The goal of these lectures is to present a survey of recent
developments in the area of mathematical modelling of credit risk.
Credit risk, embedded in a financial transaction, is the risk that
at least one of the parties involved in the transaction will
suffer a financial loss due to default or decline in the
creditworthiness of either the counter-party to the transaction or
some reference credit name.
The most extensively studied form of
credit risk is the {\it default risk} -- that is, the risk that a
counterparty in a financial contract will not fulfil a contractual
commitment to meet her/his obligations stated in the contract. For
this reason, the main tool in the area of credit risk modelling is
a judicious specification of the random time of default. Our main
goal is to present the most important mathematical tools that are
used for the arbitrage valuation of defaultable claims, which are
usually referred to as {\it credit derivatives}. We also examine
the important issue of hedging for typical credit derivatives. We
will first provide a concise summary of the main developments
within the so-called ``structural approach'' to modelling and
valuation of credit risk. In particular, we will study the
first-passage-time approach and a random barrier case. The next
part of the lectures will be devoted to the ``reduced-form
approach''. This approach is purely probabilistic in nature and,
technically speaking, it has a lot in common with the reliability
theory. Subsequently, we will examine hedging strategies under the
assumption that some defaultable assets (such as, e.g., corporate
bonds or credit default swaps) are traded. Finally, we will give a
brief survey of practical methods that are currently used in
modelling of dependent defaults and credit migrations, and we will
discuss valuation and hedging of multi-name credit derivatives,
such as: credit default index swaps and synthetic collateralized
debt obligations.