Optimization-Based Models for Measuring and Hedging Risk in Fixed Income Markets
Linköping studies in science and technology, Dissertations
The global fixed income market is an enormous financial market whose value by far exceeds that of the public stock markets. The interbank market consists of interest rate derivatives, whose primary purpose is to manage interest rate risk. The credit market primarily consists of the bond market, which links investors to companies, institutions, and governments with borrowing needs. This dissertation takes an optimization perspective upon modeling both these areas of the fixed-income market.
... income market. Legislators on the national markets require financial actors to value their financial assets in accordance with market prices. Thus, prices of many assets, which are not publicly traded, must be determined mathematically. The financial quantities needed for pricing are not directly observable but must be measured through solving inverse optimization problems. These measurements are based on the available market prices, which are observed with various degrees of measurement noise. For the interbank market, the relevant financial quantities consist of term structures of interest rates, which are curves displaying the market rates for different maturities. For the bond market, credit risk is an additional factor that can be modeled through default intensity curves and term structures of recovery rates in case of default. By formulating suitable optimization models, the different underlying financial quantities can be measured in accordance with observable market prices, while conditions for economic realism are imposed. xi III Blomvall proposed the research idea and the choice of method based on the master thesis by Uhrberg and Sjöholm (2013) , who used another approach. Hagenbjörk implemented the idea with guidance from xiii Optimization-Based Models for Measuring and Hedging Risk in Fixed Income Markets Blomvall, who also provided help finding the problems that made the hedge unstable. Blomvall also wrote the solver. Hagenbjörk wrote the paper and independently developed the stochastic dominance tests. Besides giving feedback on the paper, Blomvall contributed by writing the discussion part. IV Blomvall proposed the research topic, which was an extension of the master thesis by Hagenbjörk. Hagenbjörk found the suitable mathematical way to model credit risk, where Blomvall contributed with some critical pieces. Blomvall provided an AMPL implementation for measuring multiple term structures that Hagenbjörk modified and extended with the credit risk part. Hagenbjörk was responsible for setting up the data-retrieval from EIKON and constructed the MySQL database. Hagenbjörk was responsible for writing the paper. xiv