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Real Option Management of Hydrocarbon Cracking Operations

Selvaprabu Nadarajah, University of Illinois at Chicago - College of Business Administration
Nicola Secomandi, Carnegie Mellon University - David A. Tepper School of Business
Gary Sowers, The Dow Chemical Company
John Wassick, The Dow Chemical Company

Perturbation Analysis for Investment Portfolios Under Partial Information with Expert Opinions

Andrew Papanicolaou, University of Sydney, School of Mathematics and Statistics


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SELVAPRABU NADARAJAH, University of Illinois at Chicago - College of Business Administration
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NICOLA SECOMANDI, Carnegie Mellon University - David A. Tepper School of Business
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GARY SOWERS, The Dow Chemical Company
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JOHN WASSICK, The Dow Chemical Company
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Commodity conversion assets play important economic roles. It is well known that the market value of these assets can be maximized by managing them as real options on the prices of their inputs and/or outputs. In particular, when futures on these inputs and outputs are traded, managing such real options, that is, valuing, hedging, and exercising them, is analogous to managing options on such futures, using risk neutral valuation and delta hedging methods. This statement holds because dynamically trading portfolios of these futures and a risk less bond can replicate the cash flows of these assets. This basic principle is not always appreciated by managers of commodity conversion assets. Moreover, determining the optimal operational cash flows of such an asset requires optimizing the asset operating policy. This issue complicates the real option management of commodity conversion assets. This chapter illustrates the application of this approach to manage a hydrocarbon cracker, a specific commodity conversion asset, using linear programming and Monte Carlo simulation. The discussion is based on a simplified representation of the operations of this asset. However, the material presented here has potential applicability to the real option management of more realistic models of hydrocarbon cracking assets, as well as other energy and commodity conversion assets.

"Perturbation Analysis for Investment Portfolios Under Partial Information with Expert Opinions" 

ANDREW PAPANICOLAOU, University of Sydney, School of Mathematics and Statistics
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In this paper we consider a Merton-type portfolio problem for a market with partial information and expert predictions on future returns. Partially-informed investment addressed by the Black-Litterman model, wherein investors' views on upcoming performance are incorporated into the optimization along with any degree of uncertainty that the investor may have in these views. Compared to the weights obtained from the standard Markowitz portfolio optimization (Markowitz 1952), the Black-Litterman weights are more intuitive and have greater resemblance to those used in real-life investment. In this paper we use perturbation theory to analyze a partial information HJB equation from which we find an intuitive interpretation of how stochasticity in expected returns affects optimal investment.

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