1. Credit Risk - Market Risk Credit Risk is the risk of the other side not paying! How do we typically measure it? How do we manage it? –Pledged collateral, posted margin, loss reserves, hedging with CDS You can think of credit risk as either: –Issuer Risk –Counterparty Risk –Counterparties are sometimes Issuers… Probability of Default : “PD” Loss Given Default : “LGD” Expected Loss : “EL” Formula we will implement: EL = PD * LGD -Lookup PD and LGD’s by quality code (see SBB_ratings.h/cc) -A type of credit risk sensitivity: -How does my credit risk change on a ratings downgrade?

2. Measuring day-over-day change We can now calculate risk at a point in time for a given book –Credit : Expected loss –Market : Interest rate sensitivity However, the risk changes daily given trading activity and changing market environment… –What is the new market environment (yield curve, etc., )? –What is the new position? –What is the new risk? –Sensitivities change as well as position amounts and valuations We need an itemized bill: –“Risk Attribution” –“PnL Attribution” (How did I make/lose money?)

3. Deliverables for Nov 21 (you’ll have 2 weeks for this) Calculate Expected Loss for the book Calculate Expected Loss given a ratings downgrade scenario: –AA to A –BBB to BB (Investment grade crosses over to junk) Calculate change in position between two days (search on security ID) Build a results output class to support your GUI –Hack at existing SBB_io.h/.cc file class –Output format your client can parse By this date you should be able to: –Load a position file and price by either spread or yield –Calculate scenarios by shifting the yield curve –Show how risk has changed between two days –Programmatically parse the output of your “server” side –Have your GUI wiggling (specific requirements to follow - questions to answer…) Next quiz is Nov 21 –Will cover the remainder of MMM (chapter 13 to end)! Grades for mid-term will be distributed next class (Nov 16)