Microservices: FRTB Modellable Risk Factors

  • FRTB regulations specify that non-modellable risk factors are subject to stressed capital add-ons
  • For a risk factor to be modellable it must pass a specific test for continuously available real prices
  • The Clarus API provides functions for the risk factor modellability test for OTC Derivatives
  • These functions are very easy to call from many popular languages, including Python, R, Julia, C++, and Java.
  • Many related functions are available, see our API documentation.

FRTB Modellable test

The specific test for continuously available real prices is defined as follows.

  • a risk factor must have at least 24 observable real prices per year
  • with a maximum period of one month between two consecutive observations
  • the above criteria must be assessed on a monthly basis
  • and measured over the current ES period (e.g. past 12 months)

For further details, see FRTB – Modellable Risk Factors and Non-Modellable.

Calling directly in the browser

To appreciate the ease with which the function may be called, our first example does not even involve writing code, as the function can be called directly in the browser!

This URL is enough to call the function and see its results.
https://api.clarusft.com/rest/v1/frtb/modellablerf.html?riskfactors=USSW2,USSW3,USSW4,USSW5

To ease the exposition, we set the riskfactors parameter to a list of Bloomberg tickers for USD Swaps. To be able to click the URL and run yourself, you will need to enter a username/password the first time (obtained by registering here). The output is below and shows that all of these risk factors pass the modellable test.

RiskFactor  Ticker    Modellable  Count  Gap  Comment
USSW2       USSW2     TRUE         6029  
USSW3       USSW3     TRUE         5764
USSW4       USSW4     TRUE         2177
USSW5       USSW5     TRUE        12264 

Calling from Python

To do the same test from Python for all the risk factors that we may want on a USD Libor 3M curve, we can load a list of riskfactor names from a file and perform the modellability test using the code below.

import clarus

clarus.ApiConfig.api_key = ''
clarus.ApiConfig.api_secret = ''

mylist = open('riskfactors.txt').read()
print (clarus.frtb.modellablerf(riskfactors=mylist)) 

This code is just a few lines and relies on the  Clarus python module (which relies on the popular Requests library) and the key/secret can be obtained by registering here. The results produced are as follows.

RiskFactor  Ticker    Modellable  Count  Gap  Comment
USSW1       USSW1     TRUE         1112
USSW2       USSW2     TRUE         6029  
USSW3       USSW3     TRUE         5764
USSW4       USSW4     TRUE         2177  
USSW5       USSW5     TRUE        12264 
USSW6       USSW6     TRUE         1410 
USSW7       USSW7     TRUE         3132 
USSW8       USSW8     TRUE         1142 
USSW9       USSW9     TRUE         1273
USSW10      USSW10    TRUE        19281
USSW11      USSW11    TRUE           76
USSW12      USSW12    TRUE         1029            
USSW13      USSW13    FALSE          25   81  First trade not within a month of start
USSW14      USSW14    FALSE          30   61  Gap of greater than a month found
USSW15      USSW15    TRUE         1877
USSW20      USSW20    TRUE         2098
USSW25      USSW25    TRUE          817
USSW30      USSW30    TRUE         8887
USSW40      USSW40    TRUE           97

Showing that almost all these risk factors pass the test (modellable column equal true) and giving the count of trades in the past year for that risk factor. Two risk factors 13Y and 14Y fail the test, not because we do not have at least 24 trades in the year, but because the largest gap between consecutive trades is greater than one month at 81 calendar days and 61 calendar days respectively.

Evidencing the Modellable test

There is also a function to evidence the modellable test by returning the list of trades used, which can be called using the code snippet below for one risk factor.

response = clarus.frtb.modellablerftrades(riskfactor='USSW11'))
print (response) 

The first few rows of results produced by this code are:

TICKER TIMESTAMP              STATUS VENUE  PRICE  NOTIONAL    SDR
USSW11 2016-03-31 11:24:09.0  C      On     1.74   5000000.0   BBG
USSW11 2016-04-07 08:27:25.0  C      On     1.664  1.0E7       BBG
USSW11 2016-04-08 02:13:58.0  C      On     1.648  1.0E7       BBG
USSW11 2016-04-11 15:28:58.0  C      On     1.675  5000000.0   BBG
USSW11 2016-04-12 15:56:32.0  C      On     1.7012 7000000.0   BBG
USSW11 2016-04-27 19:36:57.0  C      On     1.822  2.5E7       BBG
USSW11 2016-04-29 12:20:28.0  C      On     1.807  3.9E7       BBG
USSW11 2016-05-02 13:20:14.0  C      On     1.7971 3.9E7       BBG
USSW11 2016-05-06 14:24:31.0  C      On     1.685  1.2E8      DTCC
USSW11 2016-05-23 13:32:10.0  C      On     1.7625 3000000.0  DTCC
....

Showing each trades execution time stamp, whether it is cleared (c) or uncleared, the execution venue type (On or Off SEF), the price, notional and the SDR it was reported to.

More Risk Factors

Next lets try USD Libor 1M v 3M Basis Swap factors by running the function with the appropriate ticker list.

RiskFactor  Ticker    Modellable  Count  Gap  Comment
USBA1       USBA1     TRUE        251 
USBA2       USBA2     TRUE        304 
USBA3       USBA3     TRUE        466
USBA5       USBA5     TRUE        965
USBA10      USBA10    TRUE        710 
USBA15      USBA15    TRUE        150 
USBA20      USBA20    TRUE        102 
USBA30      USBA30    TRUE        133 

Showing the tenors (1Y to 30Y) that we can use for this curve.

There are also other USD Basis curves to try e.g. Libor 3M vs 6M and USD FedFunds.

Followed by other currencies and cross currency basis.

Followed by Swaption volatilities.

That should cover the majority of risk factors needed for an IRD Trading Desk.

A lot of data to look at and establish the set of risk factors useable in an Internal Model.

And importantly ensure these are not subject to expensive Stressed Capital Addons.

If you are interested in such an exercise, please contact us.

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