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  • 1.
    Blomvall, Jörgen
    et al.
    Linköping University, Department of Management and Engineering, Production Economics. Linköping University, Faculty of Science & Engineering.
    Hagenbjörk, Johan
    Linköping University, Department of Management and Engineering, Production Economics. Linköping University, Faculty of Science & Engineering.
    A generic framework for monetary performance attribution2019In: Journal of Banking & Finance, ISSN 0378-4266, E-ISSN 1872-6372, Vol. 105, p. 121-133Article in journal (Refereed)
    Abstract [en]

    We propose a generic framework for performance attribution in monetary terms. Through a second-order Taylor approximation, the changes in portfolio value are attributed to a set of systematic risk factors. By considering two error terms arising from the Taylor approximation, combined with an exact definition of the carry term, we derive a residual-free performance attribution framework, where we exert control over the size of the error terms. The framework incorporates foreign exchange rates and transaction costs, which is illustrated by simulating a European investor acting on the U.S. fixed income market. For the out-of-sample period, we show that we can attribute almost all portfolio value differences and variance using six risk factors obtained from principal component analysis. The results show that our method, in combination with high-quality estimates of risk factors, outperforms other fixed-income attribution models from the literature. (C) 2019 Elsevier B.V. All rights reserved.

    The full text will be freely available from 2022-05-26 12:30
  • 2.
    Hagenbjörk, Johan
    Linköping University, Department of Management and Engineering, Production Economics. Linköping University, Faculty of Science & Engineering.
    Optimization-Based Models for Measuring and Hedging Risk in Fixed Income Markets2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    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. 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.

    Measuring and managing risk is closely connected to the measurement of the underlying financial quantities. Through a data-driven method, we can show that six systematic risk factors can be used to explain almost all variance in the interest rate curves. By modeling the dynamics of these six risk factors, possible outcomes can be simulated in the form of term structure scenarios. For short-term simulation horizons, this results in a representation of the portfolio value distribution that is consistent with the realized outcomes from historically observed term structures. This enables more accurate measurements of interest rate risk, where our proposed method exhibits both lower risk and lower pricing errors compared to traditional models.

    We propose a method for decomposing changes in portfolio values for an arbitrary portfolio into the risk factors that affect the value of each instrument. By demonstrating the method for the six systematic risk factors identified for the interbank market, we show that almost all changes in portfolio value and portfolio variance can be attributed to these risk factors. Additional risk factors and approximation errors are gathered into two terms, which can be studied to ensure the quality of the performance attribution, and possibly improve it.

    To eliminate undesired risk within trading books, banks use hedging. Traditional methods do not take transaction costs into account. We, therefore, propose a method for managing the risks in the interbank market through a stochastic optimization model that considers transaction costs. This method is based on a scenario approximation of the optimization problem where the six systematic risk factors are simulated, and the portfolio variance is weighted against the transaction costs. This results in a method that is preferred over the traditional methods for all risk-averse investors.

    For the credit market, we use data from the bond market in combination with the interbank market to make accurate measurements of the financial quantities. We address the notoriously difficult problem of separating default risk from recovery risk. In addition to the previous identified six systematic risk factors for risk-free interests, we identify four risk factors that explain almost all variance in default intensities, while a single risk factor seems sufficient to model the recovery risk. Overall, this is a higher number of risk factors than is usually found in the literature. Through a simple model, we can measure the variance in bond prices in terms of these systematic risk factors, and through performance attribution, we relate these values to the empirically realized variances from the quoted bond prices.

    List of papers
    1. Simulation and evaluation of the distribution of interest rate risk
    Open this publication in new window or tab >>Simulation and evaluation of the distribution of interest rate risk
    2019 (English)In: Computational Management Science, ISSN 1619-697X, E-ISSN 1619-6988, Vol. 16, no 1-2, p. 297-327Article in journal (Refereed) Published
    Abstract [en]

    We study methods to simulate term structures in order to measure interest rate risk more accurately. We use principal component analysis of term structure innovations to identify risk factors and we model their univariate distribution using GARCH-models with Student’s t-distributions in order to handle heteroscedasticity and fat tails. We find that the Student’s t-copula is most suitable to model co-dependence of these univariate risk factors. We aim to develop a model that provides low ex-ante risk measures, while having accurate representations of the ex-post realized risk. By utilizing a more accurate term structure estimation method, our proposed model is less sensitive to measurement noise compared to traditional models. We perform an out-of-sample test for the U.S. market between 2002 and 2017 by valuing a portfolio consisting of interest rate derivatives. We find that ex-ante Value at Risk measurements can be substantially reduced for all confidence levels above 95%, compared to the traditional models. We find that that the realized portfolio tail losses accurately conform to the ex-ante measurement for daily returns, while traditional methods overestimate, or in some cases even underestimate the risk ex-post. Due to noise inherent in the term structure measurements, we find that all models overestimate the risk for 10-day and quarterly returns, but that our proposed model provides the by far lowest Value at Risk measures.

