Introduction to Credit Risk Credit Risk vs. Market Risk Credit Risk is the risk of the other side not paying (all or part)! This is a “default”… –Failure to pay an interest amount or return all of principal amount How do we typically measure it? How do we manage it? –Pledged collateral, posted margin, keep loss reserves, hedging with CDS You can think of “credit risk” as: –Issuer Risk –Counterparty Risk –Settlement Risk (the time it takes to get your asset/cash or until it’s “inventory”) –Counterparties are sometimes Issuers… Probability of Default : “PD” Loss Given Default : “LGD” Expected Loss : “EL” Simplified Formula : EL = PD * LGD -Look up LGD’s by quality code (in SBB_ratings.h/cc - an example is ifdef’d) -LGD is a fraction so the total LGD of the position = Amount * LGD -This is the amount we would lose if counterparty defaulted -A type of credit risk sensitivity: -How does my credit risk change on a ratings downgrade?

Mid-Term Submission Requirements An executable which will build and run on either Linux or Mac using GNU Deliver an archive tar file named “yourname.tar” - create using: “tar cvf yourname.tar.” in your directory Build by typing “make all”, launch with shell script “run.sh” Direct debug to stderr, file descriptor 2 (see run.sh) Encapsulate your main with “START_TIMER() and END_TIMER() Price and calculate for each for bond position in file –Given YIELD or SPREAD, compute price, dv01 and risk –Handle both coupon bearing and zero coupon types (key off coupon value - either zero or non-zero) Our definition of “risk” is: “dv01/100 * amount” - dv01 is in percent and amount is in thousands Search positions in our portfolio according to below criteria and write out to separate file (“results.txt”) 1.Largest long position (write out the Amount) 2.Largest short position (write out the Amount) 3.Position with most risk (can be either long/positive or short/negative - write out the Risk number 4.Total risk for the whole portfolio (write out the total Risk number) –- Format of results.txt: 4 numbers per in order above 1-4. See example file “results.txt” on site Load new input files called: “tradingbook.txt” and “yieldcurve.txt” –For spread priced bonds find closest maturity treasury bond. On a tie choose shorter maturity treasury. Required output format –For the individual bond results append to the end of each line read in from tradingbook.txt E.g., “[inputs] Price dv01 risk LGD” LGD will be adjusted by amount (like risk) Exclude # lines (comments) –For portfolio measures write out 4 numbers (one per line) to file “results.txt” –The timer output writes out to stdout by default so: Execute run.sh to call your executable and the only thing displayed should be the timer output. You must call run.sh to run your executable! Units: –Show 3 decimal places for all measures –Amounts will be in thousands - 1,000 means 1 million –See code example for Risk calculation

Our “Risk” Measures Position adjusted sensitivity using 1 basis point change Example of “bond risk”: Price (pv): (that’s a total amount of $ the bond is worth) DV01: (how much price moves with a 1/100 of 1% change in yield =.01) amount: (10,000 is 10MM “position”) Position risk: (that’s $9,251 change to position value given a 1bp move) static double bond_risk( const SBB_bond_calculator_interface* bond_calc_ptr, const SBB_instrument_fields* bond_record_ptr, double dv_bump_amount) { double pv = bond_calc_ptr->PV(bond_record_ptr->Yield() ); double dv = bond_calc_ptr->dv_bump(bond_record_ptr->Yield(), pv, dv_bump_amount); double position_risk = bond_record_ptr->Amount() * dv/100.0; return position_risk; } Above is our “market” risk measure For credit risk we will use “Loss Given Default” Two measures to assess risk: one for market and one for credit

Upcoming deliverables… No class Oct 14 – fall break Mid-term submission day Oct 28 –First phase submission of project on thumb or google drive (done individually) –Have teams formed for upcoming phases (from here on we will work in teams) 1.Surgeon, Tester? 2.Client-side/server side? 3.Post Mid-term next graded phase will be a test harness client-side driver that hits your server One week after mid-term submission –Second quiz of MMM chapters 7-13 Two weeks after mid term lecture : Server built with version 1 of your message set defined Stub client (could use python to have a scripted test driver) Your regression harness is essentially your executable loading tradingbook.txt with answers inserted The tradingbook.txt and results.txt file will be posted on class page after mid term submission Submission will be a server and a client The client will write results to stdout Specification of results to follow…