Treasury Management Model with Foreign Exchange Exposure

Volosov, Konstantin; Mitra, Gautam; Spagnolo, Fabio; Lucas, Cormac

doi:10.1007/s10589-005-2059-2

Cited by 21 publications

(9 citation statements)

References 29 publications

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“…The paper that we find to be closest to ours is Volosov et al (2005), in which a stochastic programming model for hedging of foreign exchange exposure in a deterministic singlecurrency cash flow stream is formulated. Uncertainty is restricted to random fluctuations in future spot and forward rates.…”

Section: Literaturementioning

confidence: 92%

Corporate hedging: an answer to the “how” question

Blomvall

Ekblom

2017

Ann Oper Res

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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.

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Section: Literaturementioning

confidence: 92%

Corporate hedging: an answer to the “how” question

Blomvall

Ekblom

2017

Ann Oper Res

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“…Literature on SP closely related to this thesis are studies on scenario generation (Pflug, 2001;Dupačová et al, 2003;Høyland and Wallace, 2001;Høyland et al, 2003), hedging of currency risk (Volosov et al, 2005), and portfolio choice (ALM) problems (Carino et al, 1994;Consigli and Dempster, 1998;Kouwenberg, 2001;Fleten et al, 2002;Topaloglou et al, 2008;Ferstl and Weissensteiner, 2011). Other financial topics that have attracted interest include derivatives pricing (King, 2002;Pennanen and King, 2004;Topaloglou et al, 2008b), pricing problems in electricity markets (Pflug and Broussev, 2009;Kovacevic and Pflug, 2014), optimal choice of mortgage loans (Rasmussen and Clausen, 2007;Nielsen and Poulsen, 2004), and money management (Golub et al, 1995).…”

Section: Stochastic Programmingmentioning

confidence: 99%

Decision Making under Uncertainty in Financial Markets : Improving Decisions with Stochastic Optimization

Ekblom

2018

Linköping Studies in Science and Technology. Dissertations

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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 i Decision Making under Uncertainty in Financial Markets 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.

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“…Later, most of the work done uses the same assumptions as in the original models, particularly the Miller-Orr, differentiating by a stochastic modeling of the problem, as the research developed by Tapiero and Zuckerman (1980), Milbourne (1983), Hinderer and Waldman (2001), Baccarin (2002), Premachandra (2004), Volosov et al (2005), Liu and Xin (2008) and Baccarin (2009).…”

Section: Models For Cash Managementmentioning

confidence: 99%

Cash Management Policies by Evolutionary Models: a Comparison Using the Miller-Orr Model

Moraes

Nagano

2013

JISTEM USP

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This work aims to apply genetic algorithms (GA) and particle swarm optimization (PSO) to managing cash balance, comparing performance results between computational models and the Miller-Orr model. Thus, the paper proposes the application of computational evolutionary models to minimize the total cost of cash balance maintenance, obtaining the parameters for a cash management policy, using assumptions presented in the literature, considering the cost of maintenance and opportunity for cost of cash. For such, we developed computational experiments from cash flows simulated to implement the algorithms. For a control purpose, an algorithm has been developed that uses the Miller-Orr model defining the lower bound parameter, which is not obtained by the original model. The results indicate that evolutionary algorithms present better results than the Miller-Orr model, with prevalence for PSO algorithm in results.

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Treasury Management Model with Foreign Exchange Exposure

Cited by 21 publications

References 29 publications

Corporate hedging: an answer to the “how” question

Corporate hedging: an answer to the “how” question

Decision Making under Uncertainty in Financial Markets : Improving Decisions with Stochastic Optimization

Cash Management Policies by Evolutionary Models: a Comparison Using the Miller-Orr Model

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