    Place, publisher, year, edition, pages
    New York: Springer Publishing Company, 2019
    Keywords
    Interest rate risk, Principal component analysis, Term structure, Value at Risk
    National Category
    Probability Theory and Statistics
    Identifiers
    urn:nbn:se:liu:diva-151604 (URN)10.1007/s10287-018-0319-8 (DOI)000458627300013 ()2-s2.0-85048050404 (Scopus ID)
    Available from: 2019-03-12 Created: 2019-03-12 Last updated: 2019-12-08Bibliographically approved
    2. A generic framework for monetary performance attribution
    Open this publication in new window or tab >>A generic framework for monetary performance attribution
    2019 (English)In: Journal of Banking & Finance, ISSN 0378-4266, E-ISSN 1872-6372, Vol. 105, p. 121-133Article in journal (Refereed) Published
    Abstract [en]

    We propose a generic framework for performance attribution in monetary terms. Through a second-order Taylor approximation, the changes in portfolio value are attributed to a set of systematic risk factors. By considering two error terms arising from the Taylor approximation, combined with an exact definition of the carry term, we derive a residual-free performance attribution framework, where we exert control over the size of the error terms. The framework incorporates foreign exchange rates and transaction costs, which is illustrated by simulating a European investor acting on the U.S. fixed income market. For the out-of-sample period, we show that we can attribute almost all portfolio value differences and variance using six risk factors obtained from principal component analysis. The results show that our method, in combination with high-quality estimates of risk factors, outperforms other fixed-income attribution models from the literature. (C) 2019 Elsevier B.V. All rights reserved.

    Place, publisher, year, edition, pages
    ELSEVIER SCIENCE BV, 2019
    Keywords
    Performance attribution; Performance analysis; Fixed income
    National Category
    Economics
    Identifiers
    urn:nbn:se:liu:diva-158925 (URN)10.1016/j.jbankfin.2019.05.021 (DOI)000472698500009 ()
    Available from: 2019-07-20 Created: 2019-07-20 Last updated: 2020-01-15
  • 3.
    Hagenbjörk, Johan
    et al.
    Linköping University, Department of Management and Engineering, Production Economics. Linköping University, Faculty of Science & Engineering.
    Blomvall, Jörgen
    Linköping University, Department of Management and Engineering, Production Economics. Linköping University, Faculty of Science & Engineering.
    Simulation and evaluation of the distribution of interest rate risk2019In: Computational Management Science, ISSN 1619-697X, E-ISSN 1619-6988, Vol. 16, no 1-2, p. 297-327Article in journal (Refereed)
    Abstract [en]

    We study methods to simulate term structures in order to measure interest rate risk more accurately. We use principal component analysis of term structure innovations to identify risk factors and we model their univariate distribution using GARCH-models with Student’s t-distributions in order to handle heteroscedasticity and fat tails. We find that the Student’s t-copula is most suitable to model co-dependence of these univariate risk factors. We aim to develop a model that provides low ex-ante risk measures, while having accurate representations of the ex-post realized risk. By utilizing a more accurate term structure estimation method, our proposed model is less sensitive to measurement noise compared to traditional models. We perform an out-of-sample test for the U.S. market between 2002 and 2017 by valuing a portfolio consisting of interest rate derivatives. We find that ex-ante Value at Risk measurements can be substantially reduced for all confidence levels above 95%, compared to the traditional models. We find that that the realized portfolio tail losses accurately conform to the ex-ante measurement for daily returns, while traditional methods overestimate, or in some cases even underestimate the risk ex-post. Due to noise inherent in the term structure measurements, we find that all models overestimate the risk for 10-day and quarterly returns, but that our proposed model provides the by far lowest Value at Risk measures.

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