pricing credit on thousands of names all -
our challenge for the next few months is to build an automated system to
provide differential pricing on thousands of credits [ 5 , 000 by year - end ] .
most of these credits will be illiquid in terms of market price information ,
making the challenge harder , and the end result more important in terms of
competitive pricing advantage . what we need is an overall strategy for how we
plan to achieve this from the quantitative perspective .
currently we have several models for credit pricing either in use or under
development :
fmc model ( default probability approach ) . using bloomberg ' s fair market ( par
yield ) curves , probabilities are generated from the risky - libor , then
default / bankruptcy swap prices computed using expectation methodology .
fmc model ( credit spread approach ) . using the fmcs , then directly taking the
libor credit spread at each tenor , adjusting for basis and compounding
differences .
bond model ( fmc approach ) . taking the fmcs as benchmark curves , the model
regresses the input bonds ( specific to a name ) on the two best fitting
benchmarks . the result is a zero yield curve with the same shape as the fmcs ,
but with the level tweaked for the specific issuer . prices are then generated
using both spread and probability approaches . under testing .
bond model ( spline approach ) . taking only the bonds specific to an issuer ,
the model fits an exponential cubic spline to the zero - coupon price curve ,
then builds a zero yield curve from this . under testing .
market prices . for certain liquid names , or sectors / ratings , cds market
prices are used , then recovery and event discount used to get bankruptcy swap
prices .
kmv . using expected default frequencies ( edfs ) from the kmv model and
database , we will build a model to price default swaps , making appropriate
risk adjustments . kmv is being installed now , so model will be worked on next .
each of these models returns a price ( credit default and bankruptcy ) , and the
accuracy of the price depends on many factors - liquidity and regulatory
differences between bond and cds markets , recovery assumptions , risk premia ,
capital charges , etc . the aim will be to accurately price as many liquid
names as possible , based upon these models , then use these prices , alongside
other financial information , as the backbone to a full automated pricing
system .
our inputs to the proposed pricing system for a specific name are model and
market prices for all issuers , alongside name - specific ' soft ' data from
credit reports and financial statements . if the credit is liquid enough , a
price will be generated from their own information only . otherwise , the
credit will be mapped onto a subset of liquid credits , with financial
information and historical price movements providing the mapping and weights .
the model price will then be periodically adjusted to align itself with
market ( or trader ) prices , and this adjustment will feed back into the
weighting and mapping composition . in loose terms , we could think of the
system price for an illiquid credit as being a weighted average of liquid
market prices ( bonds , equities , default swaps ) , where the weightings are
calibrated using credit analysis , financial ratios , etc .
the key steps to implementing such a system will be :
establishing what exactly we want to ' predict ' - is it a price , a rating , a
probability , or a score ? we will need a clean market history to calibrate to ,
which we only really have for ratings . we will then need to develop a mapping
from rating / score to price .
getting and cleaning the historical financial and credit data required to
calibrate the model .
building the mechanics of the model , ie , the calibration methodology . neural
nets / fuzzy logic seem the obvious candidates , but which exact methods and
software packages to use ?
determining an automated methodology for mapping names with limited
information into the model .
getting the " true " market price , in order to feed back an error . at present
such a price exists for very few credits .
allocating resources to the development . mckinsey claimed such a system would
take 6 - 10 man - months to develop .
further ideas or comments are requested , as we need to develop our strategy
asap . the model description above is fairly vague , as we don ' t yet have the
knowledge needed to fill in the specific details . further help will be
especially required on this if we are to continue to move at ' internet speed ' .
regards
ben
pricing credit on thousands of names
all -
our challenge for the next few months is to build an automated system to
provide differential pricing on thousands of credits [ 5 , 000 by year - end ] .
most of these credits will be illiquid in terms of market price information ,
making the challenge harder , and the end result more important in terms of
competitive pricing advantage . what we need is an overall strategy for how we
plan to achieve this from the quantitative perspective .
currently we have several models for credit pricing either in use or under
development :
fmc model ( default probability approach ) . using bloomberg ' s fair market ( par
yield ) curves , probabilities are generated from the risky - libor , then
default / bankruptcy swap prices computed using expectation methodology .
fmc model ( credit spread approach ) . using the fmcs , then directly taking the
libor credit spread at each tenor , adjusting for basis and compounding
differences .
bond model ( fmc approach ) . taking the fmcs as benchmark curves , the model
regresses the input bonds ( specific to a name ) on the two best fitting
benchmarks . the result is a zero yield curve with the same shape as the fmcs ,
but with the level tweaked for the specific issuer . prices are then generated
using both spread and probability approaches . under testing .
bond model ( spline approach ) . taking only the bonds specific to an issuer ,
the model fits an exponential cubic spline to the zero - coupon price curve ,
then builds a zero yield curve from this . under testing .
market prices . for certain liquid names , or sectors / ratings , cds market
prices are used , then recovery and event discount used to get bankruptcy swap
prices .
kmv . using expected default frequencies ( edfs ) from the kmv model and
database , we will build a model to price default swaps , making appropriate
risk adjustments . kmv is being installed now , so model will be worked on next .
each of these models returns a price ( credit default and bankruptcy ) , and the
accuracy of the price depends on many factors - liquidity and regulatory
differences between bond and cds markets , recovery assumptions , risk premia ,
capital charges , etc . the aim will be to accurately price as many liquid
names as possible , based upon these models , then use these prices , alongside
other financial information , as the backbone to a full automated pricing
system .
our inputs to the proposed pricing system for a specific name are model and
market prices for all issuers , alongside name - specific ' soft ' data from
credit reports and financial statements . if the credit is liquid enough , a
price will be generated from their own information only . otherwise , the
credit will be mapped onto a subset of liquid credits , with financial
information and historical price movements providing the mapping and weights .
the model price will then be periodically adjusted to align itself with
market ( or trader ) prices , and this adjustment will feed back into the
weighting and mapping composition . in loose terms , we could think of the
system price for an illiquid credit as being a weighted average of liquid
market prices ( bonds , equities , default swaps ) , where the weightings are
calibrated using credit analysis , financial ratios , etc .
the key steps to implementing such a system will be :
establishing what exactly we want to ' predict ' - is it a price , a rating , a
probability , or a score ? we will need a clean market history to calibrate to ,
which we only really have for ratings . we will then need to develop a mapping
from rating / score to price .
getting and cleaning the historical financial and credit data required to
calibrate the model .
building the mechanics of the model , ie , the calibration methodology . neural
nets / fuzzy logic seem the obvious candidates , but which exact methods and
software packages to use ?
determining an automated methodology for mapping names with limited
information into the model .
getting the " true " market price , in order to feed back an error . at present
such a price exists for very few credits .
allocating resources to the development . mckinsey claimed such a system would
take 6 - 10 man - months to develop .
further ideas or comments are requested , as we need to develop our strategy
asap . the model description above is fairly vague , as we don ' t yet have the
knowledge needed to fill in the specific details . further help will be
especially required on this if we are to continue to move at ' internet speed ' .
regards
ben