Chapter 22 Duality Theory and Approximate Dynamic Programming for Pricing American Options and Portfolio Optimization

Haugh, Martin B.; Kogan, Leonid

doi:10.1016/s0927-0507(07)15022-2

Cited by 14 publications

(9 citation statements)

References 24 publications

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“…Non-trivial redeployment costs (S > 0) make diversification path-dependent, precluding the analytical identification of the policy function (Haugh & Kogan, 2007). That issue is resolved with the simulation-based technique of Brandt et al (2005) illustrated in Van Binsbergen and Brandt (2007).…”

Section: Identification Of Corporate Diversification Choicesmentioning

confidence: 99%

“…Rooted in the general principle of dynamic optimality (Bellman, ), the model identifies the sequence of a firm's scope choices. The similarity between such choices and the allocation of wealth across securities by investors (Merton, ) enables the use of the simulation‐based portfolio selection technique of Brandt, Goyal, Santa‐Clara, and Stroud (), that resolves the challenges of the informal analysis and the analytical intractability of scope choices in path‐dependent setting (Haugh & Kogan, ).…”

Section: Introductionmentioning

confidence: 99%

“…selection technique of Brandt, Goyal, Santa-Clara, and Stroud (2005), that resolves the challenges of the informal analysis and the analytical intractability of scope choices in path-dependent setting (Haugh & Kogan, 2007). The model derives several novel results, listed in Table 1.…”

Section: Introductionmentioning

confidence: 99%

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Economies of Scope, Resource Relatedness, and the Dynamics of Corporate Diversification

Sakhartov

2017

Strat. Mgmt. J

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Research summary: The dominant view has been that businesses that are more related to each other are more often combined within diversified firms. This study uses a dynamic model to demonstrate that, with inter‐temporal economies of scope, diversified firms are more likely to combine moderately related businesses than the most‐related businesses. That effect occurs because strong relatedness reduces redeployment costs and makes firms redeploy all resources to better performing businesses. The strength of that effect depends on inducements for redeployment measured as the current return advantage of one business over another business, volatilities of business returns, and correlation of those returns. This study develops hypotheses for those relationships and suggests empirical operationalizations, encouraging empiricists to retest the implications of relatedness for the dynamics of corporate diversification. Managerial summary: It is believed that diversified firms are more likely to combine more‐related businesses because relatedness enables sharing of resources between businesses. Indeed, a firm can apply knowledge created in one business to another business, avoiding costly duplication in knowledge development. Resource sharing also adds value when a firm offers several products, adding the convenience of one‐stop shopping and charging higher prices. However, resource sharing is not the only motivation for corporate diversification. In environments where profitability of businesses changes frequently, firms diversify by redeploying part of resources from an underperforming business to a better performing business. This study uses a dynamic model to demonstrate that, with that second motivation for corporate diversification, firms end up combining moderately related businesses rather than the most‐related businesses. Copyright © 2017 John Wiley & Sons, Ltd.

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Section: Identification Of Corporate Diversification Choicesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Economies of Scope, Resource Relatedness, and the Dynamics of Corporate Diversification

Sakhartov

2017

Strat. Mgmt. J

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“…Calculation of

V_{0}^{O}

and

V_{0}^{K, O}

is not feasible analytically (Broadie and Detemple, : 1163; Haugh and Kogan, : 926) because redeployment is allowed at any time before T (making O an American Type option) and involves costs. To find

V_{0}^{O}

and

V_{0}^{K, O}

, we use an efficient and popular numerical complement to the specified analytical model — the binomial lattice method of Cox et al () .…”

Section: Formal Model Of Redeployability and Synergymentioning

confidence: 99%

Resource relatedness, redeployability, and firm value

2013

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Our paper elaborates the effects of resource relatedness on value of a multibusiness firm. We emphasize that value results from interplay of benefits of synergy and redeployability. This view, considering how synergy and redeployability interact in determining value, extends prior separate considerations of the two benefits. We also diagnose that the value effect of resource relatedness is contingent on uncertainty and specify this contingent relationship. We use the real option valuation approach and formally evaluate the impacts of the two effects of relatedness. This explication enables us to demonstrate how redeployability contributes to value beyond synergy, and how they contribute in tandem. In this sense, we illuminate previously undiagnosed value in multibusiness firms. Beyond theoretical implications, our results have important empirical and managerial implications.

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“…The approximate nature of ADP raises the issue of solution quality, and consequently, a need for methods to bound the distance to optimality. The key tools for this purpose are from duality theory, which has been extended into stochastic dynamic settings by for example (Rogers, 2002;Andersen and Broadie, 2004;Haugh et al, 2006;Haugh and Kogan, 2007;Brown et al, 2010;Brown and Smith, 2011).…”

Section: Approximate Dynamic 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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Chapter 22 Duality Theory and Approximate Dynamic Programming for Pricing American Options and Portfolio Optimization

Cited by 14 publications

References 24 publications

Economies of Scope, Resource Relatedness, and the Dynamics of Corporate Diversification

Economies of Scope, Resource Relatedness, and the Dynamics of Corporate Diversification

Resource relatedness, redeployability, and firm value

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

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