observations, the orange dots are the outliers over the period 1990 to date. The boxes emcompasses the observations that fall between the 25% and 75% quantiles. The Blue lines in the box are the median value over the sample and the “wiskers” represent an interval of close to 95%.

plot of chunk chartdata

Trade Weighted Currency Indices Stretch Map

Trade Weighted Currency Indices Report

Tue Jul 04 20:51:00 2017

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

G10 FX Implied Volatilities: Cheap or Expensive ?

The following report provides a granular analysis of implied volatilities within G10 FX. I use primarily the same formatting than for my G10FX positioning report to estimate how extended the 1-month FX implied volatilities are over various time horizon.

The first set of charts shows the historical T-stat of the 1-day changes in 1-month implied volatilities over a rolling period of 61-days. This is my statistical metric to quantify how stretched the implied volatilities are, but clearly other time period could be used as shown further down on in that report. The purple line represents the median value since 1996 and the red lines represent the 95% confidence intervals. Therefore if the value is above or below those the deviation of the given implied volatility should be deemed as atypical relative to what would be expected under a normal distribution (I am not saying that implied volatilities have a normal behaviour to be clear….) and therefore overbought/oversold.

plot of chunk stretch line chart

The below charts shows the current implied volatilities relative to their historical distributions since 1996. Once again the red lines delimit the 95% confidence intervals and the purple line the median value. The blue line indicates the most current level of 1-month implied volatility.

plot of chunk stretch distribution

Finally the below shows a stretch map of the T-Stats to help visualise how much implied volatilities have departed from their equilibrium levels over time horizons ranging from 1-month to 6-month. The bigger the square the most significant the observed upside (Green) or downside (Red) of the implied volatility over the given period.

plot of chunk stretch map

GBPUSD Implied Volatility Level and Steepness of Volatility Curve

I recently have read a paper titled The yen/dollar exchange rate in 1998: views from options markets written by the Bank of England back in November 1998. This got me to think of how I could best represent in one chart the relationship between the slope of the implied volatility term structure of and the nominal level o the 1-month volatility. In the following example I have applied this to the GBPUSD implied volatility as I thought this would be interesting to look at in the light of the forthcomint BREXIT negotiations and tonight elections….

Anyhow to do this, I regress the 1, 3, 6 and 12-month GBPUSD implied volatilies against their time values for the period 1996 to Mayr 2017 (i.e 5513 volatility curves). I derived the volatility curve slopes t_stats for each day and then classified the 1-month volatilities into three groups as a function of the significance level of the slope t-stats. The chart below shows the 1-month implied volatiliy over the full period. When the volatility curve slope was positvely significant at 95% critical threshold the data is shown in green, When there was a signicantly negative slope at the 95% critical threshold the data is shown in red and pale blue for the remainder. I think this is a neat way highlight that time of high volatility are associated with a volatility curve that slope downwardly and vice-versa.

plot of chunk stretch line chart

Below are the