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Ekblom, Jonas
Publications (3 of 3) Show all publications
Blomvall, J. & Ekblom, J. (2018). Corporate Hedging: an answer to the "how" question. Annals of Operations Research, 266(1-2), 35-69
Open this publication in new window or tab >>Corporate Hedging: an answer to the "how" question
2018 (English)In: Annals of Operations Research, ISSN 0254-5330, E-ISSN 1572-9338, Vol. 266, no 1-2, p. 35-69Article in journal (Refereed) Published
Abstract [en]

We develop a stochastic programming framework for hedging currency and interest rate risk, with market traded currency forward contracts and interest rate swaps, in an environment with uncertain cash flows. The framework captures the skewness and kurtosis in exchange rates, transaction costs, the systematic risks in interest rates, and most importantly, the term premia which determine the expected cost of different hedging instruments. Given three commonly used objective functions: variance, expected shortfall, and mean log profits, we study properties of the optimal hedge. We find that the choice of objective function can have a substantial effect on the resulting hedge in terms of the portfolio composition, the resulting risk and the hedging cost. Further, we find that unless the objective is indifferent to hedging costs, term premia in the different markets, along with transaction costs, are fundamental determinants of the optimal hedge. Our results also show that to reduce risk properly and to keep hedging costs low, a rich-enough universe of hedging instruments is critical. Through out-of-sample testing we validate the findings of the in-sample analysis, and importantly, we show that the model is robust enough to be used on real market data. The proposed framework offers great flexibility regarding the distributional assumptions of the underlying risk factors and the types of hedging instruments which can be included in the optimization model.

Place, publisher, year, edition, pages
New York, United States: Springer-Verlag New York, 2018
Stochastic programming, Currency hedging, Term premia, Uncertain cash flows, Risk management
National Category
urn:nbn:se:liu:diva-142117 (URN)10.1007/s10479-017-2645-6 (DOI)000433953200003 ()2-s2.0-85032818917 (Scopus ID)
Available from: 2017-10-23 Created: 2017-10-23 Last updated: 2018-10-05Bibliographically approved
Ekblom, J. (2018). Decision Making under Uncertainty in Financial Markets: Improving Decisions with Stochastic Optimization. (Doctoral dissertation). Linköping: Linköping University Electronic Press
Open this publication in new window or tab >>Decision Making under Uncertainty in Financial Markets: Improving Decisions with Stochastic Optimization
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis addresses the topic of decision making under uncertainty, with particular focus on financial markets. The aim of this research is to support improved decisions in practice, and related to this, to advance our understanding of financial markets. Stochastic optimization provides the tools to determine optimal decisions in uncertain environments, and the optimality conditions of these models produce insights into how financial markets work. To be more concrete, a great deal of financial theory is based on optimality conditions derived from stochastic optimization models. Therefore, an important part of the development of financial theory is to study stochastic optimization models that step-by-step better capture the essence of reality. This is the motivation behind the focus of this thesis, which is to study methods that in relation to prevailing models that underlie financial theory allow additional real-world complexities to be properly modeled.

The overall purpose of this thesis is to develop and evaluate stochastic optimization models that support improved decisions under uncertainty on financial markets. The research into stochastic optimization in financial literature has traditionally focused on problem formulations that allow closed-form or `exact' numerical solutions; typically through the application of dynamic programming or optimal control. The focus in this thesis is on two other optimization methods, namely stochastic programming and approximate dynamic programming, which open up opportunities to study new classes of financial problems. More specifically, these optimization methods allow additional and important aspects of many real-world problems to be captured.

This thesis contributes with several insights that are relevant for both financial and stochastic optimization literature. First, we show that the modeling of several real-world aspects traditionally not considered in the literature are important components in a model which supports corporate hedging decisions. Specifically, we document the importance of modeling term premia, a rich asset universe and transaction costs. Secondly, we provide two methodological contributions to the stochastic programming literature by: (i) highlighting the challenges of realizing improved decisions through more stages in stochastic programming models; and (ii) developing an importance sampling method that can be used to produce high solution quality with few scenarios. Finally, we design an approximate dynamic programming model that gives close to optimal solutions to the classic, and thus far unsolved, portfolio choice problem with constant relative risk aversion preferences and transaction costs, given many risky assets and a large number of time periods.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2018. p. 36
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1956
Stochastic programming, Approximate dynamic programming, Financial optimization, Portfolio optimization, Corporate hedging, Scenario generation, Importance sampling
National Category
Economics Other Mathematics
urn:nbn:se:liu:diva-151237 (URN)10.3384/diss.diva-151237 (DOI)9789176852026 (ISBN)
Public defence
2018-10-19, ACAS, A-huset, Campus Valla, Linköping, 10:15 (Swedish)
Available from: 2018-09-13 Created: 2018-09-13 Last updated: 2019-09-30Bibliographically approved
Blomvall, J., Ekblom, J. & Ndengo, M.Estimating U.S. Treasury Yield Curves By A Generalized Optimization Framework.
Open this publication in new window or tab >>Estimating U.S. Treasury Yield Curves By A Generalized Optimization Framework
(English)Manuscript (preprint) (Other academic)
Abstract [en]

We show that traditional data sets for the U.S. Treasury yield curves contain large amounts of noise, in e.g. the Fama-Bliss discount file already the second factor loading for innovations in forward rates is a consequence of noise. We implement the quadratic and cubic McCulloch splines, Nelson-Siegel and Svensson models and compare these traditional models with a recently developed generalized optimization framework using daily CRSP data from 1961 to 2011. In out-of-sample tests, it is shown that the generalized optimization framework produces smaller pricing errors compared to the traditional methods. Factor loadings from the generalized optimization framework show that the short and long end of the forward rate curve move independently, where principal component 1-3 explain the long end, and subsequent principal components explain the short end. This is consistent with the behavior of the market where short rates are governed by central bank while long rates are dependent on e.g. the expectation of future inflation.

structure estimation, U.S. Treasury, Principal component analysis, Forward rates
National Category
urn:nbn:se:liu:diva-97407 (URN)
Available from: 2013-09-12 Created: 2013-09-12 Last updated: 2013-09-12Bibliographically approved

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